CANN: An intrusion detection system based on combining cluster centers and nearest neighbors

By Wei‐Chao Lin, Shih-Wen Ke, Chih-Fong Tsai. Knowledge-Based Systems, 2015

514 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/8a55c4c1a300fc6dcbfa13db68ab51da8e291d3f

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Citations in the corpus

Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, background): “[202] combines cluster centers and nearest neighbors for effective feature representation which ensures a better intrusion detection, however, a limi-”
A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks (2019, methodology): “Existing intrusion detection and prevention models generally use statistical approaches [15] such as Hidden Markov Model (HMM) [15], Bayes theory [16], cluster analysis [17], signal processing [18] and distance measuring [19] to detect anomalous activities.”
Network Intrusion Detection for IoT Security Based on Learning Techniques (2019, background): “deployed separately and combined [139], [140] for improved outcome in general systems.”
A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection (2019, methodology): “For example, in Cluster Center and Nearest Neighbor (CANN) [144] approach one dimensional distance based feature is used to represent each”
A Survey of Random Forest Based Methods for Intrusion Detection Systems (2018, unknown): “…Zulkernine 2005; Zhao et al. 2013), Random Forests (Hasan et al. 2016; Jiong Zhang et al. 2008; Malik et al. 2015; Singh et al. 2014), Clustering (Li et al. 2015; Lin et al. 2015; Mai and Park 2016), and ensemble of methods (Comar et al. 2013; Dubey and Dubey 2015; Ji et al. 2016; Kim et al. 2014).”
Intrusion detection model using fusion of chi-square feature selection and multi class SVM (2016, background): “Comparing the run time with the most recent related works, (ie. testing times) Lin et al. (2015), Kim et al. (2014) and Nadiammai and Hemalatha (2014) obtained times of 13, 11.2 and 8 s which had the complete KDD data set containing about 4,00,000 data packets whereas our experiments deal with…”
A GA-LR wrapper approach for feature selection in network intrusion detection (2017, background): “Neither is efficient for all kinds of threats, yet each has strengthens and limitations (Lin et al., 2015).”
Survey of intrusion detection systems: techniques, datasets and challenges (2019, background): “AIDS methods can be categorized into three main groups: Statistics-based (Chao et al., 2015), knowledge-based (Elhag et al., 2015; Can & Sahingoz, 2015), and machine learning-based (Buczak & Guven, 2016; Mesh-ram & Haas, 2017).”