Malware Detection Using Long Short Term Memory and Recurrent Neural Network

Malware is swiftly evolving making it one of the most prioritized problem of the internet. Because of the gravity that this problem possesses, it is very much essential to develop tools and software that can recognize malware, making them one of the most crucial tools in the current day market. Many industries have turned heads towards the field of machine learning to figure out solutions to various problems and cyber security is also one of such field wherein the concepts of machine learning can be incorporated to design tools that can figure out and differentiate between software that is normal and the one that is malware. Machine learning algorithms (MLAs) are now frequently used to analyze malware effectively because these new malwares are typically versions of previous malwares. In This Paper We are proposing a process to find the malware in the dataset. Following that, two deep learning techniques are utilized for detection: Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) are used to build a deep learning model for malware detection.