C4.5: programs for machine learning
C4.5: programs for machine learning
Temporal sequence learning and data reduction for anomaly detection
ACM Transactions on Information and System Security (TISSEC)
Authenticated ad hoc routing at the link layer for mobile systems
Wireless Networks
SPINS: security protocols for sensor networks
Proceedings of the 7th annual international conference on Mobile computing and networking
The quest for security in mobile ad hoc networks
MobiHoc '01 Proceedings of the 2nd ACM international symposium on Mobile ad hoc networking & computing
Ariadne: a secure on-demand routing protocol for ad hoc networks
Proceedings of the 8th annual international conference on Mobile computing and networking
Ad-hoc On-Demand Distance Vector Routing
WMCSA '99 Proceedings of the Second IEEE Workshop on Mobile Computer Systems and Applications
A study in using neural networks for anomaly and misuse detection
SSYM'99 Proceedings of the 8th conference on USENIX Security Symposium - Volume 8
Nodes bearing grudges: towards routing security, fairness, and robustness in mobile ad hoc networks
EUROMICRO-PDP'02 Proceedings of the 10th Euromicro conference on Parallel, distributed and network-based processing
Alert aggregation in mobile ad hoc networks
WiSe '03 Proceedings of the 2nd ACM workshop on Wireless security
A cooperative intrusion detection system for ad hoc networks
Proceedings of the 1st ACM workshop on Security of ad hoc and sensor networks
Mobility-based anomaly detection in cellular mobile networks
Proceedings of the 3rd ACM workshop on Wireless security
Agent-Based Cooperative Anomaly Detection for Wireless Ad Hoc Networks
ICPADS '06 Proceedings of the 12th International Conference on Parallel and Distributed Systems - Volume 1
A Bayesian game approach for intrusion detection in wireless ad hoc networks
GameNets '06 Proceeding from the 2006 workshop on Game theory for communications and networks
On the accuracy of decentralized virtual coordinate systems in adversarial networks
Proceedings of the 14th ACM conference on Computer and communications security
A hybrid data mining anomaly detection technique in ad hoc networks
International Journal of Wireless and Mobile Computing
Modelling misbehaviour in ad hoc networks: a game theoretic approach for intrusion detection
International Journal of Security and Networks
IEEE/ACM Transactions on Networking (TON)
Information fusion for computer security: State of the art and open issues
Information Fusion
A game theoretical framework on intrusion detection in heterogeneous networks
IEEE Transactions on Information Forensics and Security
Lightweight and distributed attack detection scheme in mobile ad hoc networks
Proceedings of the 7th International Conference on Advances in Mobile Computing and Multimedia
Evolutionary computation techniques for intrusion detection in mobile ad hoc networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
On the anomaly intrusion-detection in mobile ad hoc network environments
PWC'06 Proceedings of the 11th IFIP TC6 international conference on Personal Wireless Communications
FRaC: a feature-modeling approach for semi-supervised and unsupervised anomaly detection
Data Mining and Knowledge Discovery
A cross-layer game for energy-efficient jamming detection in ad hoc networks
Security and Communication Networks
Distributed anomaly detection for industrial wireless sensor networks based on fuzzy data modelling
Journal of Parallel and Distributed Computing
Performance analysis of machine learning algorithms for intrusion detection in MANETs
International Journal of Wireless and Mobile Computing
International Journal of Ad Hoc and Ubiquitous Computing
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With the proliferation of wireless devices, mobile ad-hocnetworking (MANET) has become a very exciting and importanttechnology. However, MANET is more vulnerablethan wired networking. Existing security mechanisms designedfor wired networks have to be redesigned in this newenvironment. In this paper, we discuss the problem of intrusiondetection in MANET. The focus of our research is ontechniques for automatically constructing anomaly detectionmodels that are capable of detecting new (or unseen)attacks. We introduce a new data mining method that performs"cross-feature analysis" to capture the inter-featurecorrelation patterns in normal traffic. These patterns can beused as normal profiles to detect deviation (or anomalies)caused by attacks. We have implemented our method on afew well known ad-hoc routing protocols, namely, DynamicSource Routing (DSR) and Ad-hoc On-Demand DistanceVector (AODV), and have conducted extensive experimentson the ns-2 simulator. The results show that the anomalydetection models automatically computed using our datamining method can effectively detect anomalies caused bytypical routing intrusions.