MicroArchitectural events and image processing-based hybrid approach for robust malware detection

Shukla, Sanket; Kolhe, Gaurav; D, Sai Manoj P; Rafatirad, Setareh

doi:10.1145/3349569.3351538

Cited by 13 publications

(6 citation statements)

References 7 publications

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“…The grayscale images are further represented as sequence of patterns which are further used for sequence classification using RNN-LSTM's. Work in [5] introduces a hybrid approach which utilizes the microarchitectural traces obtained through on-chip embedded hardware performance counters (HPCs) and the application binary for malware detection. The obtained HPCs are fed to multi-stage machine learning (ML) classifier for detecting and classifying the malware.…”

Section: Classification Resultsmentioning

confidence: 99%

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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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Section: Classification Resultsmentioning

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] 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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“…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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“…Consumer and defense systems demanded design and manufacturing of electronics with increased performance, compared to their predecessors [1]. As such systems became ubiquitous in a plethora of domains, their application surface increased, thus making them a target for adversaries [2][3][4][5][6][7][8][9][10][11][12][13][14]. Hence, with improved performance the aspect of security demanded even more attention of the designers.…”

Section: Introduction To Hardware Securitymentioning

confidence: 99%

Reverse Engineering of Integrated Circuits: Tools and Techniques

Dhavlle¹

2022

Preprint

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Consumer and defense systems demanded design and manufacturing of electronics with increased performance, compared to their predecessors. As such systems became ubiquitous in a plethora of domains, their application surface increased, thus making them a target for adversaries. Hence, with improved performance the aspect of security demanded even more attention of the designers. The research community is rife with extensive details of attacks that target the confidential design details by exploiting vulnerabilities. The adversary could target the physical design of a semiconductor chip or break a cryptographic algorithm by extracting the secret keys, using attacks that will be discussed in this thesis. This thesis focuses on presenting a brief overview of IC reverse engineering attack and attacks targeting cryptographic systems. Further, the thesis presents my contributions to the defenses for the discussed attacks. The globalization of the Integrated Circuit (IC) supply chain has rendered the advantage of low-cost and high performance ICs in the market for the end users. But this has also made the design vulnerable to over production, IP Piracy, reverse engineering attacks and hardware malware during the manufacturing and post manufacturing process. Logic locking schemes have been proposed in the past to overcome the design trust issues but the new state-of-the-art attacks such as SAT has proven a larger threat. This work highlights the reverse engineering attack and a proposed hardened platform along with its framework. On the other side, the side-channel attacks (SCAs) has been one of the emerging threats. These SCAs function by exploiting the side-channels which invariably leak important data during an application's execution. The information leaked through side-channels are inherent characteristics of the system and is often unintentional. This information can be microarchitectural or physical information such as power consumption, thermal maps, timing of the operation, acoustics, and cache-trace. Intercepting secret information based on the study of power signature is a subdivision of SCAs where power consumption information serves as a covert channel leaking crucial information about the executed operations. Such physical SCAs are known to be a significant threat to cryptosystems such as AES (Advanced Encryption Standard) and can reveal the encryption key efficiently. To overcome such concerns and protect the data integrity, I introduce Power Swapper in this work. The proposed Power Swapper thwarts the attack by randomly choosing one of the multiple modules that perform the intended activity, but have power signature different than a standard implementation and can lead to similar power consumption as one of the other modules that perform a different operation. To achieve this, I introduce carefully crafted swapping of the standby modules that are responsible for the AES operation thus deluding the attacker without hurting the crypto operation. This methodology has been validated for the AES power analysis ...

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MicroArchitectural events and image processing-based hybrid approach for robust malware detection

Cited by 13 publications

References 7 publications

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

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

Design of secure and robust cognitive system for malware detection

Reverse Engineering of Integrated Circuits: Tools and Techniques

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