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\begin{document}

             \author[1]{Niranjan  A}

             \author[2]{Nitish  A}

             \author[3]{P Deepa  Shenoy}

             \affil[1]{  UVCE}

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\date{\small \em Received: 8 December 2015 Accepted: 3 January 2016 Published: 15 January 2016}

\maketitle


\begin{abstract}
        


Data mining techniques, while allowing the individuals to extract hidden knowledge on one hand, introduce a number of privacy threats on the other hand. In this paper, we study some of these issues along with a detailed discussion on the applications of various data mining techniques for providing security. An efficient classification technique when used properly, would allow an user to differentiate between a phishing website and a normal website, to classify the users as normal users and criminals based on their activities on Social networks (Crime Profiling) and to prevent users from executing malicious codes by labelling them as malicious. The most important applications of Data mining is the detection of intrusions, where different Data mining techniques can be applied to effectively detect an intrusion and report in real time so that necessary actions are taken to thwart the attempts of the intruder. Privacy Preservation, Outlier Detection, Anomaly Detection and PhishingWebsite Classification are discussed in this paper.

\end{abstract}


\keywords{anamoly detection; classification; intrusion detection system; outlier detection; privacy preserving data minig.}

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\let\tabcellsep& 	 	 		 
\section[{I. Introduction}]{I. Introduction}\par
he term Security from the context of computers is the ability, a system must possess to protect data or information and its resources with respect to confidentiality, integrity and authenticity \hyperref[b0]{[1]}. Confidentiality ensures that, a third party in no way would be able to read and understand the content while Integrity would not allow a third party to change or modify the content as a whole or even parts of it. Authenticity feature on the other hand would not allow a person to use, view or modify the content or the resource, if he is found to be unauthorised \hyperref[b1]{[2]}.\par
Those actions that compromise the availability, integrity or confidentiality of one or more resources of a computer could be termed as Intrusion. Preventing intrusions employing firewall and filtering router policies fail to stop these attacks. Inspite of all attempts to build secure systems, intrusions can still happen and hence they must be detected on their onset. An Intrusion detection system(IDS) \hyperref[b2]{[3]} by employing data mining techniques can discover consistent patterns of features of a system that are useful can detect anomalies and known intrusions using a relevant set of classifiers. Using some of the basic data mining techniques such as Classification and Clustering, Intrusion can be detected easily. Classification techniques are helpful in analyzing and labelling the test data into known type of classes, while Clustering techniques are used to group objects into a set of clusters, such that all similar objects become the members of the same cluster and all other objects become members of other clusters \hyperref[b3]{[4]}. Data mining, while allowing the extraction of hidden patterns or the underlying Figure 1: Privacy Preserving Data Mining Techniques knowledge from large volumes of data, might pose security challenges \hyperref[b4]{[5]}. Privacy Preserving Data Mining(PPDM)aims at safeguarding sensitive information from an un-solicited or unsanctioned disclosure \hyperref[b5]{[6]}. A number of PPDM approaches have been proposed so far. Some of them are listed as shown in Fig. 1, based on their enforcing privacy principle. 
\section[{T a) Suppression}]{T a) Suppression}\par
Any private or sensitive information pertaing to an individual such as name, age, salary, address and other information is suppressed before any computation takes place. Some of the techniques employed for this suppression are Rounding(Rs/-35462.33 may be rounded to 35,000), Generalization (Name Louis Philip may be replaced with the initials LP and Place Hamburg may be replaced with HMG and so forth). However when data mining requires full access to sensitive values, Suppression cannot be used. An alternate way of suppression is to limit the identity linkage of a record rather than suppressing thesensitive information present within a record. This technique is referred to as De-Identification. k-Anonymity is one such de-identification technique. It ensures that protection of the data released against Re-identification of the persons to which the data refer \hyperref[b6]{[7]} \hyperref[b7]{[8]}. Enforcing k-anonymity before all data are collected in one trusted place is difficult. A cryptographic solution based on Secret Sharing technique of Shamir could be used instead; this however incurs computation overhead. 
\section[{b) Randomization}]{b) Randomization}\par
Assuming the presence of a central server of a company that accepts information present with many customers and performs data mining techniques for building an Aggregate Model; Randomization allows the customers to introduce controlled noise or randomly perturb the records and to take away true information present in it. Introduction of noise can be achieved in several ways by addition or multiplication of the values generated randomly. Perturbation helps Randomization technique to achieve preservation of the required privacy.\par
The individual records are generated by the addition of such randomly generated noise to the original data. The noise thus added to individual records cannot be recovered, resulting in the desired privacy. Randomization techniques typically involve the following steps: 1. Only after randomizing their data, the Data Providers transmit this data to the Data Receiver. 
\section[{Data receiver computes the distribution by running a Distribution Reconstruction Algorithm. c) Data Aggregation}]{Data receiver computes the distribution by running a Distribution Reconstruction Algorithm. c) Data Aggregation}\par
Data Aggregation Techniques, in order to facilitate data analysis: combine data together from various sources. This might allow an attacker to deduce private and invidual-level data and to identify the party. When the extracted data allows the data miner to identify specific individuals, his privacy is considered to be under a serious threat. To prevent data from being identified, it may be anonymized immediately after the aggregation process. However, the Anonymized data sets can still contain enough information that could be used for the identification of individuals \hyperref[b8]{[9]}. 
\section[{d) Data Swapping}]{d) Data Swapping}\par
Data swapping process involves swapping of values across different records for the sake of privacypreservation. Without perturbing the lower order totals of the data, privacy of data can still be preserved allowing aggregate computations to be performed exactly as before. Since this technique does not follow randomization, it can be used in conjunction with other frameworks such as k-anonymity without violating the privacy definitions for that model. 
\section[{e) Noise Addition/Perturbation}]{e) Noise Addition/Perturbation}\par
Differential privacy through the addition of controlled noise provides a mechanism that maximizes the accuracy of queries while minimizing the chances of identification of its records \hyperref[b9]{[10]}. Some of the techniques used in this regard are: 1. Laplace Mechanism 2. Sequential Composition 
\section[{Parallel Composition}]{Parallel Composition}\par
The rest of this paper is structured as follows: Section-II covers a brief review of Classification and Detection of intrusions by employing various Data Mining Techniques, while Clustering techniques and their applications in Intrusion Detection are presented in Section-III. PPDM techniques and their necessity along with various types of PPDM are discussed in Section-IV. An overview of Intusion Detection System is discussed in Section-V. Phishing Website Classification using Data Mining Techniques are presented in Section-VI. Artificial Neural Networks(ANN) are presented in Section-VII. Section-VIII presents Anomaly Detection/Outlier Detection. Section-IX describes the various ways of Mitigating Code Injection Attacks. 
\section[{II. Classification and Detection Using Data mining Techniques}]{II. Classification and Detection Using Data mining Techniques}\par
Malware computer programs that replicate themselves in order to spread from one computer to another computer are called as worms. Malware includes worms, computer viruses, Trojan Horse, key loggers, adware, spyware Port scan worm, UDP worm, http worm, User to Root Worm and Remote to Local Worm and other malicious code \hyperref[b10]{[11]}. Attackers write these programs for various reasons varying from interruption of a computer process, gathering sensitive information, or gaining entry to private systems. Detecting a worm on the internet is very important, because it creates vulnerable points and reduces the performance of the system. Hence it is essential to detect the worm on the onset and classify it using data mining classification algorithms much before it causes any damage. Some of the classification algorithms that can be used are Random Forest, Decision Tree, Bayesian and others \hyperref[b11]{[12]}. A majority of worm detection techniques use Intrusion Detection System(IDS) as the underlying principle. Automatic detection is challenging because it is tough to predict what form the next worm will take. IDS can be classified into two types namely Network based IDS and Host based IDS. The Network based Intusion Detection System reflects network packets before they spread to an end-host, while the Host based Intusion Detection System reflects network packets that are already spread to the end-host. Moreover, the Host based detection studies encode network packets so 
\section[{Global Journal of Computer Science and Technology}]{Global Journal of Computer Science and Technology}\par
Volume XVI Issue V Version I 52 Year 2016 ( ) that the stroke of the internet worm may be struck. When we focus on the network packet without encoding, we must study the performances of traffic in the network. Several machine learning techniques have been used in the field of intrusion and worm detection systems. Thus, Data Mining and in particular Machine Learning Technique has an important role and is essential in worm detection systems. Using various Data Mining schemes several new techniques to build several Intrusion Detection models have been proposed. Decision Trees and Genetic Algorithms of Machine Learning can be emoloyed to learn anomalous and normal patterns from the training set and classifiers are then generated based on the test data to label them as Normal orAbnormal classes. The data that is labelled as Abnormal could be a pointer to the presence of an intrusion. 
