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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2302.12415 |
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| _version_ | 1866910349843759104 |
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| author | Mohammed, Khatoon |
| author_facet | Mohammed, Khatoon |
| contents | As cyber attacks continue to increase in frequency and sophistication, detecting malware has become a critical task for maintaining the security of computer systems. Traditional signature-based methods of malware detection have limitations in detecting complex and evolving threats. In recent years, machine learning (ML) has emerged as a promising solution to detect malware effectively. ML algorithms are capable of analyzing large datasets and identifying patterns that are difficult for humans to identify. This paper presents a comprehensive review of the state-of-the-art ML techniques used in malware detection, including supervised and unsupervised learning, deep learning, and reinforcement learning. We also examine the challenges and limitations of ML-based malware detection, such as the potential for adversarial attacks and the need for large amounts of labeled data. Furthermore, we discuss future directions in ML-based malware detection, including the integration of multiple ML algorithms and the use of explainable AI techniques to enhance the interpret ability of ML-based detection systems. Our research highlights the potential of ML-based techniques to improve the speed and accuracy of malware detection, and contribute to enhancing cybersecurity |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_12415 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Harnessing the Speed and Accuracy of Machine Learning to Advance Cybersecurity Mohammed, Khatoon Cryptography and Security As cyber attacks continue to increase in frequency and sophistication, detecting malware has become a critical task for maintaining the security of computer systems. Traditional signature-based methods of malware detection have limitations in detecting complex and evolving threats. In recent years, machine learning (ML) has emerged as a promising solution to detect malware effectively. ML algorithms are capable of analyzing large datasets and identifying patterns that are difficult for humans to identify. This paper presents a comprehensive review of the state-of-the-art ML techniques used in malware detection, including supervised and unsupervised learning, deep learning, and reinforcement learning. We also examine the challenges and limitations of ML-based malware detection, such as the potential for adversarial attacks and the need for large amounts of labeled data. Furthermore, we discuss future directions in ML-based malware detection, including the integration of multiple ML algorithms and the use of explainable AI techniques to enhance the interpret ability of ML-based detection systems. Our research highlights the potential of ML-based techniques to improve the speed and accuracy of malware detection, and contribute to enhancing cybersecurity |
| title | Harnessing the Speed and Accuracy of Machine Learning to Advance Cybersecurity |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2302.12415 |