Chapters authored
Machine Learning in Application Security
Security threat landscape has transformed drastically over a period of time. Right from viruses, trojans and Denial of Service (DoS) to the newborn malicious family of ransomware, phishing, distributed DoS, and so on, there is no stoppage. The phenomenal transformation has led the attackers to have a new strategy born in their attack vector methodology making it more targeted—a direct aim towards the weakest link in the security chain aka humans. When we talk about humans, the first thing that comes to an attacker's mind is applications. Traditional signature‐based techniques are inadequate for rising attacks and threats that are evolving in the application layer. They serve as good defences for protecting the organisations from perimeter and endpoint‐driven attacks, but what needs to be focused and analysed is right at the application layer where such defences fail. Protecting web applications has its unique challenges in identifying malicious user behavioural patterns being converted into a compromise. Thus, there is a need to look at a dynamic and signature‐independent model of identifying such malicious usage patterns within applications. In this chapter, the authors have explained on the technical aspects of integrating machine learning within applications in detecting malicious user behavioural pattern.
Part of the book: Advances in Security in Computing and Communications