\section[{a) Decision Trees}]{a) Decision Trees}\par
Quinlan's decision tree technique, is one of most popular machine learning techniques. The tree is constructed using a number of decision and leaf nodes following divide-andconquer technique \hyperref[b11]{[12]}. Each decision node tests a condition on one of the attributes of the input data and can essentially have a number of branches, to handle a separate outcome of the test. The result of decision may be represented as a leaf node. A training data set T is a set of n-classes \{C1, C2 ,..., Cn\}. T is treated as a leaf when it comprises of cases belonging to a single class. If T is empty with no cases, it is still treated a leaf and the major class of the parent node is given the related class. A test based on an attribute ai of the training data is performed when T consists of multiple classes, T is split into k subsets \{T1, T2, ..., Tk\}, where k gives the number of test outcomes. The process is recursed over each Tj, where 1 <= j<= n, until every subset belongs to a single class. Choosing the best attribute for each decision node while constructing the decision tree is very crucial. The C4.5-DT adopts Gain Ratio Criterion for the same. According to this criterion, an attribute that provides maximum information gain and that reduces the bias in favor of tests is chosen. The tree thus built can then be used to classify the test data, whose features are same as that of the training data. The test is carried out starting from the root node. Based on the outcome, one of the branches leading to a child is followed. As long as the child is not a leaf, the process is repeated recursively. The class and its corresponding leaf node is given to the test case being examined. 
\section[{b) Genetic Algorithms(GA)}]{b) Genetic Algorithms(GA)}\par
A machine learning approach of solving problems by employing biological evolution techniques are called Genetic Algorithms(GA). They can be effectively used to optimize a population of candidate solutions. GA makes use of data structures that are modelled on chromosomes and they are subjected to evolution using genetic operators namely: selection, crossover and mutation \hyperref[b12]{[13]}. Random generation of a population of chromosomes is performed in the beginning. The population thus formed comprises of all possible solutions of a problem and are considered the candidate solutions. Different positions of a chromosome called 'genes' are encoded as bits, characters or numbers. A function called Fitness Function evaluates the goodness of each chromosome based on the desired solution. Crossover operator simulates natural reproduction while Mutation operator simulates mutation of the species. The Selection operator chooses the fittest chromosomes \hyperref[b13]{[14]}.  {\ref Fig 2.} depicts the operations of Genetic Algorithms. Before using GA for solving various problems, following three factors have to be considered 1. Fitness function 2. Individuals representation and 3. Parameters of GA Figure  {\ref 2}: Flowchart for a GA GA based approach can be incorporated for designing Artificial Immune Systems. Using this approach, Bin et al., \hyperref[b14]{[15]} have proposed a method for smartphone malware detection where static and dynamic signatures of malwares are extracted and malicious scores of tested samples are obtained. 
\section[{c) Random Forest}]{c) Random Forest}\par
A classification algorithm that is made up of a collection of tree structured classifiers, and that chooses the winner class based on the votes casted by the individual trees present in the forest is called the Random Forest Algorithm. Each tree is constructed by picking up random data from a training dataset. The chosen dataset may be split up into training and testsets. The major chunk of the dataset goes into the training set while the minor chunk forms the test set. The tree construction involves the following steps:  
\section[{d) Association Rule Mining (ARM)}]{d) Association Rule Mining (ARM)}\par
Association-rule mining discovers interesting relations between a set of attributes in datasets \hyperref[b15]{[16]}. The datasets and their inter-relationship can be represented as association rules. This information can be used for making strategic decisions about different activities such as, promotional pricing, shelf management and so on \hyperref[b16]{[17]}. Traditional Association rule mining involves a data analyst being given datasets of different companies for the purpose of discovering patterns or asociation rules that exist between the datsets \hyperref[b17]{[18]}. Although, we can achieve sophisticated analysis on these extremely large datasets in a cost-effective manner \hyperref[b18]{[19]}, it poses security risk \hyperref[b19]{[20]} for the data owner whose sensitive information can be deduced by the dataminer \hyperref[b20]{[21]}. Even today, association rule mining is one of the widely used pattern discovery methods in KDD.\par
Solving an ARM problem basically involves traversing the items in a database, which can be done using various algorithms based on the requirement \hyperref[b21]{[22]}. ARM algorithms are primarily categorised into BFS (Breadth First Search) and DFS (Depth First Search) methods based on the strategy used to traverse the search space \hyperref[b22]{[23]}. The BFS and DFS methods are further classified into Counting and Intersecting, based on how the support values for the itemsets are determined. The algorithms Apriori, Apriori-TID and Apriori-DIC are based on BFS with Counting strategies, while the Partition algorithm is based on BFS with Intersecting strategies. The FP-Growth algorithm on the otherhand, is based on DFS with Counting strategies while ECLAT is based on DFS with Intersecting \hyperref[b23]{[24]} \hyperref[b24]{[25]}. These algorithms can be optimized specifically for improving the speedup \hyperref[b25]{[26]} \hyperref[b26]{[27]}. BFS with Counting Occurences: The common algorithm in this category is the Apriori algorithm. It utilizes the downward closure property of an itemset, by pruning the candidates with infrequent subsets before counting their supports.The two metrics to be considered while evaluating the association rules are: support and confidence. BFS offers the desired optimization by knowing the support values of all subsets of the candidates in advance. The limitation of this approach is increased computational complexity in rule extraction from a large database. Fast Distributed Mining(FDM) algorithm is a modified, distributed and unsecured version of the Apriori algorithm \hyperref[b27]{[28]}. The advancements in data mining techniques, have enabled organizations in using data more efficiently.\par
In Apriori, the candidates of a cardinality k are counted by a single scan of the entire database. Looking up for the candidates in each transaction forms the most crucial part of the Apriori Algorithm. For this purpose, a hashtree structure is used  {\ref [29]}. Apriori-TID an extension of Apriori, represents each transaction based on the current candidates it contains, unlike normal Apriori that relies on raw database. Apriori-Hybrid combines the benefits of both Apriori and Apriori-TID. Apriori-DIC another variation of Apriori, tries to soften the separation that exists between the processes, counting and candidate generation. This is done by using a prefix-tree. BFS with Intersections: A Partition Algorithm is similar to the Apriori algorithm that uses intersections rather than counting occurences for the determination of support values. The partitioning of itemsets could result in the exponential growth of intermediate results beyond the physical memory limitations. This problem can be overcome, by splitting the database up into a number of chunks that are smaller in size and each chunk is treated independently. The size of a chunk is determined such that all intermediate lists can fit into memory. An additional scan can optionally be performed to ensure that the itemsets are not only locally frequent but also are globally frequent. DFS with Counting Occurences: In Counting, a database scan for each reasonable sized candidate set is performed. Because of the involvement of computational overhead in database scanning, the simple combination of DFS and Counting Occurences is practically irrelevant. FP-Growth on the otherhand uses a highly compressed representation of transaction data called FP-Tree. An FP-Tree is generated by counting occurences and performing DFS. DFS with Intersections: The algorithm ECLAT combines DFS with the list intersections to select agreeable values. It makes use of an optimization technique called Fast Intersections. It does not involve the process of splitting up of the database since complete path of classes beginning from the root would be maintained in the memory. As this method eliminates most of the computational overhead the process of mining association rules becomes faster. 
\section[{III. Clustering}]{III. Clustering}\par
Clustering is one of the widely used discovery methods in data mining. It allows to group a set of data in such a way that, Intra-Cluster similarity are maximized while minimizing the Inter-Cluster similarity are minimized. Clustering involves unsupervised learning of a number of classes that are not known in advance. The clustering algorithms can be broadly clasified into the following types and are listed in Fig.  Unweighted Pair Group Method with Arithmetic Mean (UPGMA), or Average Linkage Clustering. Selecting appropriate clusters from the available hierarchy of clusters, could be achieved either using Agglomerative or Divisive Clustering.In Agglomerative Clustering, we begin with single objects and conglomerate them into clusters while in Divisive clustering, we start with the complete data set and isolate it into segments. 
\section[{b) Centroid Based Clustering}]{b) Centroid Based Clustering}\par
Centroid-based clustering may have clusters that are represented by a vector, which necessarily is not a member of the data set or may have clusters strictly restricted to the members of the dataset. In kmeans Clustering algorithm, the number of clusters is limited to size k, it is required to determine k cluster centers and assigning objects to their nearest centers.\par
