Anomaly-based network intrusion detection: Techniques, systems and challenges

By Pedro Garcia-Teodoro, J. D. Verdejo, Gabriel Maciá-Fernández, Enrique Vázquez. Computers & security, 2009

2,264 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/59e742875c39d1e09cfe1be7501a4048efe343de

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In Artificial Intelligence

In Computer Networks and Communications

Citations in the corpus

Deep One-Class Classification (2018, background): “Other domains include intrusion detection for cybersecurity (Garcia-Teodoro et al., 2009), fraud detection (Phua et al.”
A Survey of Stealth Malware Attacks, Mitigation Measures, and Steps Toward Autonomous Open World Solutions (2016, background): “[5], [6], [7], some specifically discussing machine learning intrusion detection techniques [1], [8], [9], [10].”
A Survey of Random Forest Based Methods for Intrusion Detection Systems (2018, methodology): “García-Teodoro et al. (2009) propose a basic structure for anomaly-based network intrusion detection composed by the stages: parametrization, train, and detection.”
Intrusion detection system: A comprehensive review (2013, methodology): “J N U M Audit data, network packets Real-time and active measurement Bayesian-based (Kabiri and Ghorbani, 2005; Patcha and Park, 2007; Garcia-Teodoro et al., 2009; Stavroulakis and Stamp, 2010; Lazarevic et al., 2005) O O –”
A GA-LR wrapper approach for feature selection in network intrusion detection (2017, methodology): “The proposed approach is based on the wrapper approach integrating Genetic Algorithm (GA) as a feature search and Logistic Regression (LR) as a learning algorithm.”
Network intrusion detection system: A systematic study of machine learning and deep learning approaches (2020, background): “19,20 While detection system is a security mechanism for the detection of such illegal activity.”
A Survey on Advanced Persistent Threats: Techniques, Solutions, Challenges, and Research Opportunities (2019, background): “Garcia-Teodoro et al. [52] have classiﬁed and reviewed several anomaly-based network intrusion detection techniques while presenting the challenges to be addressed.”
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].”
A Survey on Machine Learning Techniques for Cyber Security in the Last Decade (2020, methodology): “Teodoro [482] used machine learning and knowledge-based approaches for anomaly-based intrusion detection.”
Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset (2018, methodology): “• Intrusion Detection Systems (IDS) - [16, 17, 18] are classified into network and host intrusion detection systems.”
Autoencoder-based feature learning for cyber security applications (2017, methodology): “Machine learning approaches are being widely used for anomaly intrusion detection [2, 3].”