Semi-supervised Statistical Approach for Network Anomaly Detection

Semi-supervised Statistical Approach for Network Anomaly Detection

Intrusion Detection Systems (IDS) have become a very important defense measure against security threats. In recent years, computer networks are widely deployed for critical and complex systems, which make them more vulnerable to network attacks. In this paper, we propose a two-stage Semi-supervised Statistical approach for Anomaly Detection (SSAD). The first stage of SSAD aims to build a probab...

Fixed-Background EM Algorithm for Semi-Supervised Anomaly Detection

Aalto University, P.O. Box 11000, FI-00076 Aalto www.aalto.fi Author Tommi Vatanen, Mikael Kuusela, Eric Malmi, Tapani Raiko, Timo Aaltonen and Yoshikazu Nagai Name of the publication Fixed-Background EM Algorithm for Semi-Supervised Anomaly Detection Publisher School of Science Unit Department of Information and Computer Science Series Aalto University publication series SCIENCE + TECHNOLOGY 2...

Statistical Approaches for Network Anomaly Detection

Semi-Supervised Learning -- A Statistical Physics Approach

We present a novel approach to semisupervised learning which is based on statistical physics. Most of the former work in the field of semi-supervised learning classifies the points by minimizing a certain energy function, which corresponds to a minimal k-way cut solution. In contrast to these methods, we estimate the distribution of classifications, instead of the sole minimal k-way cut, which ...

Anomaly Detection in Network Traffic: A Statistical Approach

A global Internet usage enlarge rate of 380% superior than the period from 2000, the year of the dot-com bubble burst, until present select that Internet technology has become a stand of our daily life. In the similar period, cyber-crime has seen an unbelievable to facilitate to create sophisticated protection device for computers and networks a complete necessity. Firewalls as the major protec...