The algorithm is run multiple times with different k random initializations to choose the best of multiple runs \hyperref[b28]{[30]}. In kmedoid clustering, the clusters are strictly restricted to the members of the dataset while in kmedians clustering, only the medians are chosen to form a cluster. The main disadvantage of these techniques is that the number of clusters k is selected beforehand. Furthermore, they result in incorrectly cut borders in between the clusters. 
\section[{c) Distribution Based Clustering}]{c) Distribution Based Clustering}\par
Distribution-based clustering technique forms clusters by choosing objects that belong more likely to the same distribution. One of the most commonly preferred distribution techniques is the Gaussian Distribution. It suffers from the overfitting problem where a model cannot fit into set of training data. 
\section[{d) Density Based Clustering}]{d) Density Based Clustering}\par
In this type of clustering, an area that is having higher density than the rest of the data set is considered as a cluster. Objects in the sparse areas are considered to be noise and border points. There are three commonly used Density-based Clustering techniques namely: DBSCAN, OPTICS and Mean-Shift. DBSCAN is based on connecting points that satisfy a density criterion within certain distance thresholds. The cluster thus formed may consist of all density-connected objects and objects that are within these objects range free to have an arbitrary shape. 
\section[{e) Recent Clustering Techniques}]{e) Recent Clustering Techniques}\par
All the standard clustering techniques fail for highdimensional data and so some of the new techniques are being explored. These techniques fall into two categories namely: Subspace Clustering and Correlation Clustering. In Subspace Clustering, the clustering model specifies a small list of attributes that should be considered for the formation of a cluster while in Correlaton Clustering,the model along with this list of attributes it also provides the correlation between the chosen attributes.\par
55 Year 2016 
\section[{( ) C f) Other Techniques}]{( ) C f) Other Techniques}\par
One of the most basic clustering techniques is the BSAS(Basic Sequential Algorithmic Scheme). Given the distance d(p, C) between a vector point p and a cluster C, the maximum number of clusters allowed q and threshold of dissimilarity 0, the BSAS constructs the clusters even when the number of clusters to be formed is not known in advance.\par
Every newly presented vector is either assigned to an already existing cluster or a new cluster is created, depending on the distance to the already present clusters. 
\section[{g) Clustering applications in IDS}]{g) Clustering applications in IDS}\par
Clustering technique may be effectively used in the process of Intrusion Detection. The setup is depicted in Fig.  {\ref 4}. Alerts generated by multiple IDSs belonging to both Network and Host types are logged into a centralized database. The alert messages arriving from diffrent IDSs will be in different formats. Before passing them into the server, a preprocessing step is needed to bring them all into some uniform format \hyperref[b29]{[31]}.\par
Best effort values are chosen for the missing attributes during the preprocessing stage. The timestamp information may have to be converted into seconds for the sake of comparison. Different IDSs may use different conventions for naming a single event and hence it is required to standardize 
\section[{Global Journal of Computer Science and Technology}]{Global Journal of Computer Science and Technology}\par
Volume XVI Issue V Version I the messages. Each alert may be added with an unique ID to keep track of the alerts. After preprocessing and normalizing alerts they are passed to the first phase to perform filtering and labeling functions. To minimise the number of Alerts, it is a good idea to employ Alert Fusion during which alerts with same attributes that differ by a small amount of time are fused together. Alert Fusion makes the generalization process fast. Generalization involves the addition of hierarchical background knowledge into each attribute. On every iteration of this process, the selected attribute is generalized to the next higher level of hierarchy and those alerts which have become similar by now are grouped together. 
\section[{IV. Privacy Preserving Data Mining (ppdm)}]{IV. Privacy Preserving Data Mining (ppdm)}\par
Privacy Preserving Data Mining techniques aim at the extraction of relevant knowledge from large volumes of data while protecting any sensitive information present in it. It ensures the protection of sensitive data to conserve privacy and still allowing us to perform all data mining operations efficiently. The two types of privacy concerned data mining techniques are: 1. Data privacy 2. Information privacy Data privacy focuses on the modification of the database for the protection of sensitive data of the individuals while Information privacy focuses on the modification for the protection of sensitive knowledge that can be deduced from the database.\par
Alternatively we can say that Data privacy is corcerned about providing privacy to the input while Information privacy on the otherhand is about providing privacy to the output. Preserving personal information from revealation is the main focus of a PPDM algorithm \hyperref[b30]{[32]}. The PPDM algorithms rely on analysing the mining algorithms for any side effects that are acquired during Data privacy. The objective of Privacy Preserving Data Mining is building algorithms that transform the original data in some mannner, so that both the private data and knowledge are not revealed even after a successful mining process. Only when some relevant adequate benefit is found resulting from the access, the privacy laws would allow the access.\par
Multiple parties may sometimes wish to share private data resulting after a successful aggregation \hyperref[b31]{[33]} without disclosing any sensitive information from their end \hyperref[b32]{[34]}. Consider for example, different Book stores with respective sales data that is in a way considered to be highly sensitive, may wish to exchange partial information among themselves to arrive at the aggregate trends without disclosing their individual store trends. This requires the use of secure protocols for sharing the information across multiple parties. Privacy in such cases should be achieved with high levels of accuracy \hyperref[b33]{[35]}.\par
57 Year 2016 
\section[{( ) C}]{( ) C}\par
The data mining technology by principle is neutral in terms of privacy \hyperref[b34]{[36]}. The motive for which a data mining algorithm is used could either be good or malicious \hyperref[b35]{[37]}. Data mining has expanded the investigation possibilities \hyperref[b36]{[38]} to enable researchers to exploit immense datasets on one hand \hyperref[b37]{[39]}, while the malicious use of these techniques on the other hand has introduced threats of serious nature against protection of privacy \hyperref[b38]{[40]}.\par
Discovering the base of privacy preserving data mining algorithms and connected privacy techniues is the need of the hour \hyperref[b39]{[41]}. We are required to answer few questions in this regard such as 1. Evaluation of these algorithms with respect to one another 2. Should privacy preserving techniques be applied to each of the data mining algorithms? Or for all applications? 3. Expanding the places of usage of these techniques. 
\section[{Investigating their use in the fields of Defense and}]{Investigating their use in the fields of Defense and}\par
Intelligence, Inspection and Geo-Spatial applications. 
\section[{The techniques of combining confidentiality, privacy}]{The techniques of combining confidentiality, privacy}\par
and trust with high opinion to data mining.\par
To answer these questions, research progresses in both data mining and privacy are required. Proper planning towards developing flexible systems is essential \hyperref[b40]{[42]}. Few applications may demand pure data mining techniques while few others may demand privacy-preserving data mining \hyperref[b41]{[43]}. Hence we require flexible techniques in data mining that can cater to the the changing needs \hyperref[b42]{[44]}. The research progress made so far in the area of PPDM is listed in Table \hyperref[tab_1]{1}. 
\section[{Distributed Privacy Preserving Data Mining(DPPDM):}]{Distributed Privacy Preserving Data Mining(DPPDM):}\par
The tremendous growth of internet in the recent times is creating new opportunities for distributed data mining \hyperref[b50]{[52]}, in which, mining operations performed jointly using their private inputs \hyperref[b51]{[53]}. Often occurence of mining operations between untrusted parties or competitors, result in privacy leakage \hyperref[b52]{[54]}. Thus, Distributed Privacy Preserving Data Mining(DPPDM) \hyperref[b9]{[10]}[55] algorithms require a high level of collaboration between parties to deduce the results or to share mining results that are not sensitive. This could sometimes result in the disclosure of sensitive information.\par
Distributed data mining are classified as Horizontally Partitioned Data and Vertically Partitioned Data. In a Horizontally partitioned data framework, each site maintains complete information on an unique set of entities, and the integrated dataset consists of the union of all of these datasets. Vertically Partitioned Data framework on the otherhand involves each site, maintaining different types of information and each dataset and has only limited information about same set of entities.\par
Privacy feature can limit the information leakage caused by the distributed computation techniques \hyperref[b54]{[56]}.\par
Each non-trusting party can compute its own functions for unique set of inputs, revealing only the defined outputs of the functions. Apart from hiding sensitive information, the privacy service also controls the information and its uses by involving various number of negotiations and tradeoffs between hiding and sharing.\par
All efficient PPDM algorithms are based on the assumption that it is acceptable to release the intermediate results obtained during the data mining operations. Encryption techniques solve the data privacy problem and their use would make it easy to perform data mining tasks among mutual untrustworthy parties, or between competitors. Due to its privacy concern, Distributed Data Mining Algorithms employ encryption techniques.\par
Encryption is used in both approaches(horizontally and vertically partitioned data) of Distributed Data mining without much stress on the effiency of encryption technique used.\par
If the data are stored on different machines and partitioning is done row-wise, it is called horizontal partitioning and if the data are stored and partitioned column wise then it is called vertical partitioning. An overview of the same is depicted in Fig. 5.\par
