Network intrusion and fault detection: a statistical anomaly approach
Year: 2002
Type: article
Abstract: With the advent and explosive growth of the global Internet and electronic commerce environments, adaptive/automatic network/service intrusion and anomaly detection in wide area data networks and e-commerce infrastructures is fast gaining critical research and practical importance. We present and demonstrate the use of a general-purpose hierarchical multitier multiwindow statistical anomaly detection technology and system that operates automatically, adaptively, and proactively, and can be applied to various networking technologies, including both wired and wireless ad hoc networks. Our method uses statistical models and multivariate classifiers to detect anomalous network conditions. Some numerical results are also presented that demonstrate that our proposed methodology can reliably detect attacks with traffic anomaly intensity as low as 3-5 percent of the typical background traffic intensity, thus promising to generate an effective early warning.
Source: IEEE Communications Magazine
Authors: C. Manikopoulos, S. Papavassiliou
Institution: New Jersey Institute of Technology
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1964793108