Automatic Feature Selection of Hardware Layout: A Step toward Robust Hardware Trojan Detection

Nasr, Abdurrahman A.; Abdulmageed, Mohamed Z.

doi:10.1007/s10836-016-5581-5

Cited by 21 publications

(8 citation statements)

References 23 publications

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“…Recent research in this field has explored machine learning methods for HT detection [41, 88–90, 105–111]. Generally, machine learning methods can be utilised for HT detection in the following aspects: providing automatic layout identification in RE‐based methods [88, 105, 106], providing run‐time HT detection architectures, which are trained by HT attack behaviours [107, 108], providing automatic feature analysis [112], and providing golden chips‐free HT detection techniques based on classification or clustering [41, 89, 90, 109–111]. In particular, the machine learning method has its own specialties in feature extraction and image recognition, which makes it possible to reveal unknown HTs by monitoring suspicious behaviours and features.…”

Section: Future Directionsmentioning

confidence: 99%

Ten years of hardware Trojans: a survey from the attacker's perspective

Xue

Liu

et al. 2020

IET Computers & Digital Techniques

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In the last decade, hardware Trojan has emerged as a serious concern in integrated circuit (IC) industry. As such, hardware Trojan detection techniques have been studied extensively. However, in order to develop reliable and effective defenses, it is important to figure out how hardware Trojans are implemented in practical scenarios. In this paper, we attempt to make a review of the hardware Trojan design and implementations in the last decade and also provide an outlook. Unlike all previous surveys that discuss Trojans from the defender's perspective, for the first time, we study the Trojans from the attacker's perspective, focusing on the attacker's methods, capabilities and challenges when he designs and implements a hardware Trojan. Particularly, the following questions are explored. What are the current methods and capabilities of attackers after ten years of research and development? By considering more and more sophisticated hardware Trojan detection techniques, what challenges do the attackers face, and vice versa? First, we present adversarial models in terms of the adversary's methods, adversary's capabilities and adversary's challenges in seven practical hardware Trojan implementation scenarios: in-house design team attacks, third-party intellectual property (3PIP) vendor attacks, computer-aided design (CAD) tools attacks, fabrication stage attacks, testing stage attacks, distribution stage attacks, and field programmable gate array (FPGA) Trojan attacks. Second, we analyze the hardware Trojan implementation methods under each adversarial model in terms of seven aspects/metrics: hardware Trojan attack scenarios, the attacker's motivation, feasibility (the practicality of the attacks), detectability (anti-detection capability of that kind of Trojan), protection and prevention suggestions for the designer, overhead analysis, and case studies of Trojan implementations. Finally, future directions on hardware Trojan attacks and defenses are discussed. This paper also presents several new insights and assumptions for the first time, including considering the Trojans not only from the copyright owner's perspective, but also from the users' perspective, and discussing the hardware Trojan attacks in the testing phase and in the distribution phase. This paper can hopefully provide a reference for future works on building effective hardware Trojan defenses.

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Section: Future Directionsmentioning

confidence: 99%

Ten years of hardware Trojans: a survey from the attacker's perspective

Xue

Liu

et al. 2020

IET Computers & Digital Techniques

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“…Limited by the mechanical precision and the workmanship, various defects may exist such as the errors of solder joints, stains and elements damages. Nowadays, the main phases of the IC's production are distributed globally, including design, synthesis, fabrication and distribution [4]. This global cooperation model makes ICs become vulnerable to the HTs, which can leak secret information, invalidate the IC or cause other catastrophic consequences [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

An Effective Clustering Algorithm for the Low-Quality Image of Integrated Circuits via High-Frequency Texture Components Extraction

et al. 2022

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Verification is one of the core steps in integrated circuits (ICs) manufacturing due to the multifarious defects and malicious hardware Trojans (HTs). In most cases, the effectiveness of the detection relies on the quality of the sample images of ICs. However, the high-precision and noiseless images are hard to capture due to the mechanical precision, manual error and environmental interference. In this paper, an effective approach for processing the low-quality image data of ICs is proposed. Our approach can successfully categorize the partial pictures of multiple objected ICs with low resolution and various noise. The proposed approach extracts the high-frequency texture components (HFTC) of the images and constructs a graph with the correlationship among features. Subsequently, the spectral clustering is conducted for obtaining the final cluster indicators. The low-quality images of ICs can be successfully categorized by the proposed approach, which will provide a data foundation for the following verification tasks. In order to evaluate the effectiveness of the proposed approach, several experiments are conducted in the simulated datasets, which are generated by corrupting the real-world data in different conditions. The clustering results reveal that our approach can achieve the best performance with good stability compared to the baselines.

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“…Most recently, a few machine learning based HT detection methods have been proposed [12][13][14][15][16]. Nasr and Abdulmageed [12] use machine learning to provide automatic layout identification in the reverse engineering-based detection method. However, this destructive detection method is typically expensive and time consuming, which also requires considerable manual efforts.…”

Section: Introductionmentioning

confidence: 99%

“…Most recently, a few machine learning based HT detection methods have been proposed [12–16]. Nasr and Abdulmageed [12] use machine learning to provide automatic layout identification in the reverse engineering‐based detection method.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, destructive reverse engineering can only be used in a small set of sample ICs while the trust of the remaining ICs remain unknown. The scheme in [12] also requires golden chips for training which are difficult to obtain. In [13, 14], Bao et al apply support vectors machine (SVM) and K‐means algorithms separately facilitating the reverse engineering approach to identify the Trojan‐free ICs.…”

Section: Introductionmentioning

confidence: 99%

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Building an accurate hardware Trojan detection technique from inaccurate simulation models and unlabelled ICs

Xue

Bian

Wang

et al. 2019

IET Computers & Digital Techniques

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Most of prior hardware Trojan detection approaches require golden chips for references. A classification-based golden chips-free hardware Trojan detection technique has been proposed in the authors' previous work. However, the algorithm in that work is trained by simulated ICs without considering a shift between the simulation and silicon fabrication. In this study, a co-training based hardware Trojan detection method by exploiting inaccurate simulation models and unlabeled fabricated ICs is proposed to provide reliable detection capability when facing fabricated ICs, which eliminates the need of golden chips. Two classification algorithms are trained using simulated ICs. These two algorithms can identify different patterns in the unlabelled ICs during test-time, and thus can label some of these ICs for the further training of the other algorithm. Moreover, a statistical examination is used to choose ICs labelling for the other algorithm. A statistical confidence interval based technique is also used to combine the hypotheses of the two classification algorithms. Furthermore, the partial least squares method is used to preprocess the raw data of ICs for feature selection. Both EDA experiment results and field programmable gate array (FPGA) experiment results show that the proposed technique can detect unknown Trojans with high accuracy and recall.

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Automatic Feature Selection of Hardware Layout: A Step toward Robust Hardware Trojan Detection

Cited by 21 publications

References 23 publications

Ten years of hardware Trojans: a survey from the attacker's perspective

Ten years of hardware Trojans: a survey from the attacker's perspective

An Effective Clustering Algorithm for the Low-Quality Image of Integrated Circuits via High-Frequency Texture Components Extraction

Building an accurate hardware Trojan detection technique from inaccurate simulation models and unlabelled ICs

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