Genetic Algorithm Based Anomaly Detection for Intrusion Detection

Author:

Mayanglambam Sushilata D.ORCID,Hulke Nainesh,Pamula RajendraORCID

Publisher

Springer Nature Switzerland

Reference39 articles.

1. Anderson, J.P.: Computer security threat monitoring and surveillance. Technical report, Fort Washington, PA, USA, pp. 9–11 (1980)

2. Endorf, C., Schultz, E., Mellander, J.: Intrusion Detection and Prevention. Illustrated edn. McGraw-Hill Education, California (2004)

3. Silva, L.D.S., Santos, A.C., Mancilha, T.D., Silva, J.D., Montes, A.: Detecting attack signatures in the real network traffic with ANNIDA. Expert Syst. Appl. 34(4), 2326–2333 (2008)

4. Manikopoulos, C., Papavassiliou, S.: Network intrusion and fault detection: a statistical anomaly approach. IEEE Commun. Mag. 40(10), 76–82 (2002)

5. Lv, L., Wang, W., Zhang, Z., Liu, X.: A novel intrusion detection system based on an optimal hybrid kernel extreme learning machine. Knowl. Based Syst. 195(105648), 1–17 (2020)

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