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An Unsupervised Detection Approach for Hardware Trojans

Chen Dong, Yulin Liu, Jinghui Chen, Ximeng Liu, Wenzhong Guo, Yuzhong Chen
2020 IEEE Access  
Therefore, this paper proposes an unsupervised hardware Trojans detection approach by combined the principal component analysis (PCA) and local outlier factor (LOF) algorithm, called PL-HTD.  ...  INDEX TERMS Hardware security, hardware Trojan detection, integrated circuit, unsupervised machine learning, LOF.  ...  is also used to select an appropriate algorithm for dimensionality reduction of feature set to optimize the detection effect of hardware Trojans.  ... 
doi:10.1109/access.2020.3001239 fatcat:jqxeuyet3zhslcoeanu772a75u

Defeating Untrustworthy Testing Parties: A Novel Hybrid Clustering Ensemble Based Golden Models-Free Hardware Trojan Detection Method

Mingfu Xue, Rongzhen Bian, Weiqiang Liu, Jian Wang
2019 IEEE Access  
INDEX TERMS Hardware security, hardware Trojan detection, untrustworthy testing parties, unsupervised learning, clustering ensemble. 5124 2169-3536  ...  We further propose an adversarial data generation method for untrustworthy testing parties to modify the collected test data.  ...  Some design-for-security approaches are also proposed to facilitate hardware Trojan detection or prevent hardware Trojan insertion, e.g., built-in self-authentication technique to prevent inserting hardware  ... 
doi:10.1109/access.2018.2887268 fatcat:63tvnk6o4vbk3oghk4h7fyn43u

Novel Techniques for High-Sensitivity Hardware Trojan Detection Using Thermal and Power Maps

Abdullah Nazma Nowroz, Kangqiao Hu, Farinaz Koushanfar, Sherief Reda
2014 IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems  
In this paper, we propose a completely new post-silicon multimodal approach using runtime thermal and power maps for Trojan detection and localization.  ...  Hardware Trojans are malicious alterations or injections of unwanted circuitry to integrated circuits (ICs) by untrustworthy factories.  ...  Fig. 5 . 5 Unsupervised clustering flow. Fig. 6 . 6 Unsupervised DBSCAN clustering for Trojan detection.  ... 
doi:10.1109/tcad.2014.2354293 fatcat:mxguhksnsfbpzanvrm6kd5em3u

An Optimal Feature Extraction using Deep Learning Technique for Trojan Detection and Validation using Game Theory

Priyatharishini Murugesan, Amrita School of Engineering, Nirmala Manickam, Amrita School of Engineering
2020 International Journal of Intelligent Engineering and Systems  
To detect the hardware Trojan is a major problem in testing process because of their inherent concealed nature.  ...  In modern electronic design, hardware Trojan has emerged as a major threat in the hardware security.  ...  The related work on different schemes for detecting hardware Trojan is described in section 2. In Section 3, the proposed deep learning approach for detecting the Trojan is presented.  ... 
doi:10.22266/ijies2020.1231.28 fatcat:rkishklm3jhlff6737cyfx54ie

Real-Time Anomaly Detection Framework for Many-Core Router through Machine-Learning Techniques

Amey Kulkarni, Youngok Pino, Matthew French, Tinoosh Mohsenin
2016 ACM Journal on Emerging Technologies in Computing Systems  
In this article, we propose a real-time anomaly detection framework for an NoC-based many-core architecture.  ...  It is also observed that it takes 25% to 18% of the total execution time to detect an anomaly in transferred packets for quad-core and 16-core, respectively.  ...  To the best of our knowledge, this is the first time that an implementation of security in hardware is approached using a machine-learning algorithm, particularly the SVM method for anomaly detection in  ... 
doi:10.1145/2827699 fatcat:znnsj5ofvjd4dodt76y2dfobka

A Survey on Machine Learning against Hardware Trojan Attacks: Recent Advances and Challenges

Zhao Huang, Quan Wang, Yin Chen, Xiaohong Jiang
2020 IEEE Access  
INDEX TERMS Machine learning, hardware Trojan detection, design-for-security, bus security, secure architecture. 10796 This work is licensed under a Creative Commons Attribution 4.0 License.  ...  Therefore, we divide the HT threats into four layers and propose a hardware Trojan defense (HTD) reference model from the perspective of the overall hardware ecosystem, therein categorizing the security  ...  Dang, and so on for their suggestions during the revision of the article.  ... 
doi:10.1109/access.2020.2965016 fatcat:dqh376eosnefbl4pyk6ad4sxjq

Bio-Inspired Approaches to Safety and Security in IoT-Enabled Cyber-Physical Systems

Anju P. Johnson, Hussain Al-Aqrabi, Richard Hill
2020 Sensors  
We demonstrate a bio-inspired approach for hardware Trojan detection using unsupervised learning methods.  ...  When hardware device parameters are in an acceptable range, the design produces a stable firing pattern. When unbalanced, the firing rate reduces to zero, indicating the presence of a Trojan.  ...  The basic unit for bio-inspired hardware Trojan detection using unsupervised learning methods Neurons N 1 to N N resides in the input layer of the network.  ... 
doi:10.3390/s20030844 pmid:32033269 pmcid:PMC7038767 fatcat:77gary6zfbh7nfppbkv32bi2qm

Supervised Machine Learning Techniques for Trojan Detection with Ring Oscillator Network [article]

