Internet was designed to serve the basic requirement of data transfer between systems. The security perspectives were therefore overlooked due to which the Internet remains vulnerable to a variety of attacks. Among all the possible attacks, Distributed Denial of Service (DDoS) attack is one of the eminent threats that target the availability of the online services to the intended clients. Now-a-days, attackers target application layer of the network stack to orchestrate attacks having a high degree of sophistication. GET flood attacks have been very much prevalent in recent years primarily due to advancement of bots allowing impersonating legitimate client behavior. Differentiating between a human client and a bot is therefore necessary to mitigate an attack. This paper introduces a mitigation framework based on Fuzzy Control System that takes as input two novel detection parameters. These detection parameters make use of clients' behavioral characteristic to measure their respective legitimacy. We design an experimental setup that incorporates two widely used benchmark web logs (Clarknet and WorldCup) to build legitimate and attack datasets. Further, we use these datasets to assess the performance of the proposed through well-known evaluation metrics. The results obtained during this work point towards the efficiency of our proposed system to mitigate a wide range of GET flood attack types.

Nature-inspired algorithms are recently being appreciated for solving complex optimization and engineering problems. Black hole algorithm is one of the recent nature-inspired algorithms that have obtained inspiration from black hole theory of universe. In this paper, four formulations of multi-objective black hole algorithm have been developed by using combination of weighted objectives, use of secondary storage for managing possible solutions and use of Genetic Algorithm (GA). These formulations are further applied for scheduling jobs on parallel machines while optimizing bi-criteria namely maximum tardiness and weighted flow time. It has been empirically verified that GA based multi-objective Black Hole algorithms leads to better results as compared to their counterparts. Also the use of combination of secondary storage and GA further improves the resulting job sequence. The proposed algorithms are further compared to some of the existing algorithms, and empirically found to be better. The results have been validated by numerical illustrations and statistical tests.