Robust Intelligent Malware Detection Using Deep Learning

Abstract: In the modern era of technology, malicious software, or malware, holds a serious security hazard as computer users, businesses, and governments see an uptick in malware attacks. In attempts to identify unknown malware, current malware detection solutions use dynamic as well as static examination of malware signatures and behavior patterns, which takes time and is unsuccessful. Modern malware employs evasive strategies such as metamorphosis and polymorphism to rapidly alter its actions and produce a multitude of variants. Machine learning algorithms (MLAs) are being used more and more to do an efficient malware analysis because new malware is primarily versions of current malware. Extensive feature engineering, feature learning, and feature representation are needed for this. It is likely to fully eliminate the feature engineering stage by utilizing sophisticated MLAs like deep learning. Even though there have been a few fresh investigations in the field, the algorithms' performance is skewed by the training set. It is a prerequisite to reduce bias and figure out these techniques holistically in order to develop new, improved techniques for successful zero-day malware detection. This paper fills a vacuum in the literature by comparing and contrasting deep learning architectures with standard MLAs for malware detection, classification, and categorization using public and private datasets. The public and private dataset’s train and test splits, which were gathered during distinctly different periods, are not connected to one another in the experimental study. Furthermore, we provide a new method of image processing with ideal parameters for deep learning architectures and MLAs. In response to a thorough scientific assessment of these methodologies, deep learning architectures perform more efficiently than traditional MLAs. All in all, our work suggests a scalable and multimodal deep learning system for real-time malware detection through visual means. An improved technique for successful zero-day malware detection is the visualization and deep learning architectures for static, dynamic, and image processing based blended methods in a big data environment.