Paper Info
Title
Anomaly-Based Behavior Analysis of Wireless Network Security
Abstract
The exponential growth in wireless network faults, vulnerabilities, and attacks make the Wireless Local Area Network (WLAN) security management a challenging research area. Newer network cards implemented more security measures according to the IEEE recommendations [14]; but the wireless network is still vulnerable to Denial of Service attacks or to other traditional attacks due to existing wide deployment of network cards with well-known security vulnerabilities. The effectiveness of a Wireless Intrusion Detection System (WIDS) relies on updating its security rules; many current WIDSs use static security rule settings based on expert knowledge. However, updating those security rules can be time-consuming and expensive. In this paper, we present a novel approach based on multi-channel monitoring and anomaly analysis of station localization, packet analysis, and state tracking to detect wireless attacks; we use adaptive machine learning and genetic search to dynamically set optimal anomaly thresholds and select the proper set of features necessary to efficiently detect network attacks. We present a self-protection system that has the following salient features: monitor the wireless network, generate network features, track wireless network state machine violations, generate wireless flow keys (WFK), and use the dynamically updated anomaly and misuse rules to detect complex known and unknown wireless attacks. To quantify the attack impact, we use the abnormality distance from the trained norm and multivariate analysis to correlate multiple selected features contributing to the final decision. We validate our Wireless Self Protection System (WSPS) approach by experimenting with more than 20 different types of wireless attacks.
Year
DOI
Venue
2007
10.1109/MOBIQ.2007.4451054
MobiQuitous
Keywords
Field
DocType
network feature,wireless attack,wireless network security,anomaly-based behavior analysis,wireless network,newer network card,security rule,wireless flow key,network card,unknown wireless attack,wireless network fault,network attack,genetics,false positive rate,data mining,exponential growth,multivariate analysis,genetic algorithms,state machine,learning artificial intelligence,denial of service attack,it security,security management,machine learning,wireless local area network,intrusion detection system,behavior analysis
Service set,Wireless network,Key distribution in wireless sensor networks,Wireless intrusion prevention system,Computer science,Wi-Fi Protected Setup,Computer security,Network security,Computer network,Wi-Fi,Network Access Control
Conference
Citations 
PageRank 
References 
8
0.71
7
Authors
3
Name
Order
Citations
PageRank
Samer Fayssal1313.45
Salim Hariri22593184.23
Youssif Alnashif3887.17