Intrusion Detection in Unlabeled Data with Quarter-sphere Support Vector Machines

By Pavel Laskov, Christin Schäfer, Igor Kotenko, K. Müller. 2004

132 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, 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 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…”
Quarter Sphere Based Distributed Anomaly Detection in Wireless Sensor Networks (2007, methodology): “In this paper, we have proposed a distributed anomaly detection approach with low communication overhead, based on a one-class quarter sphere SVM, for wireless sensor networks .”
Enhancing one-class support vector machines for unsupervised anomaly detection (2013, background): “Quarter-sphere support vector machines [16] were designed to handle intrusion detection data which have one-sided features centered around the origin.”
Big data classification: problems and challenges in network intrusion prediction with machine learning (2014, background): “Many supervised learning algorithms algorithms have been developed for the classification of network intrusion traffic [8, 9, 6, 4, 5] among them the Support Vector Machine (SVM) received a greater attention.”
High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning (2016, methodology): “The QSSVM finds a minimal radius hypersphere centred at the origin that encapsulates a majority of the image vectors in the feature space [32].”
Online Anomaly Detection under Adversarial Impact (2010, methodology): “Although extremely simple, this method is quite popular in computer security, in particular in anomaly-based intrusion detection (Hofmeyr et al., 1998; Laskov et al., 2004; Rieck and Laskov, 2007), due to its low computational burden1.”
A Unifying Review of Deep and Shallow Anomaly Detection (2020, background): “, in fraud and cybersecurity scenarios [204], [347], [430]–[433]).”
Detecting Unknown Network Attacks Using Language Models (2006, methodology): “The algorithms explore local properties of the provided data as in the case of single-linkage clustering (32) and our k-nearest neighbor method Zeta, or analyze global properties as the simplified Mahalanobis distance (10) and the quarter-sphere SVM (34).”
Data preprocessing for anomaly based network intrusion detection: A review (2011, methodology): “(Laskov et al., 2004) Data cleaning Quarter Sphere SVM KDD Cup: Probe, DoS, R2L, U2R”
Language models for detection of unknown attacks in network traffic (2007, methodology): “The quarter-sphere SVM [42] is a kernel-based learning method that determines the center of mass of input data μφ in a feature space using a non-linear mapping function φ.”