Abstract
The increased dependence of people on online services has led to a rapid increase in Cybercrime. Nowadays, each device is connected to the internet. Since every device connected to the web is vulnerable to cyber attack, securing these devices has become very crucial. The Advanced Persistent Threat (APT) is a novel techniques used by hackers. It is among the most alarming security threat. It can bypass all kinds of security appliances. The malware is becoming stronger and more redundant, which cause the victim’s system/network more damage. To prevent or mitigate such type of attacks, there is some prevention mechanism with the help of Machine Learning (ML) which would help to detect the advanced level threat. These Advanced level threats are much capable of hiding themselves from the firewall or any other defensive mechanism, of uncovering this advanced level threats there are some ML algorithms which would help to detect them with the low false positive rate and a higher level of accuracy. This paper mainly emphasizes on various Machine Learning algorithms and techniques that can be applied to detect Advanced Persistent Threats.
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Rajalakshmi, E., Asik Ibrahim, N., Subramaniyaswamy, V. (2019). A Survey of Machine Learning Techniques Used to Combat Against the Advanced Persistent Threat. In: Shankar Sriram, V., Subramaniyaswamy, V., Sasikaladevi, N., Zhang, L., Batten, L., Li, G. (eds) Applications and Techniques in Information Security. ATIS 2019. Communications in Computer and Information Science, vol 1116. Springer, Singapore. https://doi.org/10.1007/978-981-15-0871-4_12
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