The objective of data mining techniques is to generate high level rules or summaries and generalize across populations, rather than revealing information about individuals but they work by evaluating individual data that is subject to privacy concerns. Since much of 
\section[{Global Journal of Computer Science and Technology}]{Global Journal of Computer Science and Technology}\par
Volume XVI Issue V Version I  
\section[{C}]{C}\par
this information held by various organizations has already been collected, providing privacy is a big chalenge. To prevent any correlation of this information, control and individual safeguards must be separated to be able to provide acceptable privacy. Unfortunately, this separation makes it difficult to use the information for the identification of criminal activities and other purposes that would benefit the society. Proposals to share information across agencies to combat terrorism and other criminal activities, would also remove the safeguards imposed by separation.\par
Many of the complex socio-technical systems suffer from an inadequate risk model that focuses on the use of Fair Information Practice Principles(FIPPs). Anonymization suffers from the risk of failure, since the circumstances surrounding its selection are ignored. A Hybrid approach that combines privacy risk model with an integrated anonymization framework involving anonymization as the primary privacy risk control measure can be considered instead \hyperref[b55]{[57]}. Public-Key Program Obfuscation: The process of making a program uncomprehensible without altering its functionality is called Program Obfuscation. A program that is obfuscated should be a virtual black box meaning, if it is possible for one to compute something from it, it should also be possible to compute the same even from the input-output behavior of the program. Secure Multi-party Computation: Distributed computing involves a number of distinct,and connected computing devices that wish to carry out a combined computation of some function. For example, servers holding a distributed database system, may wish to update their database. The objective of secure multiparty computation is to allow parties to carry out distributed computing tasks in a secure way \hyperref[b31]{[33]}. It typically involves the parties carrying out a computation based on their private inputs and neither of them willing to disclose its own input to other parties. The problem is conducting such a computation by preserving the privacy of their inputs. This problem is called the Secure Multi-party Computation problem (SMC) \hyperref[b32]{[34]}. Consider the problem of two-parties who wish to securely compute the median. The two parties have with them two separate input sets X and Y. The parties are required to jointly compute the median of the union of their sets X U Y, without revealing anything about each other's set. Association Rules can be computed in an environment where different information holders have different types of information about a common set of entities. 
\section[{V. Intrusion Detection System(ids)}]{V. Intrusion Detection System(ids)}\par
Intrusion detection systems aim at the detection of an intrusion on its onset \hyperref[b56]{[58]}. A high level of human expertise and significant amount of time are required for the development of a comprehensive IDS \hyperref[b57]{[59]}. However, IDSs that are based on the Data Mining techniques require less expertize and yet they perform better. An Intrusion Detection System detects network attacks against services that are vulnerable \hyperref[b58]{[60]}, attacks that are data driven on applications, privilege escalation \hyperref[b59]{[61]}, logins that are un-authorized and access to files that are 59 Year 2016 
\section[{( ) C}]{( ) C}\par
sensitive in nature \hyperref[b60]{[62]}. The data mining process also efficiently detects malware from the code \hyperref[b61]{[63]}, which can be used as a tool for cyber security \hyperref[b62]{[64]} \hyperref[b63]{[65]}. An overview of an Intrusion Detection System is presented in Fig 6.\par
An IDS is basically composed of several components such as, sensors, a console monitor and a central engine \hyperref[b64]{[66]}. Sensors generate security events while all events and alerts are monitored and controlled by the Console Monitor and the Central Engine records events in a database and generate alerts based on a set of rules \hyperref[b65]{[67]}. An Intrusion detection system \hyperref[b66]{[68]} can be classified depending on the location and the type of Sensors and based on the technique used by the Central engine for the generation of alerts. A majority of IDS implementations, involve all of the three components integrated into a single device.\par
Current virus scanner methodology makes use of two parts namely a Detector based on signatures and a Classifier based on the heuristic rules for the detection of new viruses. The signature-based detection algorithms rely on signatures that are unique strings of known malicious executables for the generation of detection models.The disadvantages of this approach are: it is more time-consuming and fails in detecting new malicious executables. Heuristic classifiers on the other hand are generated by a set of virus experts for the detection of new malicious executables.  
\section[{i. Network Based IDS}]{i. Network Based IDS}\par
Because of their increasingly vital roles in modern societies, computer networks have been targeted by enemies and criminals. For the protection of our systems, it is very essential to find the best possible solutions. Intrusion prevention techniques such as, authentication technqiues involving passwords or biometrics \hyperref[b67]{[69]}, programming errors avoidance, and protection of information using encryption techniques have been widely used as a first line of defense. Intrusion prevention techniques as the sole defense mechanism are not sufficient enough to combat attacks. Hence, it can therefore be used only as a second line of defense for the protection of computer systems \hyperref[b68]{[70]}.\par
An Intrusion Detection system must protect resources such as accounts of users \hyperref[b69]{[71]}, their file systems and the system kernels of a target system and must be able enough to characterize the legitimate or normal behavior of these resources by involving techniques that compare the ongoing system activities with already established models and to identify those activities that are intrusive \hyperref[b70]{[72]} \hyperref[b71]{[73]}. Network packets are the data source for Network-Based Intrusion Detection Systems. The NIDS makes use of a network adapter to listen to and analyse network traffic as the packets travel across the network. A Network based IDS generates alerts upon detecting an intrusion from outside the perimeter of its enterprise \hyperref[b72]{[74]}. The network based IDSs are categorically placed at strategic points on LAN to observe both inbound and outbound packet \hyperref[b73]{[75]}. Network based IDSs are placed next to the firewalls to alert about the inbound packets that may bypass the firewall \hyperref[b74]{[76]}. Few Network-Based IDSs take custom signatures from the user security policy as input, permitting limited detection of security policy violations \hyperref[b75]{[77]}. When packets that contain intrusion originated from authorized users, the IDS may not be able to detect \hyperref[b76]{[78]} In a Host-based IDS, the monitoring sensors are placed on network resources nodes so as to monitor logs that are generated by the Host Operating System or application programs.\par
These Audit logs contain records of events or activities that are occuring at individual Network resources \hyperref[b79]{[81]}. Since a Host-Based IDS is capable of detecting attacks that cannot be seen by a Network based IDS, an attacker can misuse one of trusted insiders \hyperref[b80]{[82]}. A Host based system utilizes Signature Rule Base that is derived from security policy that is specific to a site. A Host Based IDS can overcome all the problems associated with a Network based IDS as it can alarm the security personnel with the location details of intrusion, he can take immediate action to thwart the intrusion. A Host based IDS can also monitor any unsuccessful attempts of an attacker. It can also maintain seperate records of user login and user logoff actions for the generation of audit records. 
\section[{Advantages}]{Advantages}\par
Some of the advantages of a Host Based IDS are as follows: 1. Can detect attacks that are not detected by a Network Based IDS. 2. Operates on Operating System audit log trails, for the detection of attacks involving software integrity breaches. 
\section[{Disadvantages}]{Disadvantages}\par
The disadvantages are: 1. Certain types of DoS(Denial of Service)attacks can disable them \hyperref[b81]{[83]}. 2. Not suited for detecting attacks that target the network. 3. Difficult to configure and manage every individual system. iii. Hybrid IDS Since Network and Host-based IDSs have strengths and benefits that are unique over one another, it is a good idea to combine both of these strategies into the next generation IDSs \hyperref[b82]{[84]}. Such a combination is often referred to as a Hybrid IDS. Addition of these two components would greatly enhance resistance to few more attacks. a. DM techniques for IDS Some of the techniques and applications of data mining required for IDS include the following 1. Pattern Matching 2. Classification and 3. Feature Selection Pattern Matching: Pattern Matching is a process of finding a particular sequence of a part of data (substring or a binary pattern), in the whole data or a packet to get a desired information \hyperref[b85]{[87]}. Though it is fairly rigid, it is indeed simple to use. A Network Based IDS succeeds in detecting an intrusion only when the packet in question is associated with a particular service or, destined to or from a particular port. That is, only few fields of the packet such as Service, Source/Destination port address and few others have to be examined thereby reducing the amount of inspection to be done on each packet.\par
However, it makes it difficult for systems to deal with Trojans and their associated traffic that can be moved at will. The pattern matching can be classified into two categories based on the frequency of occurrence namely: a) Frequent Pattern Matching and b) Outlier Pattern Matching a) Frequent Pattern Matching\par
These are the type of patterns which occur frequently in an audit data, i.e., the frequency of occurrence of these patterns is more compared to other patterns in the same data \hyperref[b80]{[82]}. 
\section[{Global Journal of Computer Science and Technology}]{Global Journal of Computer Science and Technology}\par
