Biblio

Security and consistency of smart grids is one of the main issues in the design and maintenance of highly controlled and monitored new power grids. Bad data injection attack could lead to disasters such as power system outage, or huge economical losses. In many attack scenarios, the attacker can come up with new attack strategies that couldn't be detected by the traditional bad data detection methods. Adaptive Partitioning State Estimation (APSE) method [3] has been proposed recently to combat such attacks. In this work, we evaluate and compare with a traditional method. The main idea of APSE is to increase the sensitivity of the chi-square test by partitioning the large grids into small ones and apply the test on each partition individually and repeat this procedure until the faulty node is located. Our simulation findings using MATPOWER program show that the method is not consistent where it is sensitive the systems size and the location of faulty nodes as well.

Wireless sensor network operate on the basic underlying assumption that all participating nodes fully collaborate in self-organizing functions. However, performing network functions consumes energy and other resources. Therefore, some network nodes may decide against cooperating with others. Node misbehavior due to selfish or malicious reasons or faulty nodes can significantly degrade the performance of mobile ad-hoc networks. To cope with misbehavior in such self-organized networks, nodes need to be able to automatically adapt their strategy to changing levels of cooperation. The problem of identifying and isolating misbehaving nodes that refuses to forward packets in multi-hop ad hoc networks. a comprehensive system called Audit-based Misbehavior Detection (AMD) that effectively and efficiently isolates both continuous and selective packet droppers. The AMD system integrates reputation management, trustworthy route discovery, and identification of misbehaving nodes based on behavioral audits. AMD evaluates node behavior on a per-packet basis, without employing energy-expensive overhearing techniques or intensive acknowledgment schemes. AMD can detect selective dropping attacks even if end-to-end traffic is encrypted and can be applied to multi-channel networks.