Enhancing Cybersecurity in IoT: Defending Against MalwareAttacks through Machine Learning Enabled Detection andResponse

The rapid growth of the Internet of Things (IoT) has brought about numerous benefits and opportunities for seamless communication and data exchange. However, the increasing number of IoT devices has also introduced significant security challenges, making them attractive targets for cybercriminals. Among the various threats to IoT security, malware attacks pose a severe risk, compromising the functionality and security of IoT devices and users' data. Traditional security approaches, such as signature-based methods and rule-based systems, have proven inadequate in addressing the evolving and sophisticated nature of malware attacks. Therefore, there is a need for innovative and intelligent approaches to detect and respond to malware attacks effectively.This paper explores the role of machine learning in enhancing cybersecurity in IoT networks, with a specific focus on malware attack detection and response. Machine learning techniques, with their ability to learn patterns from vast amounts of data and make accurate predictions, have shown significant potential in various domains, including cybersecurity. By leveraging machine learning, IoT security systems can improve their effectiveness in detecting and mitigating malware attacks. The objectives of this study include reviewing existing literature on machine learning for IoT security, analyzing the vulnerabilities and potential impacts of malware attacks on IoT devices, exploring the application of machine learning techniques for detecting and responding to malware attacks, proposing a methodology for enhancing cybersecurity through machine learning-enabled detection and response, evaluating the effectiveness of the proposed approach through experiments, providing practical implications for industry practitioners and policymakers, and identifying future research directions.Overall, this paper contributes to the body of knowledge on cybersecurity in IoT by providing insights into the effective defense against malware attacks through machine learning-enabled detection and response mechanisms. The findings highlight the importance of adopting intelligent and adaptive techniques in IoT security to overcome the limitations of traditional approaches.

کلیدواژه ها:

Cybersecurity ، Internet of Things (IoT) ، Malware attacks ، Machine learning ، Detection and response mechanisms