A Review of Anomaly based Intrusion Detection Systems

By V. Jyothsna, Sree Vidyanikethan, Engineering College, A. Rangampet, Tirupati V V Rama, P. Professor, H. Sree, Vidyanikethan Engineering, College A Rangampet, Tirupati K Munivara and 1 more. 2011

427 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/f4179593b4b47d8be8cd97e0caced755e16fefbb

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Citations in the corpus

Evaluation and Design of Robust Neural Network Defenses (2018, background): “And in the space of intrusion detection, there is a similar story of research on detecting intrusions [137, 138, 54, 47] (and again, even using neural networks [71]) and evading intrusion detection [120, 30].”
Fraud detection system: A survey (2016, methodology): “Anomaly or outlier detection approach is used by FDS and it relies on behavioral pro ﬁ ling methods, where it models each individual's behavioral pattern, monitoring it for any deviation from the norm (Jyothsna and Rama Prasad, 2011).”
Anomaly detection in online social networks (2014, methodology): “m domains. For example, methods for performing anomaly detection have been discussed as part of more generalreviewsinareasoffrauddetection (Bolton and Hand, 2002; Phua et al., 2010), networkintrusion(Jyothsna et al., 2011; Gogoi et al., 2011; Patcha and Park, 2007), and the deploymentofwirelesssenso”
A review of novelty detection (2014, background): “card or mobile phone fraud detection [6,7], video surveillance [8,9], mobile robotics [10,11], sensor networks [12], astronomy catalogues [13,14] and text mining [15].”