A Comparative Study of Anomaly Detection Schemes in Network Intrusion Detection

By Aleksandar Lazarevic, Levent Ertöz, Vipin Kumar, Aysel Ozgur, Jaideep Srivastava. 2003

1,019 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

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Citations in the corpus

Security analysis of online centroid anomaly detection (2010, methodology): “It has been successfully used in several well-known intrusion detection systems (e.g., Hofmeyr et al., 1998; Lazarevic et al., 2003; Wang and Stolfo, 2004; Laskov et al., 2004b; Wang et al., 2005, 2006; Rieck and Laskov, 2007).”
Outlier Analysis (2013, background): “A comparative study of network intrusion detection schemes may be found in [290].”
Machine Learning in the Presence of an Adversary: Attacking and Defending the SpamBayes Spam Filter (2008, background): “Often, security sensitive tasks, such as network intrusion/extrusion detection, financial fraud detection, virus and worm detection, and spam filtering use statistical machine learning algorithms to improve data classification [5, 12, 13, 17, 18, 23].”
Anomaly detection: A survey (2009, background): “2002] and system call intrusion detection [Eskin et al. 2002; Heller et al. 2003; Lazarevic et al. 2003].”
Outlier Detection Techniques for Wireless Sensor Networks: A Survey (2008, methodology): “ROC curves for different detection techniques [44].”
k-means-: A Unified Approach to Clustering and Outlier Detection (2013, neutral): “Furthermore we extend our approach to all distance measures that can be expressed in the form of a Bregman divergence.”
Data preprocessing for anomaly based network intrusion detection: A review (2011, methodology): “The LOF algorithm has been compared with Nearest Neighbor and SVM approaches for un-supervised network anomaly detection (Lazarevic et al., 2003).”
A Survey of Stealth Malware Attacks, Mitigation Measures, and Steps Toward Autonomous Open World Solutions (2016, background): “Many approaches in the intrusion detection literature simply pass raw log data or aggregated log data directly to a decision machine [80], [81], [82], [83], [84], [85], [86].”
A tutorial overview of anomaly detection in hyperspectral images (2010, methodology): “It should also be mentioned that anomaly detection techniques are commonly use d in fields other than hyperspect ral images analy sis, such as network and data security [19 , 43 ], biomedical imaging [30, 76, 14], mechanical engineering [28], and data mining and analysis [71].”
Anomaly Detection for Discrete Sequences: A Survey (2012, background): “There is extensive work on anomaly detection techniques [1], [2], [3] that look for individual objects that are different from normal objects.”
Identifying Multi-instance Outliers (2010, positive): “Outlier detection is a hot research topic and has been successfully applied in many areas such as intrusion detection and interesting event discovery [5, 16, 20].”
ADBench: Anomaly Detection Benchmark (2022, background): “Anomaly detection (AD), which is also known as outlier detection, is a key machine learning (ML) task with numerous applications, including anti-money laundering [94], rare disease detection [196], social media analysis [186, 193], and intrusion detection [88].”
Feature deduction and ensemble design of intrusion detection systems (2005, methodology): “Lazarevic et al. (2003) have applied it to anomaly detection.”
A Survey of Privacy-Preserving Methods Across Vertically Partitioned Data (2008, neutral): “Outlier detection has been used to find uncommon sequences in gene data, to find fradulent transactions in credit card records, fraud discovery in mobile phones, to find intrusions from network traffic data[3, 19], etc.”
Combating imbalance in network intrusion datasets (2006, background): “While the total number of packets studied will be significantly higher than that of the destinations set and the packet dataset should contain less noisy examples, accurate classifier construction may be even more problematic as the the imbalance ratio of this dataset is even less favorable than…”
Traffic Anomaly Detection Using K-Means Clustering (2007, methodology): “Later, they focused on outlier detection in order to detect anomalies based on timebased and connection-based feature sets [6], [14].”