In a power network, it is important to detect a cyber attack. In this paper, we propose a method for detecting false data injection (FDI) attacks in distributed state estimation. An FDI attack is well known as one of the typical cyber attacks in a power network. As a method of FDI attack detection, we consider calculating the residual (i.e., the difference between the observed and estimated values). In the proposed detection method, the tentative residual (estimated error) in ADMM (Alternating Direction Method of Multipliers), which is one of the powerful methods in distributed optimization, is applied. First, the effect of an FDI attack is analyzed. Next, based on the analysis result, a detection parameter is introduced based on the residual. A detection method using this parameter is then proposed. Finally, the proposed method is demonstrated through a numerical example on the IEEE 14-bus system.

In wireless sensor networks, adversaries can easily launch application layer attacks, such as false data injection attacks and false vote insertion attacks. False data injection attacks may drain energy resources and waste real world response efforts. False vote insertion attacks would prevent reporting of important information on the field. In order to minimize the damage from such attacks, several prevention based solutions have been proposed by researchers, but may be inefficient in normal condition due to their overhead. Thus, they should be activated upon detection of such attacks. Existing detection based solutions, however, does not address application layer attacks. This paper presents a scheme to adaptively counter false data injection attacks and false vote insertion attacks in sensor networks. The proposed scheme consists of two sub-units: one used to detect the security attacks and the other used to select efficient countermeasures against the attacks. Countermeasures are activated upon detection of the security attacks, with the consideration of the current network status and the attacks. Such adaptive countering approach can conserve energy resources especially in normal condition and provide reliability against false vote insertion attacks.

This paper presents a fuzzy-based path selection method for improving the security level, in which each cluster chooses paths based on the detection power of false data and energy efficiency.