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Anomaly-Based NIDS Using Artificial Neural Networks Optimised with Cuckoo Search Optimizer

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Emerging Research in Electronics, Computer Science and Technology

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 545))

Abstract

Anomaly detection in network traffic is one of the major concerns for the researches and the network administrators. Presence of anomalies in network traffic could indicate a possible intrusion on the network, increasing the need for a fast and reliable network intrusion detection system (NIDS). A novel method of using an artificial neural network (ANN) optimised with Cuckoo Search Optimizer (CSO) is developed in this research paper to act as network monitoring and anomaly detection system. Two subsets of the KDD Cup 99 dataset have been considered to train and test our model, one of 2000 instances and the other of 10,000 instances, along with the complete dataset of 61,593 instances and I have compared the result with the BCS-GA algorithm and the fuzzy K-means clustering algorithm optimised with PSO in terms of precision, recall and f1-score, and the training time for the model with the selected database instances.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, Mangalore, India

    K. Rithesh

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  1. K. Rithesh
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Correspondence to K. Rithesh .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, PES College of Engineering, Mandya, Karnataka, India

    V. Sridhar

  2. Department of Computer Science and Engineering, PES College of Engineering, Mandya, Karnataka, India

    M.C. Padma

  3. Department of Electronics and Communication Engineering, PES College of Engineering, Mandya, Karnataka, India

    K.A. Radhakrishna Rao

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Rithesh, K. (2019). Anomaly-Based NIDS Using Artificial Neural Networks Optimised with Cuckoo Search Optimizer. In: Sridhar, V., Padma, M., Rao, K. (eds) Emerging Research in Electronics, Computer Science and Technology. Lecture Notes in Electrical Engineering, vol 545. Springer, Singapore. https://doi.org/10.1007/978-981-13-5802-9_3

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  • DOI: https://doi.org/10.1007/978-981-13-5802-9_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-5801-2

  • Online ISBN: 978-981-13-5802-9

  • eBook Packages: EngineeringEngineering (R0)

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