Sandbox Environment for Real Time Malware Analysis of IoT Devices

The explosion in IoT devices’ growth becomes the primary target to attackers. It provides a large attack surface to attackers for Distributed Denial of Service (DDoS), Eavesdropping, Privilege Escalation, etc. With a lack of research in IoT security, there are lake solutions to analyze the advanced malware in a secure environment to understand IoT malware behavior. This paper has proposed a sandbox environment concept model that analyses malware, generates automated reports, and solves problems with the existing sandbox. Sandbox uses multiple machine-learning algorithms to analyze malware on three basic levels: static malware analysis, real-time malware analysis, and network
analysis. Then by consolidating the report from all this analysis, the sandbox environment generates the report. Static analysis is performed by collecting information from shared libraries, ELF, and other binary files using the Convolutional Neural Networks model generated automated analysis report.