C4.5: programs for machine learning
C4.5: programs for machine learning
TCP/IP illustrated (vol. 1): the protocols
TCP/IP illustrated (vol. 1): the protocols
Machine Learning
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Computer networks (3rd ed.)
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Internetworking with TCP/IP: Principles, Protocols, and Architecture
Internetworking with TCP/IP: Principles, Protocols, and Architecture
Computer Intrusion Detection and Network Monitoring: A Statistical Viewpoint
Computer Intrusion Detection and Network Monitoring: A Statistical Viewpoint
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
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One of the main weaknesses of signature-based intrusion-detection systems (IDSs) is their inability to detect new attacks or new versions of already known attack patterns. This topic has attracted the attention of many researchers over the past decade and has resulted, amongst other alternatives, in anomaly-based IDSs, which use statistical and/or data-mining techniques as a new approach to intrusion detection. Herein we present a comparison of classifiers known as Decision Trees and SVM machines, both of which use data-mining techniques, with and without applying attribute selection techniques, with the KDDCUP'99 data set and the Weka tool.