Volume 20 No 22 (2022)
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Using machine learning techniques, Cyber Attack detection: A Review
Masrath Parveen, Dr. Saurabh Pal, Dr. Venkateswara Rao CH
Data transmission and reception on all networks currently experience network congestion issues. Due to different cyber attacks, this data flow is moving slowly for a variety of reasons. The server system is harmed by the internal workings of the cyberattacks. Operating a system while a network is experiencing cyber attacks is particularly dangerous. The survey on cyberattack detection utilising machine learning techniques, the Jupiter simulation tool, and the WEKA simulation tool is presented in this work. The several types of cyberattacks mentioned in this study include DOS, TCP/P, flood, UDP, ICMP, U2R, R2L, DDOS, probing assaults, along with detection using novel approaches and machine learning techniques. While data mining techniques work with specialised data sets and the identification of cyberattacks, machine learning approaches work with generalised data sets. Along with the simulation method, the goals of cyberattacks detection and classifications are also elaborated.
Network security, Cyberattacks, cyber serucity, machine learning, attacks
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