Biblio

The behavior of modern power systems is becoming more stochastic and dynamic, due to the increased penetration of variable generation, demand response, new power market structure, extreme weather conditions, contingencies, and unexpected events. It is critically important to predict potential system operational issues so that grid planners and operators can take preventive actions to mitigate the impact, e.g., lack of operational reserves. In this paper, an innovative software tool is presented to assist power grid operators in a balancing authority in predicting the grid stress level over the next operating day. It periodically collects necessary information from public domain such as weather forecasts, electricity demand, and automatically estimates the stress levels on a daily basis. Advanced Neural Network and regression tree algorithms are developed as the prediction engines to achieve this goal. The tool has been tested on a few key balancing authorities and successfully predicted the growing system peak load and increased stress levels under extreme heat waves.

For location-aware applications in wireless sensor networks (WSNs), it is important to ensure that sensor nodes can get correct locations in a hostile WSNs. Sybil attacks, which are vital threats in WSNs, especially in the distributed WSNs. They can forge one or multiple identities to decrease the localization accuracy, or sometimes to collapse the whole localization systems. In this paper, a novel lightweight sybilfree (SF)-APIT algorithm is presented to solve the problem of sybil attacks in APIT localization scheme, which is a popular range-free method and performs at individual node in a purely distributed fashion. The proposed SF-APIT scheme requires minimal overhead for wireless devices and works well based on the received signal strength. Simulations demonstrate that SF-APIT is an effective scheme in detecting and defending against sybil attacks with a high detection rate in distributed wireless localization schemes.

Side-channel collision attacks have been one of the most powerful attack techniques, combining advantages of traditional side-channel attack and mathematical cryptanalysis. In this paper, we propose a novel multiple-bits side-channel collision attack based on double distance voting detection, which can find all 120 relations among 16 key bytes with only 32 averaged power traces when applied to AES (Advanced Encryption Standard) algorithm. Practical attack experiments are performed successfully on a hardware implementation of AES on FPGA board. Results show that the necessary number of traces for our method is about 50% less than correlation-enhanced collision attack and 76% less than binary voting test with 90% success rate.