- Title
- A survey on representation learning efforts in cybersecurity domain
- Creator
- Usman, Muhammad; Jan, Mian; He, Xiangjian; Chen, Jinjun
- Date
- 2020
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/185640
- Identifier
- vital:16722
- Identifier
-
https://doi.org/10.1145/3331174
- Identifier
- ISBN:0360-0300
- Abstract
- In this technology-based era, network-based systems are facing new cyber-attacks on daily bases. Traditional cybersecurity approaches are based on old threat-knowledge databases and need to be updated on a daily basis to stand against new generation of cyber-threats and protect underlying network-based systems. Along with updating threat-knowledge databases, there is a need for proper management and processing of data generated by sensitive real-time applications. In recent years, various computing platforms based on representation learning algorithms have emerged as a useful resource to manage and exploit the generated data to extract meaningful information. If these platforms are properly utilized, then strong cybersecurity systems can be developed to protect the underlying network-based systems and support sensitive real-time applications. In this survey, we highlight various cyber-threats, real-life examples, and initiatives taken by various international organizations. We discuss various computing platforms based on representation learning algorithms to process and analyze the generated data. We highlight various popular datasets introduced by well-known global organizations that can be used to train the representation learning algorithms to predict and detect threats. We also provide an in-depth analysis of research efforts based on representation learning algorithms made in recent years to protect the underlying network-based systems against current cyber-threats. Finally, we highlight various limitations and challenges in these efforts and available datasets that need to be considered when using them to build cybersecurity systems.
- Publisher
- Baltimore: ACM
- Relation
- ACM computing surveys Vol. 52, no. 6 (2020), p. 1-28
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright ACM
- Subject
- Algorithms; Computer security; Computing; Cyber-attacks; Cybersecurity; Datasets; International organizations; Machine learning; Platforms; Real time; Representation learning; Representations; Threat evaluation; Upgrading; 46 Information and computing sciences
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