Bayesian event classification for intrusion detection

By Christopher Krügel, Darren H. Mutz, William K. Robertson, Fredrik Valeur. 19th Annual Computer Security Applications Conference, 2003. Proceedings., 2003

440 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/61e4527d45bbe75cef089ba389eee286ed43cf90

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Citations in the corpus

A framework for the evaluation of intrusion detection systems (2006, neutral): “The difference lies in the fact that we are monitoring the execution of every program while the experiments in [14] only monitor the attacked programs (eject, fbconfig, fdformat and ps).”
A survey of network anomaly detection techniques (2016, background): “Based on the concept of the Bayesian network, the authors of Kruegel et al. (2003) proposed an approach for solving the aforementioned problems.”
A review of novelty detection (2014, methodology): “This has been applied to network intrusion and web-based attack detection [117,140–142].”
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection (2016, methodology): “[44] used a Bayesian network to classify events during open and executive OS calls.”
Bayesian Neural Networks for Internet Traffic Classification (2007, background): “However, this work has largely b en targeted at the detection of malicious behaviour on a multiuser machine or the filtering of false-alarms [17].”
DDoS Attack Protection in the Era of Cloud Computing and Software-Defined Networking (2014, background): “[7] proposed a Bayesian classification algorithm to do intrusion detection by monitoring the system calls.”
An overview of anomaly detection techniques: Existing solutions and latest technological trends (2007, background): “First, as pointed out in [29], the classification capability of naı¨ve Bayesian networks is identical to a threshold based system that computes the sum of the outputs obtained from the child nodes.”
Anomaly-based network intrusion detection: Techniques, systems and challenges (2009, result): “However, as pointed out in Kruegel et al. (2003), a serious disadvantage of using Bayesian networks is that their results are similar to those derived from threshold-based systems, while considerably higher computational effort is required.”
Network Anomaly Detection: Methods, Systems and Tools (2014, background): “For example, the authors of [91] introduce an event classification-based intrusion detection scheme using Bayesian networks.”
DDoS Attack Detection and Mitigation Using SDN: Methods, Practices, and Solutions (2017, methodology): “Artificial neural networks [49], Bayesian networks [50], selforganizing map (SOM) [51], and fuzzy logic [52] principles and concepts are extensively used for detecting anomalies in IDS.”