A new intrusion detection system using support vector machines and hierarchical clustering

By Latifur R. Khan, Mamoun Adel Awad, Bhavani M. Thuraisingham. 2007

462 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Networks and Communications

Citations in the corpus

Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers (2013, methodology): “In fact, SVMs are used for intrusion detection and anomaly detection ([39, 34, 22]) or as part of complex systems for similar tasks ([48, 6]).”
A Survey on Machine Learning Techniques for Cyber Security in the Last Decade (2020, methodology): “Various machine learning techniques were applied for misuse-based detection [127], [384]–[388], anomaly-based detection [389]–[392] and hybrid-based detection [393]–[397].”
A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection (2019, methodology): “[126] proposed the hybrid detection approach”
Network Anomaly Detection: Methods, Systems and Tools (2014, methodology): “[106] reduce the training time for SVMs when classifying large intrusion datasets by using a hierarchical clustering method called Dynamically Growing Self-Organizing Tree (DGSOT) intertwined with the SVMs.”
Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints (2011, unknown): “Ç”
CANN: An intrusion detection system based on combining cluster centers and nearest neighbors (2015, methodology): “[16] SOM + SVM DARPA 1998 Anomaly detection FP, FN, accuracy SVM No Li and Guo [18] TCM k-NN KDD-Cup 99 Anomaly detection TP, FP SVM, ANN, k-NN No”
A novel hybrid KPCA and SVM with GA model for intrusion detection (2014, background): “028 [5], K-nearest neighbor [6], rough set theory [7] and support vector machine (SVM) [8].”
Intrusion detection model using fusion of chi-square feature selection and multi class SVM (2016, methodology): “…and several machine learning techniques like decision tree (Lee et al., 2008), genetic algorithm (Shafi and Abbass, 2009), Support vector machines (Khan et al., 2007), Artificial Neural Network (Wang et al., 2010) and hybrid intelligent system (Peddabachigari et al., 2007) are explored to build…”