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Cheng, Z., Beshley, M., Beshley, H., Kochan, O., Urikova, O..  2020.  Development of Deep Packet Inspection System for Network Traffic Analysis and Intrusion Detection. 2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET). :877–881.
One of the most important issues in the development of the Internet of Things (IoT) is network security. The deep packet inspection (DPI) is a promising technology that helps to detection and protection against network attacks. The DPI software system for IoT is developed in this paper. The system for monitoring and analyzing IoT traffic to detect anomalies and identify attacks based on Hurst parameter is proposed. This system makes it possible to determine the Hurst flow parameter at different intervals of observation. This system can be installed on a network provider to use more effectively the bandwidth.
Cheng, Z., Chow, M.-Y..  2020.  An Augmented Bayesian Reputation Metric for Trustworthiness Evaluation in Consensus-based Distributed Microgrid Energy Management Systems with Energy Storage. 2020 2nd IEEE International Conference on Industrial Electronics for Sustainable Energy Systems (IESES). 1:215–220.
Consensus-based distributed microgrid energy management system is one of the most used distributed control strategies in the microgrid area. To improve its cybersecurity, the system needs to evaluate the trustworthiness of the participating agents in addition to the conventional cryptography efforts. This paper proposes a novel augmented reputation metric to evaluate the agents' trustworthiness in a distributed fashion. The proposed metric adopts a novel augmentation method to substantially improve the trust evaluation and attack detection performance under three typical difficult-to-detect attack patterns. The proposed metric is implemented and validated on a real-time HIL microgrid testbed.