Abstract
Local wireless networks (WLAN) are vulnerable to various types of security threats ranging from session hijacking to denial of service (DoS), and password attacks, to name a few. They are also subject to a wide range of 802.11-specific threats. The risks can become even higher and more serious when the WLAN network is made up of a number of IoT objects. As a remedy these failures, an intrusion prevention system (WIPS) has been on the network. However, the breadth of the network, the diversity of the elements to be secured and the approaches to be adopted make this integration sometimes complicated or ineffective in certain types of WLAN network. The main concern in this document is to develop, on the basis of free solutions, a flexible, easy-to-deploy and manage WIPS system that provides both intrusion detection and flow monitoring to reduce the rate of false positives, especially during home deployment or on small-scale networks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agalit MA, Khamlichi YI et Chakir EM (2019) A survey and taxonomy of techniques used for alerts of Intrusion Detection Systems. In: Proceedings of the 4th international conference on big data and Internet of Things. pp 1–6
Sun X, Dai J, Liu P, Singhal A, Yen J (2018) Using Bayesian networks for probabilistic identification of zero-day attack paths. IEEE Trans Inf Forensics Secur. 13:2506–2521
Alazab M, Tang M (2019) Deep learning applications for cyber security. Springer, Berlin/Heidelberg, Germany
Alazab A, Hobbs M, Abawajy J, Khraisat A (2013) Malware detection and prevention system based on multi-stage rules. Int. J. Inf. Secur. Priv. 7:29–43
Khraisat A, Gondal I, Vamplew P, Kamruzzaman J, Alazab A (2019) A Novel ensemble of hybrid intrusion detection system for detecting Internet of Things attacks. Electronics 8:1210
Alazab A, Hobbs M, Abawajy J, Khraisat A, Alazab M (2014) Using response action with intelligent intrusion detection and prevention system against web application malware. Inf Manage Comput Secur 22:431–449
Khraisat A, Gondal I, Vamplew P (2018) An anomaly intrusion detection system using C5 decision tree classifier. In Proceedings of the Pacific-Asia conference on knowledge discovery and data mining, Melbourne, Australia, 3–6 June 2018, pp 149–155
SumaiyaThaseen I, Aswani Kumar C (2017) Intrusion detection model using fusion of chi-square feature selection and multi class SVM. J King Saud Univ Comput Inf Sci 29:462–472
Benqdara S (2018) Anomaly intrusion detection system based on unlabeled data. Int J Comput Appl 181(25):18–26
Khraisat A et al (2019) Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity 2(1):20
Alazab A, Abawajy J, Hobbs M, Khraisat A (2013) Crime toolkits: the current threats to web applications. J. Inf. Priv. Secur. 9:21–39
Farooq Y, Beenish H, et Fahad M (2019) Intrusion detection system in wireless sensor networks—A comprehensive survey. In: 2019 Second international conference on latest trends in electrical engineering and computing technologies (INTELLECT). IEEE, pp 1–6
Islam Md (2018). Comparative analysis of intrusion prevention system. Dissertation. Daffodil International University
WRT Homepage. https://dd-wrt.com. Last accessed 10 June 2020
BRO Homepage. https://zeek.org. Last accessed 07 June 2020
Aldwairi M, Mardini W, et Alhowaide A (2018) Anomaly payload signature generation system based on efficient tokenization methodology. Int J Commun Antenna Propag (IRECAP) 8(5)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Agalit, M.A., Sadiqui, A., Khamlichi, Y., Chakir, E.M. (2022). Hybrid Intrusion Detection System for Wireless Networks. In: Bennani, S., Lakhrissi, Y., Khaissidi, G., Mansouri, A., Khamlichi, Y. (eds) WITS 2020. Lecture Notes in Electrical Engineering, vol 745. Springer, Singapore. https://doi.org/10.1007/978-981-33-6893-4_47
Download citation
DOI: https://doi.org/10.1007/978-981-33-6893-4_47
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-6892-7
Online ISBN: 978-981-33-6893-4
eBook Packages: EngineeringEngineering (R0)