Show simple item record

dc.contributor.authorShaukat, Kamran
dc.contributor.authorLuo, Suhuai
dc.contributor.authorVaradharajan, Vijay
dc.contributor.authorHameed, Ibrahim A.
dc.contributor.authorXu, Min
dc.identifier.citationIEEE Access. 2020, .en_US
dc.description.abstractPervasive 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.en_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.titleA Survey on Machine Learning Techniques for Cyber Security in the Last Decadeen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.source.journalIEEE Accessen_US

Files in this item


This item appears in the following Collection(s)

Show simple item record

Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal