Intrusion Detection Systems: A Survey and Taxonomy

By Stefan Axelsson. 2002

1,039 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/550aec01bf61ff9fd271debc394a8c3dfa59657b

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Citations in the corpus

UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set) (2015, background): “However, in the anomaly based, a normal profile is created from the normal behavior of the network, and any deviation from this is considered as attack [3] [4].”
An Overview of IP Flow-Based Intrusion Detection (2010, methodology): “At the second stage, packet inspection can be used to additionally protect critical servers or selected systems, for which the rst stage has discovered suspicious activities.”
Immune system approaches to intrusion detection – a review (2004, methodology): “Here we focus on two ways: the analysis approach and the placement of the IDS, although there have been recent work [10, 35] on alternative taxonomies.”
A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges (2021, methodology): “The survey on intrusion detection systems and taxonomy by Axelsson (Axelsson, 2000) classified intrusion detection systems based on the detection methods.”
A survey of attacks and detection mechanisms on intelligent transportation systems: VANETs and IoV (2017, background): “While some approaches have response mechanism to detected attacks, they are mainly passive responses such as raising alarms, or active attacks that seek control over the attacked system [91] such as isolation of nodes and dropping malicious packets.”
An overview of anomaly detection techniques: Existing solutions and latest technological trends (2007, methodology): “For a detailed exposition of techniques proposed before 2000, the reader is directed to the survey by Axelsson [6].”
A taxonomy of DDoS attack and DDoS defense mechanisms (2004, background): “Examples of system security mechanisms include monitored access to the machine [71], applications that download and install security patches, firewall systems [49], intrusion detection systems [6], and worm defense systems [73].”
The role of communication systems in smart grids: Architectures, technical solutions and research challenges (2013, methodology): “There are different approaches that can be applied to design intrusion detection techniques, but at least three major methodologies can be identified [323]:”
Naive Bayes vs decision trees in intrusion detection systems (2004, background): “There are two general approaches to intrusion detection [1]: Anomaly detection: The idea is that each user has a certain profile within the system that will not be changed a lot in time.”
Intrusion detection system: A comprehensive review (2013, background): “Pattern Matching (Debar et al., 1999; Axelsson, 2000; Krugel and Toth, 2000; Debar et al., 2000; Murali and Rao, 2005; Sabahi and Movaghar, 2008; Lazarevic et al., 2005; Kartit et al., 2012) –”
A Survey of Stealth Malware Attacks, Mitigation Measures, and Steps Toward Autonomous Open World Solutions (2016, background): “, [5]–[7], some specifically discussing machine learning intrusion detection techniques [1], [8]–[10].”
Taxonomy and Survey of Collaborative Intrusion Detection (2015, background): “1999; Axelsson 2000; Chandola et al. 2009; Barry and Chan 2010], only a few focus specifically on CIDSs and their architecture [Bye et al. 2010; Zhou et al. 2010]. In particular, Zhou et al. [2010] presented a study of CIDSs and focused mainly on the system architectures and the correlation of alert”
Network intrusion detection system: A systematic study of machine learning and deep learning approaches (2020, background): “25,26 The advantage includes the high detection efficiency for the known attacks due to the availability of signature for those attacks.”
Survey of intrusion detection systems: techniques, datasets and challenges (2019, methodology): “Existing review articles (e.g., such as (Buczak & Guven, 2016; Axelsson, 2000; Ahmed et al., 2016; Lunt, 1988; Agrawal & Agrawal, 2015)) focus on intrusion detection techniques or dataset issue or type of computer attack and IDS evasion.”
Machine Learning Based Intrusion Detection Systems for IoT Applications (2019, background): “Many solutions for intrusion detection have been proposed in the literature [18, 8, 47, 44, 32, 14] and most of them are dedicated for traditional networking paradigms only, and as far as the literature is concerned, less work has been done towards the development of ML-based intrusion detection…”
Intrusion detection systems for IoT-based smart environments: a survey (2018, background): “In 2000, Axelsson [45] surveyed 20 research projects on IDSs.”
Model-Based Attack Detection and Mitigation for Automatic Generation Control (2014, background): “Intrusion detection systems are traditionally classified into signature-based detection and anomaly-based detection [5].”