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Scaling IDS construction based on Non-negative Matrix factorization using GPU computing

Attacks on the computer infrastructures are becoming an increasingly serious problem. Whether it is banking, e-commerce businesses, health care, law enforcement, air transportation, or education, we are all becoming increasingly reliant upon the networked computers. The possibilities and opportunities are limitless; unfortunately, so too are the risks and chances of malicious intrusions. Intrusion detection is required as an additional wall for protecting systems despite of prevention techniques and is useful not only in detecting successful intrusions, but also in monitoring attempts to security, which provides important information for timely countermeasures. This paper presents some improvements to some of our previous approaches using a Non-negative Matrix factorization approach. To improve the performance (detection accuracy) and computational speed (scaling) a GPU implementation is detailed. Empirical results indicate that the speedup was up to 500x for the training phase and up to 190x for the testing phase.