The use of computational intelligence in intrusion detection systems: A review

By Shelly Xiaonan Wu, Wolfgang Banzhaf. Applied Soft Computing, 2010

807 citations recorded by the source corpus.

Intrusion detection based upon computational intelligence is currently attracting considerable interest from the research community. Characteristics of computational intelligence (CI) systems, such as adaptation, fault tolerance, high computational speed and error resilience in the face of noisy information, fit the requirements of building a good intrusion detection model. Here we want to provide an overview of the research progress in applying CI methods to the problem of intrusion detection. The scope of this review will encompass core methods of CI, including artificial neural networks, fuzzy systems, evolutionary computation, artificial immune systems, swarm intelligence, and soft computing. The research contributions in each field are systematically summarized and compared, allowing us to clearly define existing research challenges, and to highlight promising new research directions. The findings of this review should provide useful insights into the current IDS literature and be a good source for anyone who is interested in the application of CI approaches to IDSs or related fields.

Subfields: Artificial Intelligence, Biomedical Engineering, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/7f8f5cc692b3faad4db4d25c08140d8ad6f5ed26

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Citations in the corpus

A Survey of Random Forest Based Methods for Intrusion Detection Systems (2018, background): “Wu and Banzhaf (2010) present a review of comparison metrics, datasets, and Machine-Learning methods applied for network intrusion detection.”
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection (2016, background): “[6] focus on Computational Intelligence methods and their applications to intrusion detection.”
Intrusion Detection in 802.11 Networks: Empirical Evaluation of Threats and a Public Dataset (2016, methodology): “In particular, Swarm Intelligence techniques [66], Markov Chain [67] based ones as well as other soft computing methods [68] seem to possess desirable characteristics in”
A GA-LR wrapper approach for feature selection in network intrusion detection (2017, background): “The evaluation criteria adopted by most of papers to assess the performance of their approaches are accuracy, detection rate (DR), and false positive rate (FPR) (Lin et al., 2015; Wu and Banzhaf, 2010).”
Network Anomaly Detection: Methods, Systems and Tools (2014, background): “Methods /NIDSs /Tools Topics covered [8] [10] [11] [6] [16] [17] [3] [7] [21] [26] [29] [31] [32] [33] [34] Our survey”
A Roadmap Toward the Resilient Internet of Things for Cyber-Physical Systems (2018, neutral): “, based on machine learning / data mining [25] or computational intelligence [26]).”
Distributed Intrusion Detection System in a Multi-Layer Network Architecture of Smart Grids (2011, neutral): “The instance of the unsupervised learning of the ANN is self-organizing maps (SOM) are single-layer feed forward networks, their outputs can be compressed into a low dimensional grid and they are the most widely used NN for anomaly detection tasks [55].”
A Survey on Machine Learning Techniques for Cyber Security in the Last Decade (2020, methodology): “Others used Fuzzy Logic, ANN for their application in intrusion detection [484].”
An efficient intrusion detection system based on support vector machines and gradually feature removal method (2012, background): “For explicit review of current development, refer to the recent review (Tsai, Hsu, Lin, & Lin, 2009; Wu & Banzhaf, 2010).”
An intrusion detection and prevention system in cloud computing: A systematic review (2013, background): “However, IDPS performance is hindered by the high false alarm rate it produces (Wu and Banzhaf, 2010).”