When Machine Learning Meets Hardware Cybersecurity: Delving into Accurate Zero-Day Malware Detection

He, Zhangying; Miari, Tahereh; Makrani, Hosein Mohammadi; Aliasgari, Mehrdad; Homayoun, Houman; Sayadi, Hossein

doi:10.1109/isqed51717.2021.9424330

Cited by 26 publications

(15 citation statements)

References 16 publications

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“…IoT devices are sensitive to cyber-attacks as most IoT nodes are constantly attached and share data over the Internet (Roopak et al, 2019). The current development of portable and IoT devices has further amplified the consequence of malware attacks (He et al, 2021). As a result, the risks are exponentially more significant for IoT devices.…”

Section: Research Challenges and Issues In Cyber Securitymentioning

confidence: 99%

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Cyber Security Threats and Countermeasures using Machine and Deep Learning Approaches: A Survey

Manjula¹,

Venkatesh²,

Venugopal³

2023

Journal of Computer Science

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Recent advancements in e-business, e-healthcare, e-governance, and online digital transactions have brought valuable benefits. Unfortunately, it raises severe cyber-attacks. Cyberattacks disrupt normal operations, try to retrieve confidential information and defense secrets, and subvert the nation's defense systems and Internet-connected devices. Cyber security solutions are required to detect, analyze, defend against threats and protect sensitive data from unauthorized access. This study gives a detailed survey of different cybersecurity attacks, like Denial-of-service attacks, Botnet Evasion Attacks, Malware invasions, Spam and phishing invasion, Spoofing, Domain Generation algorithms, Probing attacks, R2L, and U2R attacks. This research review emphasizes Machine Learning and Deep Learning-based approaches to Cybersecurity problems. This study's key highlights are the research challenges, cybersecurity issues, cyber security domains, and tools for the Intrusion detection system. Data sets play a vital role in cybersecurity research; hence, Private and Publically available datasets are reviewed in this study. Various performance matrices are discussed in this survey which can be used to evaluate the effectiveness of cybersecurity solutions.

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Section: Research Challenges and Issues In Cyber Securitymentioning

confidence: 99%

“…"Zero-day" refers to newly realized security susceptibilities that attackers can exploit to attack systems. In other words, the vendor or the developer has "zero days" to fix it (He et al, 2021). The vendor or developer would have just learned about the flaw.…”

Section: Intrusion Detection Systems (Ids)mentioning

confidence: 99%

Cyber Security Threats and Countermeasures using Machine and Deep Learning Approaches: A Survey

Manjula¹,

Venkatesh²,

Venugopal³

2023

Journal of Computer Science

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“…Although researchers have suggested solutions to many of the cyber security concerns raised by the characteristics of IoT, more research is required to address the security concerns and vulnerabilities identified in this paper, particularly at the physical layer. ML is a technology often recommended alongside AI as a solution for enhanced cyber security for IoT devices [78], [79], often due to its placement on the network edge [80]. Although ML offers significant benefits in enhancing security and privacy, one of the major disadvantages of the technology is the ability of cybercriminals to also use it as a tool to circumvent protective measures [81].…”

Section: D: Application Protocols and Application Services Layermentioning

confidence: 99%

Security and Privacy for Low Power IoT Devices on 5G and Beyond Networks: Challenges and Future Directions

2023

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The growth in the use of small sensor devices, commonly known as the Internet of Things (IoT), has resulted in unprecedented amounts of data being generated and captured. With the rapidly growing popularity of personal IoT devices, the collection of personal data through such devices has also increased exponentially. To accommodate the anticipated growth in connected devices, researchers are now investigating futuristic network technologies that are capable of processing large volumes of information at much faster speeds. However, the introduction of innovative network technologies coupled with existing vulnerabilities of personal IoT devices and insufficient device security standards is resulting in new challenges for the security of data collected on these devices. While existing research has focused on the technical aspects of security vulnerabilities and solutions in either network or IoT technologies separately, this paper thoroughly investigates common aspects impacting IoT security on existing and futuristic networks, including human-centric issues and the mechanisms that can lead to loss of confidentiality. By undertaking a comprehensive literature review of existing research, this article has identified five key areas that impact IoT security for futuristic next generation networks. Furthermore, by extensively analysing each area, the article reports on conclusive findings and future research opportunities for IoT privacy and security for the next generation of network technologies.

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“…Experimental results show that the proposed approach by applying AdaBoost ensemble learning to the random forest classifier as the regular classifier achieves 92% F-measure and 95% TPR, and is superior to zero-day malware detection using only the top. Despite using a small number of microarchitectural features captured at run-time by existing HPCs, He et al [32] propose an ensemble learning-based technique to improve the performance of standard malware detectors. e experimental results show that using only the top four microarchitectural features, our proposed approach of using AdaBoost ensemble learning on the Random Forest classifier as a regular classifier achieves 92% F-measure and 95% TPR with only 2% false positive rate in detecting zero-day malware.…”

Section: Literature Surveymentioning

confidence: 99%

BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning

Chimeleze

Jamil

Ismail

et al. 2022

Security and Communication Networks

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Malware detection refers to the process of detecting the presence of malware on a host system, or that of determining whether a specific program is malicious or benign. Machine learning-based solutions first gather information from applications and then use machine learning algorithms to develop a classifier that can distinguish between malicious and benign applications. Researchers and practitioners have long paid close attention to the issue. Most previous work has addressed the differences in feature importance or the computation of feature weights, which is unrelated to the classification model used, and therefore, the implementation of a selection approach with limited feature hiccups, and increases the execution time and memory usage. BFEDroid is a machine learning detection strategy that combines backward, forward, and exhaustive subset selection. This proposed malware detection technique can be updated by retraining new applications with true labels. It has higher accuracy (99%), lower memory consumption (1680), and a shorter execution time (1.264SI) than current malware detection methods that use feature selection.

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When Machine Learning Meets Hardware Cybersecurity: Delving into Accurate Zero-Day Malware Detection

Cited by 26 publications

References 16 publications

Cyber Security Threats and Countermeasures using Machine and Deep Learning Approaches: A Survey

Cyber Security Threats and Countermeasures using Machine and Deep Learning Approaches: A Survey

Security and Privacy for Low Power IoT Devices on 5G and Beyond Networks: Challenges and Future Directions

BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning

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