Adaptive and Online Network Intrusion Detection System Using Clustering and Extreme Learning Machines

Despite the large volume of research conducted in the field of intrusion detection, finding a perfect solution of intrusion detection systems for critical applications is still a major challenge. This is mainly due to the continuous emergence of security threats which can bypass the outdated intrusion detection systems. The main objective of this paper is to propose an adaptive design of intrusion detection systems on the basis of Extreme Learning Machines. The proposed system offers the capability of detecting known and novel attacks and being updated according to new trends of data patterns provided by security experts in a cost-effective manner.

Setareh Roshan (F-Secure, Aalto University) Aapo Kalliola, Yoan Miche (Nokia Bell Labs, Aalto University) Ian Oliver, Silke Holtmanns (Nokia Bell Labs), Amaury Lendasse (University of Iowa), Anton Akusok (Arcada University of Applied Sciences): Adaptive and online network intrusion detection system using clustering and Extreme Learning Machines

Presented at the International Symposium on Extreme Learning Machines (ELM), Singapore, 2016

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