Adversarial attacks against intrusion detection systems: Taxonomy, solutions and open issues

By Igino Corona, Giorgio Giacinto, Fabio Roli. 2013

232 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Control and Systems Engineering, Safety, Robustness & Interpretability, Privacy & Security, Signal Processing

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Citations in the corpus

Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning (2017, background): “Security is an arms race, and the security of machine learning and pattern recognition systems is not an exception to this [41, 42, 54].”
Adversarial Attacks and Defences: A Survey (2018, background): “There have been a handful of surveys on security evaluation related to particular machine learning applications [13], [12], [23], [17].”
A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data Driven View (2018, background): “There are many concrete security threats towards different machine learning models and corresponding application scenarios [22].”
Lucid: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection (2020, background): “As detailed in [67], the two main attacks on IDSs are during training via a poisoning attack (i.”
On the Security of Machine Learning in Malware C&C Detection (2016, methodology): “Their categorisation takes into account the goals of the attack in terms of the speciﬁcity ( targeted or indiscriminate ), the security violation being instigated ( integrity or availability ) and the inﬂuence of the attack ( causative or exploratory ).”
Intriguing Properties of Adversarial ML Attacks in the Problem Space (2019, background): “malware [38, 60], NIDS [6, 7, 20, 28], ICS [76], source”
Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey (2023, background): “A typical NIDS consists of three phases: monitoring, detection, and response [8].”