A BLOCKCHAIN-BASED MULTIMODAL APPROACH TO MALWARE DETECTION IN ANDROID IOT ECOSYSTEMS

As the Internet of Things (IoT) continues to proliferate, the security challenges associated with interconnected devices have become increasingly complex. Malicious actors exploit vulnerabilities in IoT networks, leading to the need for robust solutions in malware detection and classification. This research proposes a novel approach by integrating block chain technology into the IoT security framework to enhance the efficiency and reliability of malware detection. The study investigates the limitations of traditional malware detection methods in IoT environments and explores the potential of block chain to address these challenges. Block chain’s decentralized and tamper-resistant nature provides a secure and transparent platform for recording and validating data transactions within the IoT ecosystem. By leveraging block chain, the research aims to establish a trustworthy and resilient infrastructure for detecting and classifying malware in real-time. Furthermore, the paper discusses the implementation of a block chain-based consensus mechanism to validate the integrity of data collected from IoT devices. This consensus model ensures the authenticity of information, reducing the risk of false positives or negatives in malware detection. Additionally, the study explores the use of smart contracts to automate and enforce security policies, enhancing the overall responsiveness of the system. The proposed approach not only contributes to the advancement of IoT security but also lays the foundation for a more collaborative and secure IoT ecosystem. The findings of this research have significant implications for industries relying on IoT technologies, emphasizing the importance of proactive measures to safeguard interconnected devices from evolving cyber threats.