ANN Based Cyber Threat Detection Utilising Event Profiles

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Sowjanya , Saad Misri , Salman Ahmed , Syed Salman


The modern world is completely reliant on the internet for all aspects of daily life. With each passing day, the amount of time spent in the virtual world is increasing. Everyone in the world has money to spend more time than ever before is spent on the Internet. [7] Consequently, the dangers the number and severity of cyber-threats and -crimes are both rising. "Cyber" is a phrase used to describe "threat" refers to criminal behaviour that is carried out through the use of the Internet. The methods used by cybercriminals are evolving as a result of passing through the barrier is now possible. Conventional there is no method that can identify zero-day attacks. Attacks with a high degree of sophistication. [5] There has been a lot of machine learning so far. Detection methods for cybercrimes have been created a fight against cyber attacks. The purpose of this investigation presents an assessment of many commonly used machines. Gaining knowledge of detection methods for some very dangerous risks to cyberspace from cyber attacks. A basic machine learning framework consisting of three components the main focus of the research is on approaches, especially strong religious belief. a network, a decision tree, and an SVM As of right now, we don't have any examined the effectiveness of these for a brief period of time spam detection, intrusion detection, and other applications of machine learning Based on commonly used and known malware detection and prevention techniques datasets for comparison purposes.


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