Undermining an Anomaly-Based Intrusion Detection System Using Common Exploits

By Kymie M. C. Tan, Kevin S. Killourhy, Roy A. Maxion. 2002

258 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Safety, Robustness & Interpretability, Privacy & Security, Signal Processing

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Citations in the corpus

Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks (2018, background): “Many of the early attacks on machine learning were exploratory attacks on network and host-based intrusion detection systems [43,41,15,14] or spam ﬁlters [44,29,30,23].”
Using Machine Teaching to Identify Optimal Training-Set Attacks on Machine Learners (2015, background): “The study on security threats to intelligent agents has a long history [21, 24, 23, 3, 18, 4, 11, 19, 5, 17].”
Poisoning behavioral malware clustering (2014, background): “…setup slightly simpliﬁes our attack, previous work has already shown that creating artiﬁcial q -grams of system calls, and similarly the use of diﬀerent MIST levels, is not a challenge for an attacker [29, 30, 34], especially if she has full control over the behavior, as in the case of malware.”
Query Strategies for Evading Convex-Inducing Classifiers (2010, background): “A number of authors have studied evading sequence-based intrusion detector systems (IDSs) (Tan et al., 2002; Wagner and Soto, 2002).”
Data Poisoning Attacks against Autoregressive Models (2016, methodology): “The security ramiﬁcations of using machine learning models have long been studied (Barreno et al. 2006; 2010; Dalvi et al. 2004; Laskov and Kloft 2009; Tan, Killourhy, and Maxion 2002).”
A Learning-Based Approach to the Detection of SQL Attacks (2005, methodology): “By using multiple models it is possible to reduce the susceptibility of the detection process to mimicry attacks [22,20].”
Open problems in the security of learning (2008, background): “intrusion detection systems [7, 27], and email spam filtering [16, 19, 30].”
An overview of anomaly detection techniques: Existing solutions and latest technological trends (2007, background): “An important aspect of intrusion detection, which has also been proposed as an evaluation metric [94-96], is the ability of an intrusion detection system to defend itself from attacks.”
Adversarial machine learning (2019, background): “Several authors have further explored evasion techniques used by spammers and demonstrated attacks against spam .lters using similar principles as those against IDSs as discussed above.”
A framework for the evaluation of intrusion detection systems (2006, unknown): “Several researchers have pointed out the need to include the resistance against attacks as part of the evaluation of an IDS [25, 27, 11, 34, 29, 30, 13].”
Anagram: A Content Anomaly Detector Resistant to Mimicry Attack (2006, background): “The notion of a mimicry attack on an anomaly detection system was first introduced in 2001 by Wagner and Dean [37], but initial efforts to generate mimicry attacks, including [38] and [39], focused on system-call anomaly detection.”
Random-Forests-Based Network Intrusion Detection Systems (2008, unknown): “to detect these kinds of intrusions [28].”
Mimicry attacks on host-based intrusion detection systems (2002, methodology): “Since in practice many attacks arise from automated scripts, script writers may someday incorporate techniques designed to evade the popular IDS s in their scripts.”
Practical Attacks Against Graph-based Clustering (2017, background): “For example, features are directly obtained from spam emails [36, 60], PDF files [49, 53, 62], phishing pages [49], images [16, 25, 44, 52], network attack packets [24], and exploits [55, 58].”
Hiding Intrusions: From the Abnormal to the Normal and Beyond (2002, background): “Preliminary results showing that intrusions can be hidden in the blind spot of stide also appear in a paper by Tan, et. al. [ 17 ].”