Hardware-Assisted Malware Detection using Explainable Machine Learning

Pan, Zhixin; Sheldon, Jennifer; Mishra, Prabhat

doi:10.1109/iccd50377.2020.00113

Cited by 18 publications

(7 citation statements)

References 9 publications

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“…The subsequent section highlights the existing research that does utilize HPCs for security, although very little research exists applying this to IoT devices. Similarly, the work presented in [19] claims to be the pioneering hardware-assisted malware detection mechanism. However, our work distinguishes itself through the utilization of distinct underlying features.…”

Section: A Malware Detection In Iot Devicesmentioning

confidence: 95%

Enhancing IoT Security: Novel Mechanisms for Malware Detection using HPCs and Neural Networks

Adhikari,

Asad,

Jones

2023

2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

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With the exponential rise of internet-connected devices, the number of Internet of Things (IoT) devices has surpassed that of traditional IT devices. This proliferation in IoT adoption can be attributed to the growing demand for manufacturing automation and the desire for enhanced quality of life, leading to the production of smart devices in various industries. However, this rapid adoption has caught the attention of malicious actors, resulting in a significant increase in cyberattacks targeting IoT devices. In response to this emerging threat landscape, research on IoT security has been active. Nevertheless, the lack of commercial tools specifically designed for IoT device security raises concerns about the ability of security research and adoption to keep pace with the rising number of malicious actors. To address this gap, this study focuses on introducing a novel mechanism for detecting malware in IoT devices. By conducting experiments, we demonstrate that using Hardware Performance Counters (HPCs), complemented by physical features such as power consumption, can improve the current malware detection capabilities. Specifically, we employ Recurrent Neural Networks (RNN) and Multi-Layer Perception Neural Networks (MLP) to achieve a remarkable detection accuracy of 95% within a timeframe of less than 10 seconds from infection.

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Section: A Malware Detection In Iot Devicesmentioning

confidence: 95%

Enhancing IoT Security: Novel Mechanisms for Malware Detection using HPCs and Neural Networks

Adhikari,

Asad,

Jones

2023

2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

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“…This aspect emphasizes understanding and explaining how the model makes predictions or decisions. Pan et al [55] proposed a solution that focuses on overcoming the limitations of current malware detection methods, which include prediction inaccuracy and a lack of transparency. To address these two challenges, they have developed a hardware-assisted malware detection framework using an XML algorithm based on regression.…”

Section: Explainable Machine Learning-based Malware Analysismentioning

confidence: 99%

Machine Learning in Malware Analysis: Current Trends and Future Directions

Altaha,

Riad

2024

IJACSA

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Malware analysis is a critical component of cybersecurity due to the increasing sophistication and the widespread of malicious software. Machine learning is highly significant in malware analysis because it can process huge amounts of data, identify complex patterns, and adjust to changing threats. This paper provides a comprehensive overview of existing work related to Machine Learning (ML) methods used to analyze malware along with a description of each trend. The results of the survey demonstrate the effectiveness and importance of three trends, which are: deep learning, transfer learning, and XML techniques in the context of malware analysis. These approaches improve accuracy, interpretability, and transparency in detecting and analyzing malware. Moreover, the related challenges and Issues are presented. After identifying these challenges, we highlight future directions and potential areas that require more attention and improvement, such as distributed computing and parallelization techniques which can reduce training time and memory requirements for large datasets. Also, further investigation is needed to develop image resizing techniques to be used during the visual representation of malware to minimize information loss while maintaining consistent image sizes. These areas can contribute to the enhancement of machine learning-based malware analysis.

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“…It was included in this paragraph because it is tested on a Malware detection dataset. Pan et al [144], [145] in two related works propose a hardware-assisted malware detection framework developing a regression-based explainable Machine Learning algorithm. They apply a Decision Tree or Linear Regression to interpret the final result.…”

Section: ) Explainable Artificial Intelligence In Malware Detectionmentioning

confidence: 99%

Explainable Artificial Intelligence in CyberSecurity: A Survey

et al. 2022

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Nowadays, Artificial Intelligence (AI) is widely applied in every area of human being's daily life. Despite the AI benefits, its application suffer from the opacity of complex internal mechanisms and doesn't satisfy by design the principles of Explainable Artificial Intelligence (XAI). The lack of transparency further exacerbates the problem in the field of Cybersecurity because entrusting crucial decisions to a system that cannot explain itself presents obvious dangers. There are several methods in the literature capable of providing explainability of AI results. Anyway, the application of XAI in Cybersecurity can be a doubleedged sword. It substantially improves the Cybersecurity practices but simultaneously leaves the system vulnerable to adversary attacks. Therefore, there is a need to analyze the state-of-the-art of XAI methods in Cybersecurity to provide a clear vision for future research. This study presents an in-depth examination of the application of XAI in Cybersecurity. It considers more than 300 papers to comprehensively analyze the main Cybersecurity application fields, like Intrusion Detection Systems,Malware detection, Phishing and Spam detection, BotNets detection, Fraud detection, Zero-Day vulnerabilities, Digital Forensics and Crypto-Jacking . Specifically, this study focuses on the explainability methods adopted or proposed in these fields, pointing out promising works and new challenges.

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Hardware-Assisted Malware Detection using Explainable Machine Learning

Cited by 18 publications

References 9 publications

Enhancing IoT Security: Novel Mechanisms for Malware Detection using HPCs and Neural Networks

Enhancing IoT Security: Novel Mechanisms for Malware Detection using HPCs and Neural Networks

Machine Learning in Malware Analysis: Current Trends and Future Directions

Explainable Artificial Intelligence in CyberSecurity: A Survey

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