Multi-Regional landslide detection using combined unsupervised and supervised machine learning

Intrusion Detection: Supervised Machine Learning

Due to the expansion of high-speed Internet access, the need for secure and reliable networks has become more critical. The sophistication of network attacks, as well as their severity, has also increased recently. As such, more and more organizations are becoming vulnerable to attack. The aim of this research is to classify network attacks using neural networks (NN), which leads to a higher de...

Learning Intrusion Detection: Supervised or Unsupervised?

Application and development of specialized machine learning techniques is gaining increasing attention in the intrusion detection community. A variety of learning techniques proposed for different intrusion detection problems can be roughly classified into two broad categories: supervised (classification) and unsupervised (anomaly detection and clustering). In this contribution we develop an ex...

Novel strategies on semi-supervised and unsupervised machine learning

Combined Supervised and Unsupervised Learning in Genomic Data Mining

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Polymorphic Worms Detection Using A Supervised Machine Learning Technique

Polymorphic worms are considered as the most dangerous threats to the Internet security, and the danger lies in changing their payloads in every infection attempt to avoid the security systems. We have designed a novel doublehoneynet system, which is able to detect new worms that have not been seen before. To generate signatures for polymorphic worms we have two steps. The first step is the pol...