Biblio

The modular multilevel converter with series and parallel connectivity was shown to provide advantages in several industrial applications. Its reliability largely depends on the absence of failures in the power semiconductors. We propose and analyze a fault-diagnosis technique to identify shorted switches based on features generated through wavelet transform of the converter output and subsequent classification in support vector machines. The multi-class support vector machine is trained with multiple recordings of the output of each fault condition as well as the converter under normal operation. Simulation results reveal that the proposed method has high classification latency and high robustness. Except for the monitoring of the output, which is required for the converter control in any case, this method does not require additional module sensors.

Reduction of Quality (RoQ) attack is a stealthy denial of service attack. It can decrease or inhibit normal TCP flows in network. Victims are hard to perceive it as the final network throughput is decreasing instead of increasing during the attack. Therefore, the attack is strongly hidden and it is difficult to be detected by existing detection systems. Based on the principle of Time-Frequency analysis, we propose a two-stage detection algorithm which combines anomaly detection with misuse detection. In the first stage, we try to detect the potential anomaly by analyzing network traffic through Wavelet multiresolution analysis method. According to different time-domain characteristics, we locate the abrupt change points. In the second stage, we further analyze the local traffic around the abrupt change point. We extract the potential attack characteristics by autocorrelation analysis. By the two-stage detection, we can ultimately confirm whether the network is affected by the attack. Results of simulations and real network experiments demonstrate that our algorithm can detect RoQ attacks, with high accuracy and high efficiency.