Criminals are working day and night to get hold of the data. They are also
getting more intelligent and are also using AI-powered threats to exploit vulnerabilities
to perform an attack. Information security is at a higher risk, now more than ever. Due
to the popularity of internet usage by users, the IT infrastructure is prone to security
threats. The damage done by computer malware and viruses is known to cost billions of
US dollars. Hence, this paper reviews the ways of integrating technology such as
machine learning, neural networks, deep learning, etc. which can help to develop an
intelligent system to protect and prevent the IT infrastructure from security threats. The
authors proposed AIVA, a Machine learning (ML) based detection system which is
able to classify a suspicious object as “safe” or “dangerous”. AIVA is composed of
three core components: static analysis, machine learning, and malicious detection.
Keywords: Confusion matrix, Machine learning, Malware analysis, Portable executable (PE) file.
