Artificial Intelligence and Data Mining Approaches in Security Frameworks

5 Code Reuse Attacks: Code reuse attacks (CRAs) are recent development in security. They occur when an attacker expresses the flow of control through a previously existing code. By using this, attackers are allowed to execute random code on a compromised machine. These are return-oriented and jump-oriented programming approaches. They can reclaim library code fragments. The Return Into Lib C (RILC) is a type of code-reuse attack 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 that allows both write and execution operations on it to bypass W+X (Bhatkar et al., 2005). To overcome such attacks, we use data mining techniques. When the source code is checked to reveal any such fault and for this the instructions are classified as malicious. Some of the classification algorithms that can be used in this Regard are Logistic Regression, Bayesian, Support Vector Machine and Decision Tree.

The main aim of this study is to find the role of Data Mining techniques in attaining security. A few applications such as Privacy Preserving Data Mining (PPDM), Intrusion Detection System (IDS), Phishing Website Classification and Mitigation of Code Injection are discussed. Some Classification and Clustering algorithms are also discussed for their significant role in an intrusion detection system. Other basic Data mining techniques used for intrusion detection system such as Feature Extraction, Association Rule Mining and Decision Trees are also discussed. Other security applications of Data Mining such as Malware Detection, Spam Detection, Web Mining and Crime Profiling can also be explored in terms of security as a future scope.

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