Volume XVI Issue V Version I 60 Year 2016 ( ) Determining frequent patterns in a big data helps in analyzing and forecasting of a particular characteristic of the data. For example, by analyzing the sales information of an organization, frequent pattern matching might help to predict the possible sales outcome for the future. It also helps in decision making. The frequent pattern mining in ADAM project data is done by mining the repository for attack-free (train) data which is compared with the patterns of normal profile (train) data. A classifier is used to reduce the false positives. 
\section[{b) Outlier Pattern Matching}]{b) Outlier Pattern Matching}\par
Patterns that are unusual and are different from the remaining patterns and that are not noise are referred to as Outlier Patterns. Preprocessing phase eliminates noise as it is not a part of the actual data while outliers on the other hand cannot be eliminated. Outliers exhibit deviating characteristics as compared to the majority of other instances. Outliers patterns are not usual and they occur less frequently and for this reason will have minimal support in the data. These patterns can quite often point out some sort of discrepancy in data such as transactions that are fraudulent, intrusion, abnormal behavior, economy recession etc.,. The outlier pattern mining algorithms can be of two types, one that looks for patterns only at fixed time intervals, and the other that calculates monitors patterns at all times. Outlier pappers make use of special data structures such as Suffix Tree and other String Matching Algorithms. Classification: Classification makes use of training examples for learning a model and to classify samples of data into known classes \hyperref[b86]{[88]}. A wide range of classification techniques ranging from Neural Networks, Decision Trees, Bayesian classifier \hyperref[b87]{[89]}, Bayesian Belief Networks and others are used in applications that involve Data Mining techniques. Classification typically involves steps that are outlined below:  
\section[{Feature Selection}]{Feature Selection}\par
Better classification Can consider NSL-KDD instances instead of their probabilities. Spam Mail classification and Text classification applications extensively use Naive Bayesian classifiers for they are less error prone. However, their disadvantage is that they require probabilities in advance. The probability information that is required by them is extremely huge which consist number of classes, their attributes and the maximum cardinality of attributes. The space and computational complexity of these classifiers increase exponentially. 
\section[{Support Vector Machine(SVM):}]{Support Vector Machine(SVM):}\par
Support Vector Machine is one of the learning methods extensively used for the Classification and Regression analysis of Linear and Non-linear data \hyperref[b88]{[90]}. It maps input feature vectors into a higher dimensional space using non-linear mapping techniques. In SVM, the classifier is created by the linear separation of hyperpalnes and linear separation is achieved using a function called kernel.The Kernel transforms a linear problem by mapping it into feature spaces. Some of the commonly used kernel functions are Radial basis, sigmoid neural nets and polynomials. Users specify one of these functions while training the classifier and it selects support vectors along the surface of this function. The SVM implementation tries to achieve maximum separation between the classes \hyperref[b89]{[91]}. Intrusion detection system involves two phases namely training and testing. SVMs are capable of learning a larger set of patterns and can provide better classification, because the categorizing complexity is independent of the feature space dimensionality \hyperref[b90]{[92]}. SVMs can update the training patterns dynamically with the availability of new pattern during classification. For the efficient classification it is required to reduce the dimensionality of the dataset. To do this we have Feature Selection.\par
iii. Feature Selection(FS)\par
The process of reducing the dataset dimensionality by selecting a subset of the features from the given set of features is called Feature Selection \hyperref[b91]{[93]}. FS involves discarding of redundant and irrelevant features. FS is considered to be an efficient machine learning technique that helps in building classification systems which are efficient. With the reduction in subset dimensionality, the time complexity is reduced with improved accuracy, of a classifier. Information Gain is a proposition of feature selection that can be used to compute entropy cost of each attribute. An entropy cost can be called as a rank. Rank of each feature represents its importance or association with an solution class that is used to recognize the data. So a feature with comparatively higher rank will be one of the most important features for classification. The three standard approaches that are commonly followed for feature selection are embedded technique, filter technique, and wrapper technique. 
\section[{VI. Phishing Websites Classification}]{VI. Phishing Websites Classification}\par
In the art of emulating a website of a trusted and creditable firm with the intention of grabbing users' private information (ussername, password) is called phishing. Fake websites are ususlly created by dishonest people to masquerade honest websites. Users unknowingly lose money due to phishing activities of attackers. Online trading therefore demands protection from these attacks and is considered a critical step. The prediction and classification accuracy of a website depends on the goodness of the extracted features. Most of the internetusers feel safe against phishing attacks by utilizing antiphishing tool, and hence the anti-phishing tools are required to be accurate in predicting phishing \hyperref[b92]{[94]}. Phishing websites give us a set of clues within its content parts and through security indicators of the browsers \hyperref[b93]{[95]}. A variety of solutions have been proposed to tackle the problem of phishing. Data mining techniques involving Rule based classification \hyperref[b94]{[96]} serve as promising methods in the prediction of phishing attacks.\par
Phishing attack typically starts by, attacker sending an email to victims requesting personal information to be disclosed, by visiting a particular URL \hyperref[b95]{[97]}. Phishers use a set of mutual features to create phishing websites to carry out proper deception \hyperref[b96]{[98]}. We can exploit this information to successfully distinguish between phishy and non-phishy websites based on the extracted features of the website visited \hyperref[b92]{[94]}. The two approaches that are commonly used in the identification of phishing sites are: black-list based, which involves comparison of the requested URL with those that are present in that list and Heuristic based method that involves the collection of certain features from the website to label it either as phishy or legitimate \hyperref[b97]{[99]}. The disadvantage of Black-list based approach is that the black-list can not contain all phishing websites since, a new malicious website is launched every second \hyperref[b98]{[100]}. In contrast, a Heuristic-based approach can recognize fraudulent websites that are new \hyperref[b99]{[101]}. The success of Heuristic-based methods depend on the selection of features and the way they are processed. Data mining can be effectively used here to find patterns as well as relations among them \hyperref[b100]{[102]}. Data mining is considered to be important for taking decisions, since decisions are made based on the patterns and rules derived using the data mining algorithms \hyperref[b101]{[103]}.\par
Although there is substantial progress made in the development of prevention techniques, phishing still remains a threat since most of the counter measures techniques in use are based still on reactive URL blacklisting \hyperref[b102]{[104]}. Since Phishing Web sites will have shorter life time these methods are considered to be inefficient. Newer approaches such as Associative Classification (AC) are more suitable for these kinds of applications. Associative Classification technique is a new technique derived by combining Association rule and Classification techniques of data mining \hyperref[b103]{[105]}. AC typically includes two phases; the training phase to induce hidden knowledge (rules) using Association rule and the Classification phase to construct a Classifier after pruning useless and redundant rules. Many research studies have revealed that AC usually shows better classifiers with reference to error rate than other standard classification approaches such as decision tree and rule induction. 
\section[{VII. Artificial Neural Networks(ann)}]{VII. Artificial Neural Networks(ann)}\par
An Artificial Neural Network is basically a connected set of processing units. Each connection has a specific weight that determines how one unit affects the other. Few of these units act as input nodes and few other as output nodes and remaining nodes consists of hidden layer. Neural network performs functionally, a mapping from input values to output values by activating each input node and allowing it to spread through the hidden layer nodes to the output nodes. The mapping is stored in terms of weight over connection. Fig.  {\ref 7} shows the structure of HHNN \hyperref[b60]{[62]}.\par
ANN is one of the widely used techniques in the field of intrusion detection.  {\ref ANN}  Feature selection is independent of the classifier used in case of Filter method, while in Wrapper method features are chosen specifically to the intended classifier. Filter method uses an arbitrary statistical way for the selection of features whereas wrapper method uses a learning algorithm to find the best subset of features. Wrapper approach is more expensive and requires more computational time than the filter approach but gives more accurate results compared to filter technique.   {\ref (HHNN)}. Anomaly detection assumes that the intrusions always return as a number of deviations from the normal patterns. HHNN technique studies the relationship between the two sets of information, and generalizes it in getting new inputoutput pairs reasonably. Neural networks can be used hypothetically for the identification of attacks and look for these attacks in the audit stream. Since there is no reliable method at present to realize causes of association, it cannot clarify the reason behind the classification of the attack. The research progress made in HHNN is summarized in Table \hyperref[tab_6]{3}. 
\section[{VIII. Anomaly Detection/Outlier Detection}]{VIII. Anomaly Detection/Outlier Detection}\par
