Adaptive Firefly Algorithm (AFA) based Feature Selection and Unsupervised Fuzzy Extreme Learning Machine (USUFELM) with Network-based Intrusion Detection and Prevention System
While Internet and network technology have been growing rapidly, cyber attack incidents also increase accordingly.
The increasing occurrence of network attacks is an important problem to network services. In the recent work
present a network based Intrusion Detection and Prevention System DPS, which can efficiently detect many wellknown
attack types and can immediately, prevent the network system from Worm attacks. But still many of the
network attacks and security threats have been previously reported. Damages caused by network attacks and
malware tend to be high. So how to detect different types of network attack becomes very difficult task. However
the selection of optimal features for different attacks becomes very difficult task. Some different kinds of intelligent
techniques are appropriate for selection of features from network IDPS. In this work we introduce an Adaptive
Firefly Algorithm (AFA) for feature selection and classification for intrusion detection in networks based on
Unsupervised Fuzzy Extreme Learning Machine (USUFELM) classifier. This proposed approach is simple and
efficient and can be used with USUFELM actually implement to IDPS for different attacks. The experimental results
show that our IDPS can distinguish normal network activities from main attack types (Probe and Denial of Service)
with high accuracy of detection rate in a few seconds and automatically prevent the victim's computer network from
the attacks. Surprisingly, the USUFELM be able to work very well, when experiencing with untrained or unknown
network attack types.