Biblio

Nowadays, the Network on Chip (NoC) is widely adopted by multi-core System on Chip (SoC) to meet its communication needs. With the gradual popularization of the Internet of Things (IoT), the application of NoC is increasing. Due to its distribution characteristics on the chip, NoC has gradually become the focus of potential security attacks. Denial of service (DoS) is a typical attack and it is caused by malicious intellectual property (IP) core with unnecessary data packets causing communication congestion and performance degradation. In this article, we propose a novel approach to detect DoS attacks on-line based on random forest algorithm, and detect the router where the attack enters the sensitive communication path. This method targets malicious third-party vendors to implant a DoS Hardware Trojan into the NoC. The data set is generated based on the behavior of multi-core routers triggered by normal and Hardware Trojans. The detection accuracy of the proposed scheme is in the range of 93% to 94%.

Internet of Things (IoT) era has gradually entered our life, with the rapid development of communication and embedded system, IoT technology has been widely used in many fields. Therefore, to maintain the security of the IoT system is becoming a priority of the successful deployment of IoT networks. This paper presents an intrusion detection model based on improved Deep Belief Network (DBN). Through multiple iterations of the genetic algorithm (GA), the optimal network structure is generated adaptively, so that the intrusion detection model based on DBN achieves a high detection rate. Finally, the KDDCUP data set was used to simulate and evaluate the model. Experimental results show that the improved intrusion detection model can effectively improve the detection rate of intrusion attacks.

Network functions (NFs), like firewall, NAT, IDS, have been widely deployed in today’s modern networks. However, currently there is no standard specification or modeling language that can accurately describe the complexity and diversity of different NFs. Recently there have been research efforts to propose NF models. However, they are often generated manually and thus error-prone. This paper proposes a method to automatically synthesize NF models via program analysis. We develop a tool called NFactor, which conducts code refactoring and program slicing on NF source code, in order to generate its forwarding model. We demonstrate its usefulness on two NFs and evaluate its correctness. A few applications of NFactor are described, including network verification.

Underwater sensing is envisioned using inexpensive underwater sensor nodes distributed over a wide area, deployed close to the bottom, and networked through underwater acoustic communications. In this paper, an underwater acoustic sensor node to perform the underwater sensing is designed and implemented. Specifically, we describe the design criteria, architecture and functional modules of underwater acoustic sensor node. Moreover, we give the experiment results of ocean current field estimation using the designed underwater acoustic sensor nodes at the sea area of Liuheng, Zhoushan, China.