Anomaly detection is a process that involves finding nonconforming patterns to the expected behavior. Such patterns are called anomalies. Different application domains term them differently as outliers or aberration or surprises or peculiarities or It is ineffective against new types of attacks which makes it susceptible to evasion methods.\par
Anomaly Based IDS on the other hand, records normal behavior and classifies the deviations from normal behavior as anomalies. It is considered to be robust and reliable to unknown attacks and prevent attacks from malicious users who improvise their attacking strategy. The widely used implementation of Anomaly Based IDS is by the extensive use of data through the same modules: Feature Extractor and Feature Selector, that is finally evaluated by the already trained Classifier. When the sample is found to be deviating from normal profiles, an alarm is raised. The profiles are required to be updated at regular intervals of time and Classifier training is also carried out periodically, so as to minimize the false alarm rate. For Feature selection, we can either employ the Ranking methods or the Filter methods. The Ranking methods output the feature set sorted in descending order according to a particular evaluation measure. The top variables in the feature set are considered to be the most discriminant features. It is therefore essential to determine a threshold to discard features that are considered to have little or no contribution to the classification process. Information Gain(IG) is one of the commonly used evaluation measures.\par
A variant of IG, with improvisation is the Gain Ratio (GR).\par
The GR overcomes the bias found in IG towards features resulting in a smaller set of features. For the purpose of Feature Selection we can employ a ranking method that is unsupervised called Principal components analysis(PCA).\par
The advantage of Filter methods for Feature Selection is that they automatically choose a set of selected features based on a particular evaluation measure. One of the widely employed Filtering methods for Feature Selection is the Best First Search(BFS). It makes use of Forward Selection and Backward Elimination to search through the feature space adopting a Greedy approach. When performance is found to be dropping, it backtracks to the previous feature subsets that have better performance and start all over again from there. BFS is computationally expensive for larger sets. Genetic Algorithms \hyperref[b107]{[109]} is another type of Filtering technique that is considered to be very effective in practice \hyperref[b108]{[110]}. 
\section[{IX. Mitigating Code Injection Attacks}]{IX. Mitigating Code Injection Attacks}\par
A code injection attack typically involves writing of new machine code into the vulnerable programs memory \hyperref[b109]{[111]}, and after exploiting a bug in the program the control is redirected to the new code \hyperref[b110]{[112]}. The protection technique \hyperref[b111]{[113]}, W+X mitigates this attack by allowing only either a Write or Execute operations on memory but never allows both \hyperref[b112]{[114]}.\par
The research progress made so far in this regard is summarized in Table \hyperref[tab_7]{4}. 
\section[{a) Types of Code Injection}]{a) Types of Code Injection}\par
Some of the flavours of Code Injection attacks are: SQL Injection \hyperref[b118]{[121]}, HTML Script Injection \hyperref[b119]{[122]}, Object Injection \hyperref[b120]{[123]}, Remote File Injection \hyperref[b121]{[124]} and Code Reuse Attacks(CRAs) \hyperref[b122]{[125]}. 
\section[{i. SQL Injecton}]{i. SQL Injecton}\par
A technique that uses SQL syntax to input commands that can alter read or modify a database is called SQL Injection. Consider for example a web page having a field on it to allow users to enter a password for authentication. The code behind the page usually a script code, will generate a SQL query to verify the matching password entered against the list of user names:\par
SELECT UsrList.Username FROM UsrList WHERE UsrList. Password = 'Password'\par
The access is granted when the password entered by the user matches the password specified in the query. If the malicious user can inject some valid code ('password' OR '1'='1') in the Password field. An attacker by leaving the password field empty makes the condition "'1'='1"' to become true and gains access to the database. 
\section[{ii. HTML Script Injection}]{ii. HTML Script Injection}\par
An attacker injects malicious code by making use of the <script>and </script>tags, within which he would change the location property of the document by setting it to an injected script.\par
iii. Object Injection PHP allows serialization and deserialization of objects. If an untrustworthy input is allowed into the deserialization function, it is possible to modify existing classes in the program and execute malicious attacks. 
\section[{iv. Remote File Injection}]{iv. Remote File Injection}\par
Attackers might provide a Remote Infected file name as the path by modifying the path command of the script file to cause the intended destruction \hyperref[b123]{[126]}.  Attacks in which an attacker directs control flow through an already existing code with an erroneous result are called Code Reuse Attacks \hyperref[b124]{[127]}.\par
Attackers therfore have come out with codereuse attacks \hyperref[b125]{[128]}, in which a defect in the software is exploited to create a control flow through existing codebase to a malicious end \hyperref[b126]{[129]}. The Return Into Lib C(RILC)is a type of code-reuse attack \hyperref[b127]{[130]} where the stack is compromised and the control is transferred to the beginning of an existing library function such as mprotect() to create a memory region \hyperref[b128]{[131]}that allows both write and execution operations on it to bypass W+X \hyperref[b129]{[132]}. Such attacks can be effiently overcome using Data Mining techniques \hyperref[b130]{[133]}. The source code is checked to find any such flaws and if so the instructions are classified as malicious \hyperref[b131]{[134]}. Some of the classification Algorithms that can be used in this Regard are Bayesian \hyperref[b132]{[135]}, SVM \hyperref[b134]{[136]} and Decision Tree \hyperref[b135]{[137]}.  
\section[{vi. Return Oriented Programming}]{vi. Return Oriented Programming}\par
ROP attacks start when an attacker gains stack control \hyperref[b136]{[138]} and redirects the control to a small snippet of code called gadget typically ending with a RET instruction \hyperref[b137]{[139]}. Because attackers gain control over the return addresses \hyperref[b138]{[140]}, they can assign the RET of one gadget to the start of another gadget \hyperref[b140]{[141]}, achieving the desired functionality out of a large finite set of such small gadgets \hyperref[b141]{[142]}. ROP Attacks inject no code and yet can induce arbitrary behavior in the targeted system \hyperref[b142]{[143]}. A compiler-based approach has been suggested in \hyperref[b143]{[144]} to combat any form of ROP. In \hyperref[b144]{[145]}, the authors present in-place code randomization that can be applied directly on third-party software, to mitigate ROP attacks. Buchanan et al., \hyperref[b145]{[146]}, have demonstrated that return-oriented exploits are practical to write, as the complexity of gadget combination is abstracted behind a programming language and compiler. Davi et al. \hyperref[b146]{[147]} proposed runtime integrity monitoring techniques that use tracking instrumentation of program binaries based on taint analysis and dynamic tracing. In \hyperref[b147]{[148]} a tool DROP, that detects ROP malicious code dynamically, is presented.\par
vii. Jump Oriented Programming In Jump Oriented Programming(JOP), an attacker links the gadgets using a finite set of indirect JMP instructions \hyperref[b148]{[149]}, instead of RET instructions. A special gadget called a dispatcher is used for flow control management among the gadgets \hyperref[b149]{[150]}. 
\section[{X. Conclusion}]{X. Conclusion}\par
The purpose of this survey is to explore the importance of Data Mining techniques in achieving security          \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.12978817299205647\textwidth}P{0.004501323918799647\textwidth}P{0.10728155339805825\textwidth}P{0.4321270962047661\textwidth}P{0.1763018534863195\textwidth}}
\multicolumn{2}{l}{Authors}\tabcellsep Algorithm\tabcellsep Performance\tabcellsep Future enhancement\\
Boutet\tabcellsep et\tabcellsep kNN\tabcellsep Better than Randomization scheme\tabcellsep Can consider all attacking models\\
\multicolumn{2}{l}{al.(2015)[45]}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{Tianqing et}\tabcellsep \multicolumn{2}{l}{Correlated Differential Privacy (CDP) Enhances the utility while answering}\tabcellsep Can be experimented with Complex\\
\multicolumn{2}{l}{al.(2015)[46]}\tabcellsep \tabcellsep a large group of queries on correlated\tabcellsep Applications\\
\tabcellsep \tabcellsep \tabcellsep datasets\tabcellsep \\
Bharath\tabcellsep et\tabcellsep PP k-NN classifier\tabcellsep Irrespective of the values of k, it is\tabcellsep Parallelization is not used\\
\multicolumn{2}{l}{al.(2015)[47]}\tabcellsep \tabcellsep observed that SRkNNo is around 33\%\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep faster than SRkNN. E.g., when k=10,\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep the computation costs of SRkNNo and\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep SRkNN are 84.47 and 127.72 minutes,\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep respectively (boosting the online run-\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep ning time of Stage 1 by 33.86\%)\tabcellsep \\
\multicolumn{2}{l}{Nethravathi et}\tabcellsep PPDM\tabcellsep Reduced misplacement clustering error\tabcellsep Works only for numerical data\\
\multicolumn{2}{l}{al.(2015)[48]}\tabcellsep \tabcellsep and removal of data that is sensitive\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep and correlated\tabcellsep \\
\multicolumn{2}{l}{Mohammed}\tabcellsep Differential Privacy\tabcellsep More secured under the Semi-Honest\tabcellsep Overcoming Privacy Attack\\
et\tabcellsep \tabcellsep \tabcellsep model\tabcellsep \\
\multicolumn{2}{l}{al.(2014)[49]}\tabcellsep \tabcellsep \tabcellsep \\
Vaidya\tabcellsep et\tabcellsep Distributed RDT\tabcellsep Lower Computation and Communica-\tabcellsep Limited information that is still re-\\
\multicolumn{2}{l}{al.(2014)[50]}\tabcellsep \tabcellsep tion cost\tabcellsep vealed must be checked\\
\multicolumn{3}{l}{Lee(2014)[51] Perturbation methods}\tabcellsep \multicolumn{2}{l}{Capable of performing RFM Analysis Partial disclosure is still possible}\end{longtable} \par
 
