• M.Sai Swetha
• V.Sucharitha

The multiplication of the malware business is the consequence of the enormous volume of individual and classified data shared on individual and public organization. This has augmented the extent of the associations being helpless against malware - driven cybercrime. Such coordinated and dispersed digital assaults can think twice about secrecy, honesty and accessibility of any association's important information and assets. The endpoints (Work areas, PCs, Mobiles, Servers, and so forth) are more powerless and consequently for the most part focused on by the digital hoodlums. The point of this study is to decide the likelihood of such endpoints being impacted by network safety dangers, in light of specific attributes of the specific endpoint. Utilizing the AI methods applied in this review, such as missing information examination and attribution (Numerous Attribution), outfit learning calculations (Stowing and Supporting), it tends to be anticipated what gadgets/frameworks in an association are probably going to be contaminated by malwares, ransomwares or other such dangers.In view of such discoveries, proactive measures can be taken, and digital protection methodologies can be concocted which can assist associations with forestalling misfortunes as much as of millions of dollars and become digital versatile.

Cyber Threat Identification And Visualization Using Machine Learning

"Cyber Threat Identification And Visualization Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.487-497, June-2024, Available :http://www.jetir.org/papers/JETIRGL06081.pdf

"Cyber Threat Identification And Visualization Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp487-497, June-2024, Available at : http://www.jetir.org/papers/JETIRGL06081.pdf