تشخیص نفوذ در شبکه‌های رایانه‌ای با استفاده از انتخاب ویژگی ترکیبی مؤثر مبتنی بر روش اشتراک‌گیری اطلاعات متقابل، آزمون F تحلیل واریانس و الگوریتم ژنتیک

نوع مقاله : کامپیوتر - محاسبات نرم و هوش مصنوعی

نویسندگان

1 استادیار، گروه مطالعات علم و فناوری دانشگاه فرماندهی و ستاد آجا، تهران، ایران

2 دانشیار، گروه مهندسی برق دانشگاه علوم و فنون هوایی شهید ستاری، تهران، ایران

چکیده

سامانه تشخیص نفوذ (IDS) حجم عظیمی از داده‌ها را مدیریت می‌کند که شامل ویژگی‌های نامرتبط و زائد است که منجر به مصرف منابع قابل توجه، روندهای آموزش و آزمایش طولانی مدت و نرخ تشخیص پایین می‌شود. از این رو، انتخاب ویژگی یک گام مهم در تشخیص نفوذ در نظر گرفته شده است. هدف این پژوهش، معرفی یک راهبرد مبتنی بر اشتراک است که به‌طور بهینه ویژگی‌ها را برای طبقه‌بندی انتخاب می‌کند. این انتخاب ویژگی شامل اشتراک‌گیری از روش‌های اطلاعات متقابل بر اساس مدل انتقال (MIT-MIT)، آزمون F تحلیل واریانس و الگوریتم ژنتیک (GA) است. یک مجموعه داده معیار، به نام NSL-KDD، برای ارزیابی اثربخشی رویکرد پیشنهادی استفاده می‌شود. این مطالعه شامل صحت، دقت، یادآوری و امتیاز F1  به‌عنوان معیارهای ارزیابی برای IDS است که روش پیشنهادی را با طبقه‌بندی کننده‌های پیشرفته تحلیل می‌کند. نتایج ارزیابی تأیید کرده است که الگوریتم انتخاب ویژگی ما ویژگی‌های ضروری‌تری را برای IDS جهت دستیابی به دقت بالا فراهم می‌نماید و از سایر الگوریتم‌های مقایسه‌ای برتری می‌جوید.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Network Intrusion Detection in Computer Networks Using an Efficacious Combined Feature Selection Technique Based on the Intersection Method of Mutual Information, Anova F-Test and Genetic Algorithm

نویسندگان [English]

  • Jalil Mazloum 1
  • hamid bigdeli 2
1 Department of Electrical Engineering, Shahid Sattari Aeronautical University of Science and Technology
2 AJA Command & Staff University
چکیده [English]

The intrusion detection system (IDS) manages a massive volume of data that comprises irrelevant and redundant features, leading to more significant resource consumption, long-time training and testing procedures, and low detection rate. Hence, feature selection is a crucial phase in intrusion detection. The aim of this paper is to introduce an intersection-based strategy that optimally selects the features for classification. This feature selection involves an intersection of simultaneous mutual information based on the transductive model (MIT-MIT), Anova F-test, and genetic algorithm (GA) methods. A benchmark dataset, named NSL-KDD, is applied to evaluate the effectiveness of the proposed approach. This study includes accuracy, precision, recall, and F1 score as the evaluation metrics for IDS, which analyzes the proposed method with state-of-the-art classifiers. The evaluation results confirm that our feature selection algorithm provides more essential features for IDS to achieve high accuracy, outperforming other comparative algorithms.

کلیدواژه‌ها [English]

  • Intrusion Detection System
  • Feature Selection
  • Mutual Information
  • Anova F-Test
  • Genetic Algorithm

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مراجع

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سابقه مقاله

  • تاریخ دریافت: 02 اسفند 1400
  • تاریخ بازنگری: 28 تیر 1401
  • تاریخ پذیرش: 16 مرداد 1401
  • تاریخ انتشار: 31 شهریور 1401

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