Parzen-window network intrusion detectors

By Dit-Yan Yeung, Calvin C. T. Chow. 2002

301 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

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In Artificial Intelligence

In Computer Networks and Communications

Citations in the corpus

Security analysis of online centroid anomaly detection (2010, methodology): “It has been successfully used in a variety of anomaly detection applications such as intrusion detection (Hofmeyr et al., 1998; Yeung and Chow, 2002; Laskov et al., 2004a; Wang and Stolfo, 2004; Rieck and Laskov, 2006; Wang et al., 2006; Rieck and Laskov, 2007), wireless sensor networks…”
Unsupervised learning techniques for an intrusion detection system (2004, methodology): “such as the Parzen Window method presented in [33].”
Novelty detection: a review - part 1: statistical approaches (2003, methodology): “, 1999; Yeung and Chow, 2002).”
A Classification Framework for Anomaly Detection (2005, background): “anomalous jet engine vibrations [18, 12, 13], abnormalities in medical data [27, 2], unexpected conditions in engineering [6] and network intrusions [14, 30, 9].”
Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints (2011, methodology): “several non-parametric approaches available, such as parzen window method [ 23 ] and K-nearest”
Can machine learning be secure? (2006, background): “Because this approach often fails to detect novel intrusions, a variety of researchers have proposed incorporating machine learning techniques into intrusion detection systems [1, 16, 18, 24, 38, 41].”
Outlier detection by active learning (2006, methodology): “The outlier detection method we compare against is based on the so-called Parzen Window method, and is documented in [17].”
A review of novelty detection (2014, methodology): “This method has been used for novelty detection in applications such as network intrusion detection [97], oil flow data [21], and for mammographic image analysis [1].”
Application of Machine Learning Algorithms to KDD Intrusion Detection Dataset within Misuse Detection Context (2003, unknown): “Yeung and Chow [7] proposed a novelty detection approach using non-parametric density estimation based on Parzen-window estimators with Gaussian kernels to”
Asymptotic normality of plug-in level set estimates. (2009, background): “Further areas of anomaly detection include intrusion detection [e.g., Fan et al. (2001) and Yeung and Chow (2002)], anomalous jet engine vibrations [e.g., Nairac et al. (1997), Desforges, Jacob and Cooper (1998) and King et al. (2002)] or medical imaging [e.g., Gerig, Jomier and Chakos (2001) and…”
Loda: Lightweight on-line detector of anomalies (2016, methodology): “Due to Loda's similarity to the Parzen window detector (Yeung and Chow 2002), the similar argumentation might explain Loda's surprisingly good performance.”
A Survey of Stealth Malware Attacks, Mitigation Measures, and Steps Toward Autonomous Open World Solutions (2016, methodology): “Chow et al. [90] demonstrated that structure data in kernel memory can survive up to 14 days after de-allocation, provided that the machine has not been rebooted.”
Data preprocessing for anomaly based network intrusion detection: A review (2011, methodology): “Yeung and Chow (2002) use a machine learning algorithm designed to work with numeric data only, so they use a coding scheme to convert the 7 symbolic features from the KDD Cup 99 dataset into numeric features.”
Semi-supervised detection of collective anomalies with an application in high energy particle physics (2012, background): “…models, high energy physics I. INTRODUCTION Anomaly detection (outlier detection, novelty detection) refers to the problem of detecting patterns in the data that deviate from the expected, normal behavior so much that they arouse suspicion of having been generated by a different mechanism [1]–[4].”
Deep Learning Approach for Intelligent Intrusion Detection System (2019, methodology): “In [28], a non-parametric density estimation method based on Parzen-window estimators was studied with Gaussian kernels and Normal distribution.”
Learning to Classify With Incremental New Class (2021, unknown): “While these methods ignore an important issue of Fig.”