Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

Energies

Dongxi Liu¹  Jiaming Li¹  IbrahimA. Hameed²  Shan Chen³  Kamran Shaukat³  Suhuai Luo³  Vijay Varadharajan³ 

[1] Data61, Commonwealth Scientific and Industrial Research Organization, Canberra 3169, Australia;Department of ICT and Natural Sciences, Norwegian University of Science and Technology, 7491 Trondheim, Norway;School of Electrical Engineering and Computing, The University of Newcastle, Newcastle 2308, Australia;

关键词: cybersecurity;    machine learning;    malware detection;    intrusion detection system;    spam classification;   

DOI  :  10.3390/en13102509

来源: DOAJ

【 摘 要 】

Cyberspace has become an indispensable factor for all areas of the modern world. The world is becoming more and more dependent on the internet for everyday living. The increasing dependency on the internet has also widened the risks of malicious threats. On account of growing cybersecurity risks, cybersecurity has become the most pivotal element in the cyber world to battle against all cyber threats, attacks, and frauds. The expanding cyberspace is highly exposed to the intensifying possibility of being attacked by interminable cyber threats. The objective of this survey is to bestow a brief review of different machine learning (ML) techniques to get to the bottom of all the developments made in detection methods for potential cybersecurity risks. These cybersecurity risk detection methods mainly comprise of fraud detection, intrusion detection, spam detection, and malware detection. In this review paper, we build upon the existing literature of applications of ML models in cybersecurity and provide a comprehensive review of ML techniques in cybersecurity. To the best of our knowledge, we have made the first attempt to give a comparison of the time complexity of commonly used ML models in cybersecurity. We have comprehensively compared each classifier’s performance based on frequently used datasets and sub-domains of cyber threats. This work also provides a brief introduction of machine learning models besides commonly used security datasets. Despite having all the primary precedence, cybersecurity has its constraints compromises, and challenges. This work also expounds on the enormous current challenges and limitations faced during the application of machine learning techniques in cybersecurity.

【 授权许可】

Unknown