Biblio

Train operation is highly influenced by the rail track state and the surrounding environment. An abnormal obstacle on the rail track will pose a severe threat to the safe operation of urban rail transit. The existing general obstacle detection approaches do not consider the specific urban rail environment and requirements. In this paper, we propose an edge intelligence (EI)-based obstacle intrusion detection system to detect accurate obstacle intrusion in real-time. A two-stage lightweight deep learning model is designed to detect obstacle intrusion and obtain the distance from the train to the obstacle. Edge computing (EC) and 5G are used to conduct the detection model and improve the real-time detection performance. A multi-agent reinforcement learning-based offloading and service migration model is formulated to optimize the edge computing resource. Experimental results show that the two-stage intrusion detection model with the reinforcement learning (RL)-based edge resource optimization model can achieve higher detection accuracy and real-time performance compared to traditional methods.

Recent concepts in defense herald an increasing degree of automation of future military systems, with an emphasis on accelerating sensing-to-decision loops at the tactical edge, reducing their network communication footprint, and improving the inference quality of intelligent components in the loop. These requirements pose resource management challenges, calling for operating-system-like constructs that optimize the use of limited computational resources at the tactical edge. This paper describes these challenges and presents IoBT-OS, an operating system for the Internet of Battlefield Things that aims to optimize decision latency, improve decision accuracy, and reduce corresponding resource demands on computational and network components. A simple case-study with initial evaluation results is shown from a target tracking application scenario.

Recent trends in the convergence of edge computing and artificial intelligence (AI) have led to a new paradigm of “edge intelligence”, which are more vulnerable to attack such as data and model poisoning and evasion of attacks. This paper proposes a white-box poisoning attack against online regression model for edge intelligence environment, which aim to prepare the protection methods in the future. Firstly, the new method selects data points from original stream with maximum loss by two selection strategies; Secondly, it pollutes these points with gradient ascent strategy. At last, it injects polluted points into original stream being sent to target model to complete the attack process. We extensively evaluate our proposed attack on open dataset, the results of which demonstrate the effectiveness of the novel attack method and the real implications of poisoning attack in a case study electric energy prediction application.

Multi-access edge computing (MEC) equipped with artificial intelligence is a promising technology in B5G wireless systems. Due to outsourcing and other transactions, some primitive security modules need to be introduced. In this paper, we design a primitive cipher based on double discrete exponentiation and double discrete logarithm. The machine learning methodology is incorporated in the development. Several interesting results are obtained. It reveals that the number of key-rounds is critically important.

With the rapid growth in diversity of PoE-IoT devices and concept of "Edge intelligence", PoE-IoT security and behavior analysis is the major concern. These PoE-IoT devices lack visibility when the entire network infrastructure is taken into account. The IoT devices are prone to have design faults in their security capabilities. The entire network may be put to risk by attacks on vulnerable IoT devices or malware might get introduced into IoT devices even by routine operations such as firmware upgrade. There have been various approaches based on machine learning(ML) to classify PoE-IoT devices based on network traffic characteristics such as Deep Packet Inspection(DPI). In this paper, we propose a novel method for PoE-IoT classification where ML algorithm, Decision Tree is used. In addition to classification, this method provides useful insights to the network deployment, based on the deviations detected. These insights can further be used for shaping policies, troubleshooting and behavior analysis of PoE-IoT devices.