\caption{\label{tab_1}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.6646750524109014\textwidth}P{0.18532494758909854\textwidth}}
1. Creation of a training dataset 2. Identification of classes and attributes 3. Identification of attributes that are useful for classification 4. Relevance analysis 5. Learning the Model using training examples 6. Training the set 7. Using the model for the classification of unknown data samples. Algorithm Performance M Vittapu et al.(2015)[85] SVM Classification TPR of 96\% and FPR of 5\% Mitchell et al.(2015)[61] Behavior Rule Analysis Better performance Jabez J et al.(2014)[98] Hyperboli Hopfiel Neural Network(HHNN) Detection rate of about 90\% S Abadeh et al.(2014)[151] Genetic Fuzzy System Best tradeoff in terms of the mean F-measure,the average accuracy and the false alarm rate Soni et Bayesian Classifiers: Authors al.(2014)[86]\tabcellsep Future enhancement Can be experimented with other tech-niques Can be tested with other techniques Can be improved A Multi-objective Evolutionary Al-gorithm for maximizing performance metrics may be considered\end{longtable} \par
 
\caption{\label{tab_3}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.32583333333333336\textwidth}P{0.5241666666666667\textwidth}}
\tabcellsep Security in Data Mining-A Comprehensive Survey\\
Year 2016\tabcellsep \\
62\tabcellsep \\
Volume XVI Issue V Version I\tabcellsep FS runs as a part of data mining algorithms, in Embbedded technique.\\
)\tabcellsep \\
( C\tabcellsep \\
Global Journal of Computer Science and Technology\tabcellsep \\
\tabcellsep © 2016 Global Journals Inc. (US) 1\end{longtable} \par
 
