Portable Executable Header Based Ransomware Detection using Power Iteration and Artificial Neural Network

In the present world, the dependency on different devices connected to the internet is increasing at a rapid rate day by day. Devices like smart watches, mobile phones, personal computers, etc., are part of our day-to-day life. We rely on them for most of our daily tasks. Since these devices are frequently used, they contain users’ personal information and other essential data. More and more people use the internet due to emerging technology and intelligent devices, increasing the risk of misusing confidential information and other user-specific crucial data. With the development of cryptocurrency, Ransomware is one of the emerging attacks that prevent authentic users from accessing systems, resources, or data and enables adversaries to control access to such information. This paper presents an Artificial Neural Network (ANN) based model that uses the “Power Iteration” method and Portable Executable (PE) Headers to detect various types of Ransomware. We analyze the performance of the proposed model by experimenting with a dataset created using the PE files collected from multiple sources and demonstrate its better detection capability.