Use of K-Nearest Neighbor classifier for intrusion detection

By Yihua Liao, V. Rao Vemuri. Computers & security, 2002

834 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/e06694149868f95a99d13ea1b49ce16d5b080144

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In Computer Networks and Communications

In Information Systems

In Signal Processing

Citations in the corpus

DDoS attack detection method using cluster analysis (2008, methodology): “Liao and Vemuri (2001) used K-nearest Neighbor Classifier (KNNC) to categorize process into normal or intrusive class.”
Evaluation of machine learning classifiers for mobile malware detection (2014, neutral): “(Liao and Vemuri 2002).”
Application of SVM and ANN for intrusion detection (2005, background): “In a dynamic environment, it is nearly impossible to create user pro6les that determine normal behavior [11].”
Enhanced Network Anomaly Detection Based on Deep Neural Networks (2018, methodology): “Supervised approaches such as k-nearest neighbor (k-NN) [19], neural networks and support vector machine (SVM) [20] have been studied extensively for anomaly detection.”
Detecting and Preventing Cyber Insider Threats: A Survey (2018, methodology): “Subsequently, various classification algorithms such as the k-nearest neighbour (kNN) [65], [66], k-means clustering (kMC) [67] and support vector machine (SVM) [68] can be applied to differentiate an anomalous frequency vector from the known normal ones using a metric of similarity (or distance).”
A Survey on Machine Learning Techniques for Cyber Security in the Last Decade (2020, methodology): “Statistical models, association rules, ANN, and KNN, were used to detect the anomaly-based detection techniques on a host [418]–[420].”
KNN with TF-IDF based Framework for Text Categorization (2014, neutral): “The function of classifier is to merge text documents into one or more predefined categories based on their content [9].”
Learning Novel Policies For Tasks (2019, methodology): “One possible approach would be using nearest-neighbour based approch as in (Zhao & Saligrama, 2009; Liao & Vemuri, 2002) to measure the difference between the newly generated rollout and the rollouts from previous policies and aggregate them to obtain a novelty measurement.”
Deep Learning Approach Combining Sparse Autoencoder With SVM for Network Intrusion Detection (2018, background): “The topology of the used AE was different from the common topology, and the extracted feature by the AE was applied to many traditional machine learning algorithms, such as SVM, K-NN, and Gaussian naïve Bayes.”
An overview of anomaly detection techniques: Existing solutions and latest technological trends (2007, methodology): “Several papers [76,77] have proposed using the k-nearest neighbor outlier detection algorithms for the purpose of anomaly detection.”
Autoencoder-based network anomaly detection (2018, methodology): “In this subsection, we present a general comparison between our two autoencoder based anomaly detection systems and other famous detection systems: k-Nearest Neighbor (k-NN) [11], Support Vector Machine (SVM) [10] and Triangle area”
Robust Anomaly Detection Using Support Vector Machines (2003, methodology): “More importantly, we deliberately form noisy training data and show the robustness of RSVMs.”
Anomaly-based network intrusion detection: Techniques, systems and challenges (2009, methodology): “For example, the KNN (k-nearest neighbour) approach (Liao and Vemuri, 2002) uses the Euclidean distance to define the membership of data points to a given cluster, while other systems use the Mahalanobis distance.”
Intrusion detection by machine learning: A review (2009, background): “…SVM NB MLP DT SOM GA No. of articles 1 6 7 3 2 4 1 2 Chimphlee et al. (2006) Bouzida et al. (2004), Eskin et al. (2002), Li and Guo (2007), Liao and Vemuri (2002), Wang and Stolfo (2004), Wang et al. (2004) Chen et al. (2005), Eskin et al. (2002), Heller et al. (2003), Peddabachigari et…”