- Title
- A survey on machine learning techniques for cyber security in the last decade
- Creator
- Shaukat, Kamran; Luo, Suhuai; Varadharajan, Vijay; Hameed, Ibrahim A.; Xu, Min
- Relation
- IEEE ACCESS Vol. 8, p. 222310-222354
- Publisher Link
- http://dx.doi.org/10.1109/ACCESS.2020.3041951
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- journal article
- Date
- 2020
- Description
- Pervasive growth and usage of the Internet and mobile applications have expanded cyberspace. The cyberspace has become more vulnerable to automated and prolonged cyberattacks. Cyber security techniques provide enhancements in security measures to detect and react against cyberattacks. The previously used security systems are no longer sufficient because cybercriminals are smart enough to evade conventional security systems. Conventional security systems lack efficiency in detecting previously unseen and polymorphic security attacks. Machine learning (ML) techniques are playing a vital role in numerous applications of cyber security. However, despite the ongoing success, there are significant challenges in ensuring the trustworthiness of ML systems. There are incentivized malicious adversaries present in the cyberspace that are willing to game and exploit such ML vulnerabilities. This paper aims to provide a comprehensive overview of the challenges that ML techniques face in protecting cyberspace against attacks, by presenting a literature on ML techniques for cyber security including intrusion detection, spam detection, and malware detection on computer networks and mobile networks in the last decade. It also provides brief descriptions of each ML method, frequently used security datasets, essential ML tools, and evaluation metrics to evaluate a classification model. It finally discusses the challenges of using ML techniques in cyber security. This paper provides the latest extensive bibliography and the current trends of ML in cyber security.
- Subject
- cyber security; deep learning; intrusion detection; malware; machine learning; spam
- Identifier
- http://hdl.handle.net/1959.13/1452506
- Identifier
- uon:44446
- Identifier
- ISSN:2169-3536
- Language
- eng
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