Biblio

Over the years, technology has reformed the perception of the world related to security concerns. To tackle security problems, we proposed a system capable of detecting security alerts. System encompass audio events that occur as an outlier against background of unusual activity. This ambiguous behaviour can be handled by auditory classification. In this paper, we have discussed two techniques of extracting features from sound data including: time-based and signal based features. In first technique, we preserve time-series nature of sound, while in other signal characteristics are focused. Convolution neural network is applied for categorization of sound. Major aim of research is security challenges, so we have generated data related to surveillance in addition to available datasets such as UrbanSound 8k and ESC-50 datasets. We have achieved 94.6% accuracy for proposed methodology based on self-generated dataset. Improved accuracy on locally prepared dataset demonstrates novelty in research.

In view of the problem that the intrusion detection method based on One-Class Support Vector Machine (OCSVM) could not detect the outliers within the industrial data, which results in the decision function deviating from the training sample, an anomaly intrusion detection algorithm based on Robust Incremental Principal Component Analysis (RIPCA) -OCSVM is proposed in this paper. The method uses RIPCA algorithm to remove outliers in industrial data sets and realize dimensionality reduction. In combination with the advantages of OCSVM on the single classification problem, an anomaly detection model is established, and the Improved Particle Swarm Optimization (IPSO) is used for model parameter optimization. The simulation results show that the method can efficiently and accurately identify attacks or abnormal behaviors while meeting the real-time requirements of the industrial control system (ICS).

A low latency is a fundamental timeliness requirement to reduce the potential risks of cyber sickness and to increase effectiveness, efficiency, and user experience of Virtual Reality Systems. The effects of uniform latency degradation based on mean or worst-case values are well researched. In contrast, the effects of latency jitter, the distribution pattern of latency changes over time has largely been ignored so far although today's consumer VR systems are extremely vulnerable in this respect. We investigate the applicability of the Walsh, generalized ESD, and the modified z-score test for the detection of outliers as one central latency distribution aspect. The tests are applied to well defined test cases mimicking typical timing behavior expected from concurrent architectures of today. We introduce accompanying graphical visualization methods to inspect, analyze and communicate the latency behavior of VR systems beyond simple mean or worst-case values. As a result, we propose a stacked modified z-score test for more detailed analysis.