Data Mining Methods for Detection of New Malicious Executables
SP '01 Proceedings of the 2001 IEEE Symposium on Security and Privacy
A semantics-based approach to malware detection
Proceedings of the 34th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
A Feature Selection and Evaluation Scheme for Computer Virus Detection
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Intrusion detection using sequences of system calls
Journal of Computer Security
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Pattern Recognition and Information Processing Using Neural Networks;Guest Editors: Fuchun Sun,Ying Tan,Cong Wang
Concentration based feature construction approach for spam detection
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A Hierarchical Artificial Immune Model for Virus Detection
CIS '09 Proceedings of the 2009 International Conference on Computational Intelligence and Security - Volume 01
A Virus Detection System Based on Artificial Immune System
CIS '09 Proceedings of the 2009 International Conference on Computational Intelligence and Security - Volume 01
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
A danger feature based negative selection algorithm
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
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This paper proposes an immune concentration based virus detection approach which utilizes a two-element concentration vector to construct the feature In this approach, ‘self' and ‘nonself' concentrations are extracted through ‘self' and ‘nonself' detector libraries, respectively, to form a vector with two elements of concentrations for characterizing the program efficiently and fast Several classifiers including k-nearest neighbor (KNN), RBF neural network and support vector machine (SVM) with this vector as input are then employed to classify the programs The selection of detector library determinant and parameters associated with a certain classifier is here considered as an optimization problem aiming at maximizing the accuracy of classification A clonal particle swarm optimization (CPSO) algorithm is used for this purpose Experimental results demonstrate that the proposed approach not only has a very much fast speed but also gives around 98% of accuracy under optimum conditions.