A retrovirus inspired algorithm for virus detection & optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Genetic fingerprinting for copyright protection of multicast media
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Bio-Inspired Information Hiding; Guest editors: Jeng-Shyang Pan, Ajith Abraham
Computer Immune System for Intrusion and Virus Detection - Adaptive Detection Mechanisms and their Implementation
CTO roundtable: malware defense
Communications of the ACM
A refactoring method for cache-efficient swarm intelligence algorithms
Information Sciences: an International Journal
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
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This paper presents a novel approach for computer viruses detection based on modeling the structures and dynamics of real life paradigm that exists in the bodies of all living creatures. It aims to develop an algorithm based on the concept of the artificial immune system (AIS) for the purpose of detecting viruses. The algorithm is called Virus Detection Clonal algorithm (VDC), and it is derived from the clonal selection algorithm. The VDC algorithm consists of three basic steps: cloning, hyper-mutation and stochastic re-selection. In later stage, the developed VDC algorithm is subjected to validation, which consists of two phases; learning and testing. Two main parameters are determined; one of them is setting the number of signatures per clone (Fat), while the other defines the hypermutation probability (Pm). Later on, the Genetic Algorithm (GA) is used as a tool, to improve the developed algorithm by searching the values of the main parameters (Fat and Pm) to reproduce better results. The results have shown that the detection rate of viruses, by using the developed algorithm, is 94.4%, whereas the detection rate of false positives has reached 0%. These percentages indicate that the VDC algorithm is sufficient and usable in this field. Moreover, the results of employing the GA to optimize the VDC algorithm have shown an improvement in the detection speed of the algorithm.