| Title | Cyber Threat Detection Using Machine Learning Techniques: A Performance Evaluation Perspective |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Shaukat, Kamran, Luo, Suhuai, Chen, Shan, Liu, Dongxi |
| Conference Name | 2020 International Conference on Cyber Warfare and Security (ICCWS) |
| Keywords | Computer crime, cyber threat, Cybercrime, Cyberspace, Decision trees, intrusion detection system, machine learning, Machine Learning Application, Malware, malware detection, Measurement, performance evaluation, privacy, pubcrawl, spam classification, Support vector machines, threat vectors, unsolicited e-mail |
| Abstract | The present-day world has become all dependent on cyberspace for every aspect of daily living. The use of cyberspace is rising with each passing day. The world is spending more time on the Internet than ever before. As a result, the risks of cyber threats and cybercrimes are increasing. The term `cyber threat' is referred to as the illegal activity performed using the Internet. Cybercriminals are changing their techniques with time to pass through the wall of protection. Conventional techniques are not capable of detecting zero-day attacks and sophisticated attacks. Thus far, heaps of machine learning techniques have been developed to detect the cybercrimes and battle against cyber threats. The objective of this research work is to present the evaluation of some of the widely used machine learning techniques used to detect some of the most threatening cyber threats to the cyberspace. Three primary machine learning techniques are mainly investigated, including deep belief network, decision tree and support vector machine. We have presented a brief exploration to gauge the performance of these machine learning techniques in the spam detection, intrusion detection and malware detection based on frequently used and benchmark datasets. |
| DOI | 10.1109/ICCWS48432.2020.9292388 |
| Citation Key | shaukat_cyber_2020 |
Cyber Threat Detection Using Machine Learning Techniques: A Performance Evaluation Perspective