This paper focuses on Security and Privacy for Small

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 atackers to have a new strategy born in their atack 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 irst thing that comes to an atacker's mind is applications. Traditional signature-based techniques are inadequate for rising atacks and threats that are evolving in the application layer. They serve as good defences for protecting the organisations from perimeter and endpoint-driven atacks, 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 paterns being converted into a compromise. Thus, there is a need to look at a dynamic and signature-independent model of identifying such malicious usage paterns within applications. In this chapter, the authors have explained on the technical aspects of integrating machine learning within applications in detecting malicious user behavioural patern.