scholarly journals Attack Mitigation of Hardware Trojans for Thermal Sensing via Micro-ring Resonator in Optical NoCs

2021 ◽  
Vol 17 (3) ◽  
pp. 1-23
Author(s):  
Jun Zhou ◽  
Mengquan Li ◽  
Pengxing Guo ◽  
Weichen Liu

As an emerging role in new-generation on-chip communication, optical networks-on-chip (ONoCs) provide ultra-high bandwidth, low latency, and low power dissipation for data transfers. However, the thermo-optic effects of the photonic devices have a great impact on the operating performance and reliability of ONoCs, where the thermal-aware control with accurate measurements, e.g., thermal sensing, is typically applied to alleviate it. Besides, the temperature-sensitive ONoCs are prone to be attacked by the hardware Trojans (HTs) covertly embedded in the counterfeit integrated circuits (ICs) from the malicious third-party vendors, leading to performance degradation, denial-of-service (DoS), or even permanent damages. In this article, we focus on the tampering and snooping attacks during the thermal sensing via micro-ring resonator (MR) in ONoCs. Based on the provided workflow and attack model, a new structure of the anti-HT module is proposed to verify and protect the obtained data from the thermal sensor for attacks in its optical sampling and electronic transmission processes. In addition, we present the detection scheme based on the spiking neural networks (SNNs) to implement an accurate classification of the network security statuses for further high-level control. Evaluation results indicate that, with less than 1% extra area of a tile, our approach can significantly enhance the hardware security of thermal sensing for ONoC with trivial costs of up to 8.73%, 5.32%, and 6.14% in average latency, execution time, and energy consumption, respectively.


Micromachines ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 54
Author(s):  
Yan-Li Zheng ◽  
Ting-Ting Song ◽  
Jun-Xiong Chai ◽  
Xiao-Ping Yang ◽  
Meng-Meng Yu ◽  
...  

The photoelectric hybrid network has been proposed to achieve the ultrahigh bandwidth, lower delay, and less power consumption for chip multiprocessor (CMP) systems. However, a large number of optical elements used in optical networks-on-chip (ONoCs) generate high transmission loss which will influence network performance severely and increase power consumption. In this paper, the Dijkstra algorithm is adopted to realize adaptive routing with minimum transmission loss of link and reduce the output power of the link transmitter in mesh-based ONoCs. The numerical simulation results demonstrate that the transmission loss of a link in optimized power control based on the Dijkstra algorithm could be maximally reduced compared with traditional power control based on the dimensional routing algorithm. Additionally, it has a greater advantage in saving the average output power of optical transmitter compared to the adaptive power control in previous studies, while the network size expands. With the aid of simulation software OPNET, the network performance simulations in an optimized network revealed that the end-to-end (ETE) latency and throughput are not vastly reduced in regard to a traditional network. Hence, the optimized power control proposed in this paper can greatly reduce the power consumption of s network without having a big impact on network performance.



Author(s):  
Yu-Kai Chuang ◽  
Yong Zhong ◽  
Yi-Hao Cheng ◽  
Bo-Yi Yu ◽  
Shao-Yun Fang ◽  
...  


2021 ◽  
pp. 1-1
Author(s):  
Linpeng Gu ◽  
Yuan Qingchen ◽  
Qiang Zhao ◽  
Ji Yafei ◽  
Liu Ziyu ◽  
...  


2016 ◽  
Vol 34 (15) ◽  
pp. 3550-3562 ◽  
Author(s):  
Yiyuan Xie ◽  
Tingting Song ◽  
Zhendong Zhang ◽  
Chao He ◽  
Jiachao Li ◽  
...  


Author(s):  
Shanshan Yu ◽  
Jicheng Zhang ◽  
Ju Liu ◽  
Xiaoqing Zhang ◽  
Yafeng Li ◽  
...  

AbstractIn order to solve the problem of distributed denial of service (DDoS) attack detection in software-defined network, we proposed a cooperative DDoS attack detection scheme based on entropy and ensemble learning. This method sets up a coarse-grained preliminary detection module based on entropy in the edge switch to monitor the network status in real time and report to the controller if any abnormality is found. Simultaneously, a fine-grained precise attack detection module is designed in the controller, and a ensemble learning-based algorithm is utilized to further identify abnormal traffic accurately. In this framework, the idle computing capability of edge switches is fully utilized with the design idea of edge computing to offload part of the detection task from the control plane to the data plane innovatively. Simulation results of two common DDoS attack methods, ICMP and SYN, show that the system can effectively detect DDoS attacks and greatly reduce the southbound communication overhead and the burden of the controller as well as the detection delay of the attacks.



2019 ◽  
Vol 27 (7) ◽  
pp. 1513-1526 ◽  
Author(s):  
Yuan Liang ◽  
Chirn Chye Boon ◽  
Chenyang Li ◽  
Xiao-Lan Tang ◽  
Herman Jalli Ng ◽  
...  




2013 ◽  
Vol 21 (10) ◽  
pp. 1823-1836 ◽  
Author(s):  
Yiyuan Xie ◽  
Mahdi Nikdast ◽  
Jiang Xu ◽  
Xiaowen Wu ◽  
Wei Zhang ◽  
...  


Author(s):  
Yurii A. Vlasov ◽  
Fengnian Xia ◽  
Solomon Assefa ◽  
William M. J. Green
Keyword(s):  


2021 ◽  
Author(s):  
Zhidan Zheng ◽  
Mengchu Li ◽  
Tsun-Ming Tseng ◽  
Ulf Schlichtmann


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