Adaptive protocols for information dissemination in wireless sensor networks
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Mimicry attacks on host-based intrusion detection systems
Proceedings of the 9th ACM conference on Computer and communications security
Medians and beyond: new aggregation techniques for sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Faster In-Network Evaluation of Spatial Aggregationin Sensor Networks
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Sparse data aggregation in sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
Virtual address space mapping for IP auto-configuration in MANET with security capability
ICAIT '08 Proceedings of the 2008 International Conference on Advanced Infocomm Technology
EWSN'07 Proceedings of the 4th European conference on Wireless sensor networks
A dynamic en-route filtering scheme for data reporting in wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
A survey of security issues in mobile ad hoc and sensor networks
IEEE Communications Surveys & Tutorials
Security in mobile ad hoc networks: challenges and solutions
IEEE Wireless Communications
Routing security in wireless ad hoc networks
IEEE Communications Magazine
Statistical en-route filtering of injected false data in sensor networks
IEEE Journal on Selected Areas in Communications
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A Mobile Ad Hoc Network MANET has been used in both commercial and tactical applications and faces security risks. Conducting cyber-attack monitoring and detection in MANET is challenging owning to limited resources. To deal with this issue, in this paper we develop lossless and lossy aggregation techniques to reduce the resources e.g., energy consumption and bandwidth use for transmitting security information through MANET while preserving the desired detection accuracy for cyber security operation. Particularly, our novel compression-based data aggregation technique effectively removes the duplicated information and compresses the pivotal data. The event-based data aggregation consolidates the data with the same type of predefined events. We also develop lossy data aggregation technique, namely feature-based data aggregation, which defines a series of basic features. We conduct real-world experiments and simulations to evaluate the effectiveness of our proposed data aggregation techniques in terms of energy consumption and detection accuracy.