The huge amounts of data and information that need to be analyzed for possible malicious intent are one of the big and significant challenges that the Web faces today. Malicious software, also referred to as malware developed by attackers, is polymorphic and metamorphic in nature which can modify the code as it spreads. In addition, the diversity and volume of their variants severely undermine the effectiveness of traditional defenses that typically use signature-based techniques and are unable to detect malicious executables previously unknown. Malware family variants share typical patterns of behavior that indicate their origin and purpose. The behavioral trends observed either statically or dynamically can be manipulated by using machine learning techniques to identify and classify unknown malware into their established families. This survey paper gives an overview of the malware detection and analysis techniques and tools.

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The urging need for seamless connectivity in mobile environment has contributed to the rapid expansion of Mobile IP. Mobile IP uses wireless transmission medium, thereby making it subject to many security threats during various phases of route optimization. Modeling Mobile IP attacks reasonably and efficiently is the basis for defending against those attacks, which requires quantitative analysis and modeling approaches for expressing threat propagation in Mobile IP. In this Paper, we present four well-known Mobile IP attacks, such as Denialof-Service (DoS) attack, bombing attack, redirection attack and replay attack and model them with Stochastic Game Petri Net (SGPN). Furthermore, we propose mixed strategy based defense strategies for the aforementioned attacks and model them with SGPN. Finally, we calculate the Nash Equilibrium of the attacker-defender game and thereby obtain the steady state probability of the vulnerable attack states. We show that, under the optimal strategy, an IDS needs to remain active 72.4%, 70%, 68.4% and 66.6% of the time to restrict the attacker's success rate to 8.5%, 6.4%, 7.2% and 8.3% respectively for the aforementioned attacks, thus performing better than the stateof-the-art approach.

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