Enhancing Cybersecurity in IoT: Defending Against MalwareAttacks through Machine Learning Enabled Detection andResponse
- سال انتشار: 1402
- محل انتشار: هفتمین همایش بین المللی مهندسی فناوری اطلاعات، کامپیوتر و مخابرات ایران
- کد COI اختصاصی: ICTBC07_034
- زبان مقاله: انگلیسی
- تعداد مشاهده: 175
نویسندگان
Bachelor student of Computer Engineering, Pole Dokhtar Higher Education Institute, Lorestan, Iran
Master of Lorestan University, PHD in Computer Software, Lorestan, Iran
Department of Computer Scince, PHD in Computer Software, Najaf Abad, Isfahan, Iran
چکیده
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 mechanismsمقالات مرتبط جدید
- ارتقای امنیت و حریم خصوصی اینترنت اشیا: نقش انقلابی فناوری های بلاک چین
- طراحی متدولوژی های ترکیبی (Hybrid) در هوش مصنوعی برای حل مسائل پیچیده
- بهینه سازی سلسله مراتبی جریان ترافیک در محیط های شهری با استفاده از یک چارچوب ترکیبی مبتنی بر سیستم های چندعامله، یادگیری عمیق و یادگیری تقویتی
- بهینه سازی یادگیری فدرال در سیستم های رایانش لبه موبایل: مروری جامع بر چالش ها، راه حل ها و کاربردهای نوظهور
- فناوری اطلاعات و ارتباطات سبز
اطلاعات بیشتر در مورد COI
COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.
کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.