دانلود مقاله تکنیک های یادگیری ماشین برای امنیت اینترنت اشیا

خلاصه
1. معرفی
2. نظرسنجی های مرتبط
3. ML در امنیت اینترنت اشیا: مطالعات موردی
4. روش های تشخیص تهدیدات سایبری
5. طبقه بندی روش های یادگیری ماشین
6. چالش ها
7. چشم انداز آینده با هوش مصنوعی و LLM مولد
8. نتیجه گیری
اعلامیه منافع رقابتی
منابع

Abstract
1. Introduction
2. Related surveys
3. ML in IoT security: case studies
4. Cyber threats’ detection methods
5. Classification of machine learning methods
6. Challenges
7. Future vision with generative AI and LLMs
8. Conclusion
Declaration of competing interest
References

چکیده
علیرغم ارائه اتصال و راحتی بی نظیر، رشد تصاعدی اکوسیستم اینترنت اشیا (IoT) نگرانی های قابل توجهی در مورد امنیت سایبری ایجاد کرده است. این نگرانی‌ها از عوامل مختلفی ناشی می‌شوند، از جمله ناهمگونی دستگاه‌های IoT، استقرار گسترده و محدودیت‌های محاسباتی ذاتی. ادغام فناوری های نوظهور برای رسیدگی به این نگرانی ها با تکامل چشم انداز پویا اینترنت اشیا ضروری می شود. یادگیری ماشینی (ML)، یک فناوری به سرعت در حال پیشرفت، نوید قابل توجهی را در رسیدگی به مسائل امنیتی اینترنت اشیا نشان داده است. این به طور قابل توجهی بر تحقیقات در تشخیص تهدیدات سایبری تأثیر گذاشته و پیشرفت کرده است. این نظرسنجی مروری جامع از روندها، روش‌شناسی‌ها و چالش‌های فعلی در استفاده از یادگیری ماشین برای تشخیص تهدید سایبری در محیط‌های IoT ارائه می‌کند. به طور خاص، ما تجزیه و تحلیل مقایسه‌ای از پیشرفته‌ترین سیستم‌های تشخیص نفوذ مبتنی بر ML (IDS) را در چشم‌انداز امنیت اینترنت اشیا انجام می‌دهیم. علاوه بر این، ما مسائل و چالش‌های حل‌نشده فوری را در این زمینه پویا روشن کردیم. ما چشم انداز آینده را با هوش مصنوعی مولد و مدل های زبان بزرگ برای افزایش امنیت اینترنت اشیا ارائه می کنیم. این بحث ها درک عمیقی از روش های مختلف تشخیص تهدیدات سایبری ارائه می دهد و پایگاه دانش محققان و متخصصان را به طور یکسان تقویت می کند. این مقاله منبع ارزشمندی برای کسانی است که مشتاق هستند در دنیای در حال تکامل تشخیص تهدید سایبری با استفاده از امنیت ML و IoT تحقیق کنند.

Abstract

Despite providing unparalleled connectivity and convenience, the exponential growth of the Internet of Things (IoT) ecosystem has triggered significant cybersecurity concerns. These concerns stem from various factors, including the heterogeneity of IoT devices, widespread deployment, and inherent computational limitations. Integrating emerging technologies to address these concerns becomes imperative as the dynamic IoT landscape evolves. Machine Learning (ML), a rapidly advancing technology, has shown considerable promise in addressing IoT security issues. It has significantly influenced and advanced research in cyber threat detection. This survey provides a comprehensive overview of current trends, methodologies, and challenges in applying machine learning for cyber threat detection in IoT environments. Specifically, we further perform a comparative analysis of state-of-the-art ML-based Intrusion Detection Systems (IDSs) in the landscape of IoT security. In addition, we shed light on the pressing unresolved issues and challenges within this dynamic field. We provide a future vision with Generative AI and large language models to enhance IoT security. The discussions present an in-depth understanding of different cyber threat detection methods, enhancing the knowledge base of researchers and practitioners alike. This paper is a valuable resource for those keen to delve into the evolving world of cyber threat detection leveraging ML and IoT security.

Introduction

Brendan O'Brien astutely observed, ”If you think the Internet has changed your life, think again. The Internet of Things is about to change it all over again!” [1]. This is indeed the case, as the Internet of Things (IoT) has heralded unprecedented connectivity. The advancements in sensor technology, wireless communication, and data analytics have spurred an exponential increase in connected devices. This influx of connectivity, brought about by integrating IoT into various industries, cities, and households, promotes unmatched efficiency and convenience. As the backbone of IoT, sensors and actuators acquire and convert data from the physical world into digital signals. These compact devices amass a diverse range of data, thereby enabling real-time monitoring and control of numerous systems and processes.

However, the rapid proliferation and extensive integration of IoT devices into everyday life have ushered in various security challenges. These issues must be robustly addressed to ensure the safety and reliability of this expanding ecosystem. The sheer volume and variety of IoT devices and their often inconsistent security features and protocols engender a fragmented environment teeming with potential attack vectors. IoT devices frequently prioritize low cost and user simplicity over security, making them susceptible to breaches and exploitation. As a result, these devices are at risk of various cyber threats, including data breaches, Distributed Denial-of-Service (DDoS) attacks, and malware infections. Any security breach in these devices could significantly compromise privacy and crucial infrastructure systems, given the sensitive nature of the data they handle. Moreover, IoT devices, potentially serving as entry points, might allow attackers to infiltrate broader networks, amplifying the potential impact of security breaches. Another primary concern is the security of communication routes between IoT devices and networks, as many IoT devices utilize wireless communication protocols susceptible to interception or manipulation. These vulnerabilities can be exacerbated by the resource constraints of specific IoT devices, which prevent them from adopting contemporary encryption and authentication techniques. Furthermore, the long lifespan and widespread deployment of IoT devices compound the difficulty of managing security upgrades and patches, as many devices may not receive regular updates or may be difficult to access for maintenance. This could lead to an increased number of outdated or vulnerable devices, further exacerbating security concerns [2].

Conclusion

IoT devices have become fundamental to everyday life, offering increased connectivity and convenience. However, this accelerated development in IoT devices also brought several security challenges that need to be addressed to guarantee the safety and reliability of these interconnected systems. The survey was organized into five sections detailing the role of machine learning in enhancing IoT security. We have provided an overview of the current trends in ML for cyber threat detection in IoT environments. Furthermore, we survey recent cyber detection methods, define them, highlight the approach, and detail the attack surface utilized along with their evaluations. The utilized ML techniques are also discussed, defined, and compared regarding advantages and drawbacks in the relevant use cases. Moreover, open issues were discussed briefly, concluding that further research and development is required as no current solution can address the issues related to IoT cyber threat detection. A holistic strategy combining diverse techniques and strategies is required as a final recommendation. We present this survey as a reference for the current advancements in the field and point out the direction being taken. Our goal is for the survey to create a more secure and resilient IoT environment by identifying the current issues, understanding their root causes and possible implications, and developing innovative solutions.