A Malware Distribution Simulator for the Verification of Network Threat Prevention Tools

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 21; p. 6983
• Main Authors: Hwang, Song-Yi, Kim, Jeong-Nyeo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 21.10.2021; MDPI
• Subjects: Access control; Algorithms; Control equipment; Denial of service attacks; diffusion; Infrastructure; Internet of Things; IoT malware; Malware; Packets (communication); Prevention; Propagation; Security management; Servers; Simulation; Threats; verification
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21216983

With the expansion of the Internet of Things (IoT), security incidents about exploiting vulnerabilities in IoT devices have become prominent. However, due to the characteristics of IoT devices such as low power and low performance, it is difficult to apply existing security solutions to IoT devices. As a result, IoT devices have easily become targets for cyber attackers, and malware attacks on IoT devices are increasing every year. The most representative is the Mirai malware that caused distributed denial of service (DDoS) attacks by creating a massive IoT botnet. Moreover, Mirai malware has been released on the Internet, resulting in increasing variants and new malicious codes. One of the ways to mitigate distributed denial of service attacks is to render the creation of massive IoT botnets difficult by preventing the spread of malicious code. For IoT infrastructure security, security solutions are being studied to analyze network packets going in and out of IoT infrastructure to detect threats, and to prevent the spread of threats within IoT infrastructure by dynamically controlling network access to maliciously used IoT devices, network equipment, and IoT services. However, there is a great risk to apply unverified security solutions to real-world environments. In this paper, we propose a malware simulation tool that scans vulnerable IoT devices assigned a private IP address, and spreads malicious code within IoT infrastructure by injecting malicious code download command into vulnerable devices. The malware simulation tool proposed in this paper can be used to verify the functionality of network threat detection and prevention solutions.