Interface Detector Based on Vaccination Strategy for Anomaly Detection

Interface detector is an enhanced negative selection algorithm with online adaptive learning under small training samples for anomaly detection. It has better detection performance when it has an appropriate self-radius. Otherwise, overfitting or underfitting would occur. In the present paper, an improved interface detector, which is based on vaccination strategy, is proposed. During the testing stage, negative vaccine can overcome overfitting to improve the detection rate and positive vaccine can overcome underfitting to reduce the false alarm rate. The experimental results show that under the same dataset, self-radius, and training samples condition, the detection rate of the interface detector with negative vaccine is much higher than that of interface detector, SVM, and BP neural network. Moreover, the false alarm rate of the interface detector with positive vaccine is much lower than that of the interface detector and PSA.

[1]  Furong Gao,et al.  Continual learning classification method with new labeled data based on the artificial immune system , 2020, Appl. Soft Comput..

[2]  Muhammad Tahir Khan,et al.  Layered and Real-Valued Negative Selection Algorithm for Fault Detection , 2018, IEEE Systems Journal.

[3]  Gilbert L. Peterson,et al.  An evolutionary algorithm to generate ellipsoid network intrusion detectors , 2005, GECCO '05.

[4]  Alan S. Perelson,et al.  Self-nonself discrimination in a computer , 1994, Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy.

[5]  Zhou Ji,et al.  V-detector: An efficient negative selection algorithm with "probably adequate" detector coverage , 2009, Inf. Sci..

[6]  Salim Chikhi,et al.  Clustered negative selection algorithm and fruit fly optimization for email spam detection , 2017, Journal of Ambient Intelligence and Humanized Computing.

[7]  André Dionísio Rocha,et al.  Artificial immune systems based multi-agent architecture to perform distributed diagnosis , 2019, J. Intell. Manuf..

[8]  Fernando Niño,et al.  A Framework for Evolving Multi-Shaped Detectors in Negative Selection , 2007, 2007 IEEE Symposium on Foundations of Computational Intelligence.

[9]  Fabio A. González,et al.  An immunity-based technique to characterize intrusions in computer networks , 2002, IEEE Trans. Evol. Comput..

[10]  Fabio A. González,et al.  An Imunogenetic Technique To Detect Anomalies In Network Traffic , 2002, GECCO.

[11]  N. Grubhofer.  Vaccine Adjuvants Revisited , 2008 .

[12]  Luca Benini,et al.  A semisupervised autoencoder-based approach for anomaly detection in high performance computing systems , 2019, Eng. Appl. Artif. Intell..

[13]  Shulin Liu,et al.  Numerical analysis of the dynamic behavior of a rotor-bearing-brush seal system with bristle interference , 2019, Journal of Mechanical Science and Technology.

[14]  Dong Li,et al.  A method of anomaly detection and fault diagnosis with online adaptive learning under small training samples , 2017, Pattern Recognit..

[15]  Dong Li,et al.  A negative selection algorithm with online adaptive learning under small samples for anomaly detection , 2015, Neurocomputing.

[16]  Liang Xi,et al.  Evolving boundary detector for anomaly detection , 2011, Expert Syst. Appl..

[17]  Fernando Niño,et al.  Recent Advances in Artificial Immune Systems: Models and Applications , 2011, Appl. Soft Comput..

[18]  Li Tao,et al.  A self-adaptive negative selection algorithm used for anomaly detection , 2009 .

[19]  P. Helman,et al.  A formal framework for positive and negative detection schemes , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[20]  Maoguo Gong,et al.  An efficient negative selection algorithm with further training for anomaly detection , 2012, Knowl. Based Syst..

[21]  Dong Li,et al.  Negative selection algorithm with constant detectors for anomaly detection , 2015, Appl. Soft Comput..