Network Intrusion Detection by Artificial Immune System and Neural Network
Easy access, simulation of IOT network increases its application and demands in different area. As many of IOT networks are vulnerable in nature and attracts intruders to take advantage of weak security. This paper has developed a model that can detect the IOT network intrusion. In this work feature optimization was done by use of artificial immune system algorithm. AIS reduces the dimension of the dataset by applying affinity check and cloning steps. Selected features were further use for the traiing of neural network. Trained neural network predict the class of IOT network session (Normal / Malicious). Experiment was done on real dataset of IOT session and result shows that rpopsoed model has improved the detection accuracy as compared o existing models.
References:
J. Deogirikar and A. Vidhate, “Security attacks in iot: A survey,” International Conference on I-SMAC (I-SMAC), pp. 32–37, 2017.
T. Bodstrom and T. H ¨ am¨ al¨ ainen, “State of the art literature review ¨ on network anomaly detection with deep learning,” Internet of Things, Smart Spaces, and Next Generation Networks and Systems, pp. 64–76, 2018.
I. Arnaldo, A. Cuesta-Infante, A. Arun, M. Lam, C. Bassias, and K. Veeramachaneni, “Learning representations for log data in cybersecurity,” International Conference on Cyber Security Cryptography and Machine Learning, pp. 250–268, 2017.
M. Du, F. Li, G. Zheng, and V. Srikumar, “Deeplog: Anomaly detection and diagnosis from system logs through deep learning,” Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 1285–1298, 2017.
B. J. Radford, B. D. Richardson, and S. E. Davis, “Sequence aggregation rules for anomaly detection in computer network traffic,” arXiv preprint arXiv:1805.03735, 2018.
I. Lambert and M. Glenn, “Security analytics: Using deep learning to detect cyber attacks,” 2017.
M. Stevanovic and J. M. Pedersen, “Detecting bots using multi-level traffic analysis.” IJCSA, vol. 1, no. 1, pp. 182–209, 2016.
Zhang, Y.; Li, P.; Wang, X. Intrusion Detection for IoT Based on Improved Genetic Algorithm and Deep Belief Network. IEEE Access 2019, 7, 31711–31722.
Moustafa, N.; Turnbull, B.; Choo, K.R. An Ensemble Intrusion Detection Technique based on proposed Statistical Flow Features for Protecting Network Traffic of Internet of Things. IEEE Internet Things J. 2018, 6, 4815–4830.
Bagaa, M.; Taleb, T.; Bernal, J.; Skarmeta, A. A machine learning Security Framework for Iot Systems. IEEE Access 2020, 8, 114066–114077.
Susilo, B.; Sari, R. Intrusion Detection in IoT Networks Using Deep Learning Algorithm. Information 2020, 11, 279.
Pokhrel, S.; Abbas, R.; Aryal, B. IoT Security: Botnet detection in IoT using Machine learning. arXiv 2021, arXiv:2104.02231.
21. Shafiq, M.; Tian, Z.; Sun, Y.; Du, X.; Guizani, M. Selection of effective machine learning algorithm and Bot-IoT attacks traffic identification for internet of things in smart city. Future Gener. Comput. Syst. 2020, 107, 433–442.
Ullah I., Mahmoud Q.H. (2020) A Scheme for Generating a Dataset for Anomalous Activity Detection in IoT Networks. In: Goutte C., Zhu X. (eds) Advances in Artificial Intelligence. Canadian AI 2020.
A. Fatani, M. Abd Elaziz, A. Dahou, M. A. A. Al-Qaness and S. Lu, "IoT Intrusion Detection System Using Deep Learning and Enhanced Transient Search Optimization," in IEEE Access, vol. 9.
Copyright (c) 2022 International Journal of Engineering and Computer Science
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.