شبکه عصبی عمیق ترکیبی بهینه ادغام شده با انتخاب ویژگی برای سامانه تشخیص نفوذ در حملات سایبری

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار، دانشکده مهندسی برق، دانشگاه علوم و فنون هوایی شهید ستاری، تهران، ایران

2 استادیار، دانشگاه فرماندهی و ستاد آجا، تهران، ایران

چکیده

امروزه در عصر دیجیتال، از آنجا که مسائل امنیتی و حملات سایبری، حریم اطلاعات ایمن و حیاتی سازمان‌ها یا افراد را مختل می‌کنند، بسیار جدی و لازم توجه به شمار می‌روند. بنابراین، تشخیص به موقع این آسیب‌‌ها از طرف نفوذگران ضروری است، به‌طوری که سنگ‌بنای امنیت تحت عنوان سیستم تشخیص نفوذ (IDS)، حریم خصوصی داده‌های کاربر را حفظ نماید. از طرف دیگر، همراه با پیشرفت سریع روش‌های یادگیری ماشین (ML) و یادگیری عمیق (DL) در دنیای داده، یکی از کاربردهای مهم آن‌ها در زمینه IDS با استفاده از الگوریتم‌های طبقه‌بندی پیشرفته است که در سال‌های اخیر موضوع تحقیقات متعددی جهت افزایش دقت و قابلیت اطمینان بوده است. در نتیجه، این مقاله یک مدل ترکیبی IDS را ارائه می‌کند که به ادغام انتخاب ویژگی، طبقه‌بندی و بهینه‌سازی هایپرپارامترها پرداخته است. ابتدا، ویژگی‌های انبوه اولیه به طور جداگانه به روش‌های اطلاعات متقابل اصلاح‌شده (MMI)، الگوریتم ژنتیک (GA)، و آزمون F تحلیل واریانس وارد می‌شوند و پس از آن، اشتراک‌گیری از خروجی آن‌ها به‌عنوان ویژگی‌های نهایی مؤثر و کاهش‌یافته صورت می‌پذیرد. در ادامه، یک طبقه‌بند ترکیبی CNN و LSTM (CNN-LSTM) به کار گرفته می‌شود که هایپرپارامترهای آن به‌جای روش سعی و خطای زمان‌بر دستی، توسط یک الگوریتم بهینه‌سازی به نام گرگ خاکستری - نهنگ با جابه‌جایی تصادفی (RS-GWO-WOA) تعیین خواهد شد. نهایتاً، به‌منظور تجزیه‌وتحلیل طرح پیشنهادی، مقایسه‌ای با سایر روش‌ها از نظر صحت، دقت، یادآوری، امتیاز F1 و مدت‌زمان در مجموعه‌داده NSL-KDD انجام شده است که برتری رویکرد توسعه‌یافته را تأیید می‌نماید.

کلیدواژه‌ها

عنوان مقاله [English]

An Optimized Compound Deep Neural Network Integrating With Feature Selection for Intrusion Detection System in Cyber Attacks

نویسندگان [English]

  • Jalil Mazloum 1
  • Hamid Bigdeli 2
1 Associate Professor, Faculty of Electrical Engineering, Shahid Sattari University of Aeronautical Sciences and Technology, Tehran, Iran
2 Assistant Professor, Command University and Aja Headquarters, Tehran, Iran
چکیده [English]

In today's digital era, security issues and cyber attacks have become a serious and attention-needed concern as they hamper secured and vital information relating to organizations or individuals. Accordingly, timely detection of these vulnerabilities made by intruders is essential, wherein the cornerstone of security ensures the user's data privacy as an intrusion detection system (IDS). On the other hand, with the rapid development of machine learning (ML) and deep learning (DL) methods in the data world, one of their significant applications is dedicated to IDS using state-of-the-art classification algorithms, which has been the subject of numerous research to enhance accuracy and reliability in recent years. As a consequence, this paper presents a hybrid model integrating feature selection, classification, and hyper-parameters optimization. First, the initial massive features are subjected separately to the modified mutual information (MMI), genetic algorithm (GA), and Anova F-value approaches, followed by extracting the common outputs as optimal and reduced final features. Subsequently, a compound CNN and LSTM classifier (CNN-LSTM) is employed, where its hyper-parameters will be determined through a random switch grey wolf-whale optimization algorithm (RS-GWO-WOA) instead of a time-consuming trial and error manual process. Ultimately, to analyze the suggested scheme, a comparison with other strategies in terms of accuracy, precision, recall, F1 score, and periods of time on the NSL-KDD dataset has been accomplished, confirming the superiority of the developed approach.

کلیدواژه‌ها [English]

  • Intrusion Detection System
  • Feature Selection
  • Hyper-parameter Optimization
  • Mutual Information
  • Genetic Algorithm
  • Anova F-value
  • Grey Wolf Optimization Algorithm
  • Whale Optimization Algorithm

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مراجع

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پدافند الکترونیکی و سایبری
دوره 10، شماره 4 - شماره پیاپی 40
شماره پیاپی 40، فصلنامه زمستان
بهمن 1401
صفحه 41-51

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سابقه مقاله

  • تاریخ دریافت: 14 دی 1400
  • تاریخ بازنگری: 01 اسفند 1400
  • تاریخ پذیرش: 18 مرداد 1401
  • تاریخ انتشار: 01 بهمن 1401

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