\caption{\label{tab_4}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_5}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.10672645739910314\textwidth}P{0.21154708520179372\textwidth}P{0.3068385650224215\textwidth}P{0.2248878923766816\textwidth}}
Authors\tabcellsep Algorithm\tabcellsep Performance\tabcellsep Future enhancement\\
C Cortes et\tabcellsep Theoretical framework for analyzing\tabcellsep \multicolumn{2}{l}{Optimizes generalization performance Can be applied for different optimiza-}\\
al.(2016)[106]\tabcellsep and learning artificial neural networks\tabcellsep \tabcellsep tion tecniques and network architec-\\
\tabcellsep \tabcellsep \tabcellsep tures.\\
D T Bui et\tabcellsep ROC and Kappa Index\tabcellsep MLP (90.2 \%), SVM (88.7 \%), KLR\tabcellsep Information Gain Ratio as feature se-\\
al.(2015)[107]\tabcellsep \tabcellsep (87.9 \%), RBF (87.1 \%) and LMT\tabcellsep lection can be tried.\\
\tabcellsep \tabcellsep (86.1 \%).\tabcellsep \\
\tabcellsep \multicolumn{2}{l}{Figure 7:}\tabcellsep \end{longtable} \par
 
\caption{\label{tab_6}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_7}Table 4 :}\end{figure}
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\end{bibitemlist}
 			 		 	 
\end{document}
