Deep Graph Learning for Circuit Deobfuscation

Chen, Zhiqian; Zhang, Lei; Kolhe, Gaurav; Kamali, Hadi Mardani; Rafatirad, Setareh; Dinakarrao, Sai Manoj Pudukotai; Homayoun, Houman; Lu, Chang-Tien; Zhao, Liang

doi:10.3389/fdata.2021.608286

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…Moreover, this study can be further developed to for the purpose of linking finite element modelling software in AI assisted design of magnetic components for the purpose of optimal component values/shape design. Additionally, the proposed methodology has very high potential in circuit obfuscation and reverse engineering when it is required to identify/obscure circuit structure [80]. One idea works on the circuit side utilizing the GNN capability of learning the proper transformation function of the converter, i.e can obtain a mathematical transformation of every circuit component and eventually all circuit behavior.…”

Section: B) Switching Circuit Classifiermentioning

confidence: 99%

Comprehensive Mapping of Continuous/Switching Circuits in CCM and DCM to Machine Learning Domain Using Homogeneous Graph Neural Networks

Khamis

Agamy

2023

IEEE Open J. Circuits Syst.

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This paper proposes a method of transferring physical continuous and switching/converter circuits working in continuous conduction mode (CCM) and discontinuous conduction mode (DCM) to graph representation, independent of the connection or the number of circuit components, so that machine learning (ML) algorithms and applications can be easily applied. Such methodology is generalized and is applicable to circuits with any number of switches, components, sources and loads, and can be useful in applications such as artificial intelligence (AI) based circuit design automation, layout optimization, circuit synthesis and performance monitoring and control. The proposed circuit representation and feature extraction methodology is applied to seven types of continuous circuits, ranging from second to fourth order and it is also applied to three of the most common converters (Buck, Boost, and Buck-boost) operating in CCM or DCM. A classifier ML task can easily differentiate between circuit types as well as their mode of operation, showing classification accuracy of 97.37% in continuous circuits and 100% in switching circuits.

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Section: B) Switching Circuit Classifiermentioning

confidence: 99%

Comprehensive Mapping of Continuous/Switching Circuits in CCM and DCM to Machine Learning Domain Using Homogeneous Graph Neural Networks

Khamis

Agamy

2023

IEEE Open J. Circuits Syst.

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“…The hardware security discipline in recent years experienced a plethora of threats like the Malware attacks [1][2][3][4][5][6][7], Side-Channel Attacks [8][9][10][11], Hardware Trojan attacks [12], reverse engineering threats [13][14][15][16][17][18][19][20][21][22][23][24][25][26] and so on. I focus on the malware detection technique here along with some state-of-the-art works.…”

Section: Introduction To Malware Threatsmentioning

confidence: 99%

Design of secure and robust cognitive system for malware detection

Shukla¹

2022

Preprint

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The computer systems for decades have been threatened by various types of hardware and software attacks of which Malware have been one of the pivotal issues. This malware has the ability to steal, destroy, contaminate, gain unintended access, or even disrupt the entire system. There have been techniques to detect malware by performing static and dynamic analysis of malware files, but, stealthy malware has circumvented the static analysis method and for dynamic analysis, there have been previous works that propose different methods to detect malware. However, these techniques do not perform well on stealthy malware. Moreover, the rising trend and advancements in machine learning has resulted into its numerous applications in the field of computer vision, pattern recognition to providing security to hardware devices. Machine learning based malware detection techniques rely on grayscale images of malware and tends to classify malware based on the distribution of textures in graycale images. Albeit the advancement and promising results shown by machine learning techniques, attackers can exploit the vulnerabilities by generating adversarial samples. Adversarial samples are generated by intelligently crafting and adding perturbations to the input samples. There exists majority of the software based adversarial attacks and defenses. To defend against the adversaries, the existing malware detection based on machine learning and grayscale images needs a preprocessing for the adversarial data. This can cause an additional overhead and can prolong the real-time malware detection. So, as an alternative to this, we explore RRAM (Resistive Random Access Memory) based defense against adversaries. Therefore, the aim of this thesis is to address the above mentioned critical system security issues. The above mentioned challenges are addressed by demonstrating proposed techniques to design a secure and robust cognitive system. First, a novel technique to detect stealthy malware is proposed. The technique uses malware binary images and then extract different features from the same and then employ different ML-classifiers on the dataset thus obtained. Results demonstrate that this technique is successful in differentiating classes of malware based on the features extracted. Secondly, I demonstrate the effects of adversarial attacks on a reconfigurable RRAM-neuromorphic architecture with different learning algorithms and device characteristics. I also propose an integrated solution for mitigating the effects of the adversarial attack using the reconfigurable RRAM architecture.

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