الگوریتم‌های فراابتکاری برای انتخاب ویژگی در سیستم‌های تشخیص نفوذ: یک بررسی سیستماتیک
محورهای موضوعی : سامانه های پردازشی و ارتباطی چند رسانه ای هوشمند
یاشار پور اردبیل خواه
1
(دانشجوی دکتری، گروه مهندسی کامپیوتر، دانشکده فنی مهندسی، دانشگاه آزاد اسلامی، ساری، ایران)
میر سعید حسینی شیروانی
2
(استادیار، گروه مهندسی کامپیوتر، واحد ساری، دانشگاه آزاد اسلامی، ساری، ایران)
همایون مؤتمنی
3
(استاد، گروه مهندسی کامپیوتر، واحد ساری، دانشگاه آزاد اسلامی، ساری، ایران)
کلید واژه: انتخاب ویژگی, الگوریتم فراابتکاری, سیستم تشخیص نفوذ, رایانش ابری, بهینه‌سازی, امنیت شبکه,
چکیده مقاله :
با توجه به اهمیت روزافزون امنیت شبکه در محیط‌های پیچیده و پویا، سیستم‌های تشخیص نفوذ نقش بسیار مهمی در شناسایی و مقابله با تهدیدات امنیتی ایفامی‌کنند. با این حال، وجود حجم زیادی از داده‌ها در شبکه‌ها، کارایی سیستم‌های تشخیص نفوذ را تحت تأثیر قرارمی‌دهد. انتخاب ویژگی به عنوان یک مرحله حیاتی در پیش‌پردازش داده‌ها می‌تواند به بهبود دقت، سرعت و کارایی این سیستم‌ها کمک کند. این مقاله به بررسی سیستماتیک روش‌های انتخاب ویژگی مبتنی بر الگوریتم‌های فراابتکاری در سیستم‌های تشخیص نفوذ در محیط رایانش ابری می‌پردازد. روش کار شامل مرور جامعی بر تحقیقات انجام‌شده در حوزه انتخاب ویژگی برای سیستم‌های تشخیص نفوذ است. در این بررسی، الگوریتم‌های فراابتکاری مانند الگوریتم ژنتیک، بهینه‌سازی ازدحام ذرات ، بهینه‌سازی کلونی زنبور، الگوریتم خفاش، و سایر روش‌های بهینه‌سازی الهام گرفته از طبیعت، به طور کامل بررسی شده‌اند. این الگوریتم‌ها به دلیل توانایی‌شان در جستجوی فضای بزرگ ویژگی‌ها و شناسایی بهترین ترکیب‌ها برای بهبود عملکرد سیستم‌های تشخیص نفوذ، انتخاب شده‌اند. در این مقاله، مقایسه‌های دقیقی بین الگوریتم‌های مختلف از نظر معیارهای عملکردی همچون دقت تشخیص، نرخ هشدار غلط، زمان پردازش و تعداد ویژگی‌های انتخاب‌شده انجام شده است. همچنین، مجموعه داده‌های مختلفی در این زمینه، مورد بحث و بررسی قرار گرفته‌اند تا کارایی هر یک از روش‌ها در شرایط مختلف ارزیابی شود. علاوه بر این، چالش‌های کلیدی در این حوزه مورد شناسایی و تحلیل قرار گرفته‌اند. این چالش‌ها شامل مواردی همچون پیچیدگی محاسباتی بالا، مسئله سربار پردازشی در محیط‌های ابری، تعادل بین دقت و سرعت تشخیص، و مشکل تداخل ویژگی‌ها هستند. همچنین، شکاف‌های تحقیقاتی موجود در این زمینه که نیازمند تحقیقات بیشتر است، شناسایی شده‌اند. نتایج این بررسی نشان می‌دهد که هریک از الگوریتم‌های فراابتکاری دارای نقاط قوت و ضعف خاص خود هستند و انتخاب بهترین روش بستگی به نیازمندی‌ها و شرایط خاص سیستم دارد. این مقاله با ارائه توصیه‌هایی برای تحقیقات آینده، به عنوان یک راهنمای جامع برای پژوهشگران و توسعه‌دهندگان سیستم‌های تشخیص نفوذ در محیط‌های ابری مطرح می‌شود.
Introduction: Considering the increasing importance of network security in complex and dynamic environments, intrusion detection systems (IDS) play a very important role in identifying and dealing with security threats. However, the presence of a large amount of data in networks affects the efficiency of intrusion detection systems. Feature selection as a critical step in data preprocessing can help improve the accuracy, speed, and efficiency of these systems. This article deals with the systematic review of feature selection methods based on meta-heuristic algorithms in intrusion detection systems in the cloud computing environment.
Method: The methodology of this article includes a comprehensive review of the research conducted in the field of feature selection for IDS. In this review, meta-heuristic algorithms such as genetic algorithm, particle swarm optimization (PSO), bee colony optimization, bat algorithm, and other nature-inspired optimization methods are thoroughly reviewed. These algorithms are chosen due to their ability to search a large space of features and identify the best combinations to improve the performance of IDSs.
Evaluation: In this paper, detailed comparisons have been made between different algorithms in terms of performance criteria such as detection accuracy, false alarm rate, processing time and the number of selected features. Also, different data sets that have been used in this field have been discussed to evaluate the efficiency of each of the methods in different conditions.
Challenge: In addition, key challenges in this field have been identified and analyzed. These challenges include things such as high computational complexity, the problem of processing overhead in cloud environments, the balance between accuracy and detection speed, and the problem of feature interference. Also, research gaps in this field that require further research have been identified.
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