Imitating Functional Operations for Mitigating Side-Channel Leakage

Dhavlle, Abhijitt; Rafatirad, Setareh; Khasawneh, Khaled N.; Homayoun, Houman; Dinakarrao, Sai Manoj Pudukotai

doi:10.1109/tcad.2021.3070243

Cited by 9 publications

(10 citation statements)

References 29 publications

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“…With respect to related works [57][58][59][60][61][62][63], our classification method does not require any disassembly or execution of the actual malware code. Moreover, the image textures used for classification provide more resilient features in terms of obfuscation techniques, and in particular for encryption.…”

Section: Literature Review and Motivationmentioning

confidence: 99%

“…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%

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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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Section: Literature Review and Motivationmentioning

confidence: 99%

Section: Introduction To Malware Threatsmentioning

confidence: 99%

Design of secure and robust cognitive system for malware detection

Shukla¹

2022

Preprint

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“…As described in the previous sections, I have been working and publishing papers on Malware Detection [6], Side-Channels Analysis [20,21], Hardware-based Trojan Attack and Detection [10], and Survey-based papers [43,44] published to conferences and journals. I intend to dive deeper into more of SCA based attacks and defenses in future and contribute my work to top tier conferences and journals.…”

Section: Research Progressmentioning

confidence: 99%

“…Snooping on co-located programs' cache trace is performed by cache-based SCAs and is prominently explored in the domain of SCAs. A part of my thesis work focuses on timing-based cache attacks, which exploit the time required to flush/reload an instruction from the cache subsystem as a covert channel [20,21]. Some of the popular cache targeted SCAs are the Flush+Reload [1] and Flush+Flush attack [2].…”

Section: Introductionmentioning

confidence: 99%

Adversarial Learning Inspired Emerging Side-Channel Attacks and Defenses

Dhavlle¹

2021

Preprint

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Evolving attacks on the vulnerabilities of the computing systems demand novel defense strategies to keep pace with newer attacks. This report discusses previous works on side-channel attacks (SCAs) and defenses for cache-targeted and physical proximity attacks. We then discuss the proposed Entropy-Shield as a defense against timing SCAs, and explain how we can extend the same to hardware-based implementations of crypto applications as "Entropy-Shield for FPGA". We then discuss why we want to build newer attacks with the hope of coming up with better defense strategies.

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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] and so on. We focus on the malware detection technique here along with some state-of-the-art works.…”

Section: Introductionmentioning

confidence: 99%

A Novel Malware Detection Mechanism based on Features Extracted from Converted Malware Binary Images

Dhavlle,

Shukla

2021

Preprint

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Our computer systems since decades have been threatened by various types of harware and software attacks of which Malwares have been one of them. These malwares have the ability to steal, destroy, contaminate, gain unintended access or even disrupt the entire system. There have been techniques to detect malwares 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 malwares but, in this work we propose a novel technique to detect malwares. We use malware binary images and then extract different features from the same and then employ different ML-classifiers on the dataset thus obtained. We show that this technique is successful in differentiating classes of malware based on the features extracted.

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Imitating Functional Operations for Mitigating Side-Channel Leakage

Cited by 9 publications

References 29 publications

Design of secure and robust cognitive system for malware detection

Design of secure and robust cognitive system for malware detection

Adversarial Learning Inspired Emerging Side-Channel Attacks and Defenses

A Novel Malware Detection Mechanism based on Features Extracted from Converted Malware Binary Images

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