Bibliographic Details
| Title: |
Cyber attack detection in network using ML. |
| Authors: |
Koppula, Neeraja1 (AUTHOR) kneeraja123@gmail.com, Rao, Koppula Srinivas2 (AUTHOR) ksreenu2k@gmail.com, Philip, Jeethu3 (AUTHOR) jeethu.philip@mlrinstitutions.ac.in |
| Source: |
AIP Conference Proceedings. 2023, Vol. 2492 Issue 1, p1-4. 4p. |
| Subject Terms: |
*CYBERTERRORISM, *DEEP packet inspection (Computer security), *INTERNET security, *MACHINE learning |
| Abstract: |
Cyber attacks are increasing in both frequently and in class throughout the years. It enhances the art of complexity and the ability for further development as well as the continuous implementation of defensive tactics. As the alarming growth of computer connections and as a result of the large number of computer-related applications has increased in recent years, the challenge of achieving cyber security has steadily increased. It also requires a proper protection system against various cyber-attacks. The known conventional methods like deep packet inspection and intrusion detection, are largely used and recommended, but these traditional methods are no longer adequate enough for upcoming security threats. This project inspects Machines Learning as an effective solution by testing its ability to distinguish malicious traffic present within a network. Algorithms like Random Forest, CNN, can acquire better accuracies in detecting a cyber attack. [ABSTRACT FROM AUTHOR] |
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| Database: |
Academic Search Complete |