Kyle Worley, Md Tauhidur Rahman
2019 arXiv   pre-print
The ever-increasing threat of hardware Trojan attacks against integrated circuits has spurred a need for accurate and efficient detection methods.  ...  However, the process variation and measurement noise are the major obstacles to detect hardware Trojan with high accuracy.  ...  Forte for sharing the resources on Trusthub [28] .  ... 
arXiv:1903.04677v1 fatcat:c6kjmjt7mbakdguosnhy4qi5v4

Security For System-On-Chip (SoC) Using Neural Networks [article]

Vedant Ghodke, Shubham Deshmukh, Atharva Deshpande, Ninad Ekbote, Swati Shilaskar
2021 arXiv   pre-print
SoCs have a chance of functionality failure, leakage of information, even a denial of services (DoS), Hardware Trojan Horses and many more factors which are categorized as security threats.  ...  Modern computing ICs are now using system-on-chip for better on-chip processing and communication.  ...  'Runtime Latency Auditor for NoCs (RLAN)' -This approach analyzes data-path latency comparisons to detect whether network bandwidth is being reduced and if it is it indicates the presence of an attack.  ... 
arXiv:2108.13307v1 fatcat:37fam6scknal3m5nfmn3hs54mm

Neural Trojans [article]

Yuntao Liu, Yang Xie, Ankur Srivastava
2017 arXiv   pre-print
The input anomaly detection approach is able to detect 99.8% of Trojan triggers although with 12.2% false positive.  ...  The re-training approach is able to prevent 94.1% of Trojan triggers from triggering the Trojan although it requires that the neural IP be reconfigurable.  ...  to detect the existence of hardware Trojans [27] .  ... 
arXiv:1710.00942v1 fatcat:5h7rnyd7vvb6daisn3e4jhhrdm

HTDet: A clustering method using information entropy for hardware Trojan detection

Renjie Lu, Haihua Shen, Zhihua Feng, Huawei Li, Wei Zhao, Xiaowei Li
2021 Tsinghua Science and Technology  
The DBSCAN is an unsupervised learning algorithm, that can improve the applicability of HTDet.  ...  Hardware Trojans (HTs) have drawn increasing attention in both academia and industry because of their significant potential threat.  ...  An unsupervised learning algorithm, DBSCAN, is used for Trojan detection, which means that HTDet does not require "golden circuit". Furthermore, HTDet does not need to trigger Trojan logics.  ... 
doi:10.26599/tst.2019.9010047 fatcat:5b4qurs2kbex5mqzti3r2l4esm

Special issue on "Cyber Security"

Tarek Saadawi, Ayman El-Desouki
2014 Journal of Advanced Research  
This paper proposes a new methodology to detect/prevent Hardware Trojans in third party IPs.  ...  Miller, and George Kesidis titled; Unsupervised, low latency anomaly detection of algorithmically generated domain names by generative probabilistic modeling, presents a method for detecting anomalous  ... 
doi:10.1016/j.jare.2014.01.007 pmid:25685509 pmcid:PMC4294759 fatcat:w3yrnu6mgjhydmmsed6qpzym7a

SIGNED: A Challenge-Response Based Interrogation Scheme for Simultaneous Watermarking and Trojan Detection [article]

Abhishek Nair, Patanjali SLPSK, Chester Rebeiro, Swarup Bhunia
2020 arXiv   pre-print
Additionally, these watermarks cannot detect Trojan implantation attacks where an adversary alters a design for malicious purposes.  ...  In particular, IP piracy, overproduction, and hardware Trojan attacks pose significant threats to digital design manufacturers.  ...  SIGNED is a hardware watermarking scheme that can detect hardware Trojans and malicious modifications while introducing low overheads.  ... 
arXiv:2010.05209v1 fatcat:r4aia6qlxfcr7jxfgskthouqsq

Two Sides of the Same Coin: Boons and Banes of Machine Learning in Hardware Security

Wenye Liu, Chip-Hong Chang, Xueyang Wang, Chen Liu, Jason Fung, Mohammad Ebrahimabadi, Naghmeh Karimi, Xingyu Meng, Kanad Basu
2021 IEEE Journal on Emerging and Selected Topics in Circuits and Systems  
ML schemes have been extensively used to enhance the security and trust of embedded systems like hardware Trojans and malware detection.  ...  We will discuss the possible future research directions, and thereby, sharing a roadmap for the hardware security community in general.  ...  Controllability and Observability for hardware Trojan Detection (COTD) [89] uses unsupervised k-means clustering to isolate Trojan signals based on the controllability and observability analysis of gate-level  ... 
doi:10.1109/jetcas.2021.3084400 fatcat:c4wdkghpo5fwbhvkekaysnahzm

Cross-layer security framework for smart grid: Physical security layer

Mohammed M. Farag, Mohamed Azab, Bassem Mokhtar
2014 IEEE PES Innovative Smart Grid Technologies, Europe  
We describe an attack scenario raising the emerging hardware Trojan threat in process control systems (PCSes) and its novel PSL resolution leveraging the model predictive control principles.  ...  Such an approach does not address the smart grid security requirements because usually intricate attacks are cross-layer exploiting multiple vulnerabilities at various grid layers and domains.  ...  Fortunately, the PSL employs a proactive approach that enables detecting and preempting Trojan threats.  ... 
doi:10.1109/isgteurope.2014.7028963 fatcat:yb2v7gm5e5cfrlvxpek7jb6tye
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