Adaptive Intrusion Detection: A Data Mining Approach

By Wenke Lee, Salvatore J. Stolfo, Kui W. Mok. Artificial Intelligence Review, 2000

436 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/2a5792e017a225e767c54960c027148d4d057e2f

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Citations in the corpus

Efficient Feature Selection via Analysis of Relevance and Redundancy (2004, background): “…applications like text categorization (Yang and Pederson, 1997; Forman, 2003), image retrieval (Swets and Weng, 1995; Dy et al., 2003), genomic microarray analysis (Xing et al., 2001; Yu and Liu, 2004), customer relationship management (Ng and Liu, 2000), and intrusion detection (Lee et al., 2000).”
Anomaly detection: A survey (2009, background): “2003], system call intrusion detection [Lee et al. 2000; Lee and Stolfo 1998; Qin and Hwang 2004], credit card fraud detection [Brause et al.”
Toward integrating feature selection algorithms for classification and clustering (2005, background): “…[40], [63], [81], [90], machine learning [6], [41], [43], [44], and data mining [17], [18], [42], and widely applied to many fields such as text categorization [50], [70], [94], image retrieval [77], [86], customer relationship management [69], intrusion detection [49], and genomic analysis [91].”
Network Anomaly Detection: Methods, Systems and Tools (2014, background): “Hence, the extensive amount of activities that may be needed to detect intrusion near real time in an online NIDS may be obviated, achieving efficiency [112].”
An Overview of Concept Drift Applications (2016, background): “Anomaly detection is often tackled as one class classiﬁ-cation due to changes in behavior, characteristics of legitimate users, or new creative particular, network intrusion detection [50].”
An overview of anomaly detection techniques: Existing solutions and latest technological trends (2007, methodology): “Data Mining based Anomaly Detection To eliminate the manual and ad-hoc elements from the process of building an intrusion detection system, researchers are increasingly looking at using data mining techniques for anomaly detection [45-47].”
Systematic Poisoning Attacks on and Defenses for Machine Learning in Healthcare (2015, background): “These applications include healthcare [1], network intrusion detection systems[2], spam and fraud detection, phishing detection [3], political decision making [4], adversarial advertisement detection [5], and financial engineering [6].”
A review of feature selection methods on synthetic data (2013, neutral): “This technique has found success in many different real-world applications like DNA microarray analysis [7], intrusion detection [8,9], text categorization [10,11] or information retrieval [12], including image retrieval [13] or music information retrieval [14].”
Random-Forests-Based Network Intrusion Detection Systems (2008, methodology): “Many data mining approaches have been proposed for intrusion detection based on association rules [8], [19], [20], [ 39 ].”