Book ChapterDOI
Hardware Trojan Detection Using Deep Learning Technique
K. Reshma,M. Priyatharishini,M. Nirmala Devi +2 more
- pp 671-680
TLDR
The paper shows that it is easy to identify genuine nodes and Trojan-infected nodes based on controllability and transition probability values of a given Trojan- infected circuit.Abstract:
A method to detect hardware Trojan in gate-level netlist is proposed using deep learning technique. The paper shows that it is easy to identify genuine nodes and Trojan-infected nodes based on controllability and transition probability values of a given Trojan-infected circuit. The controllability and transition probability characteristics of Trojan-infected nodes show large inter-cluster distance from the genuine nodes so that it is easy to cluster the nodes as Trojan-infected nodes and genuine nodes. From a given circuit, controllability and transition probability values are extracted as Trojan features using deep learning algorithm and clustering the data using k-means clustering. The technique is validated on ISCAS’85 benchmark circuits, and it does not require any golden model as reference. The proposed method can detect all Trojan-infected nodes in less than 6 s with zero false positive and zero false negative detection accuracy.read more
Citations
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Proceedings ArticleDOI
HTnet: Transfer Learning for Golden Chip-Free Hardware Trojan Detection
TL;DR: In this paper, the authors proposed a novel neural network design (i.e. HTNet) and a feature extractor training methodology that can be used for HT detection in run time.
Journal ArticleDOI
Deep learning-based security behaviour analysis in IoT environments: A survey
TL;DR: In this paper, the authors provide a survey of deep learning applications in IoT for security and privacy concerns, and evaluate the suitability of DNNs to improve the security of IoT systems.
Book ChapterDOI
Hardware Trojan Detection Using Machine Learning Technique
TL;DR: A hardware Trojan detection method that works at the gate-level using the netlist of the circuit under test using the unsupervised machine learning algorithm, K-Means classification is used for categorization.
Book ChapterDOI
Hardware Trojan Detection Using Deep Learning-Deep Stacked Auto Encoder
R. Vishnupriya,M. Nirmala Devi +1 more
TL;DR: Deep Learning (DL) class of ML can choose the relevant features and learn and learn, which has been proposed in this paper and which is higher than previously discussed works.
Proceedings ArticleDOI
Toggle Count Based Logic Obfuscation
TL;DR: An obfuscation module shall be proposed to enhance the security of integrated chips(IC) and it is ensured that the corrupted output is obtained when the wrong key is given.
References
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Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
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A Survey of Hardware Trojan Taxonomy and Detection
TL;DR: A classification of hardware Trojans and a survey of published techniques for Trojan detection are presented.
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Hardware Trojan: Threats and emerging solutions
TL;DR: The threat posed by hardware Trojans and the methods of deterring them are analyzed, a Trojan taxonomy, models of Trojan operations and a review of the state-of-the-art Trojan prevention and detection techniques are presented.
Proceedings ArticleDOI
Towards trojan-free trusted ICs: problem analysis and detection scheme
TL;DR: This work analyzes and formulates the trojan detection problem based on a frequency analysis under rare trigger values and provides procedures to generate input trigger vectors and trojan test vectors to detect trojan effects.