تشخیص شبکه‌بات نظیربه‌نظیر با استفاده از روش یادگیری عمیق

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکترا، گروه مهندسی کامپیوتر، واحد شبستر، دانشگاه آزاد اسلامی، شبستر، ایران

2 تهران، اتوبان رسالت، خیابان هنگام، دانشگاه علم و صنعت ایران

3 استادیار، گروه مهندسی کامپیوتر، واحد شبستر، دانشگاه آزاد اسلامی، شبستر، ایران

چکیده

یک شبکه‌بات، شبکه­ای از رایانه‌های آلوده و دستگاه­های هوشمند بر روی اینترنت است که توسط مدیر‌بات بد­افزار از راه دور کنترل می‌شود تا فعالیت­های بدخواهانه مختلفی نظیر اجرای حملات منع خدمات، ارسال هرزنامه، سرقت کلیک و غیره را انجام دهند. زمانی­که مدیربات با بات‌های خود ارتباط برقرار می­کند، ترافیکی تولید می­کند که تجزیه و تحلیل این ترافیک برای شناسایی ترافیک شبکه­بات می­تواند یکی از عوامل تاثیر گذار برای سامانه­های تشخیص نفوذ باشد. در این مقاله، روش یادگیری عمیق با حافظه کوتاه‌مدت ماندگار (LSTM) جهت طبقه‌بندی فعالیت­های شبکه‌بات نظیر­به­نظیر پیشنهاد می­شود. رویکرد پیشنهادی بر اساس ویژگی­های بسته­های پروتکل­کنترل­انتقال بوده و کارایی روش با استفاده از دو مجموعه داده ISCX و ISOT ارزیابی می‌شود. نتایج آزمایش‌های انجام‌یافته، توانایی بالای رویکرد پیشنهادی برای شناسایی فعالیت­های شبکه‌بات نظیر­به­نظیر را بر اساس معیارهای ارزیابی نشان می­دهد. روش پیشنهادی نرخ دقت 65/99‌ درصد، نرخ صحت 32/96 درصد و نرخ بازخوانی 63/99 درصد را با نرخ مثبت کاذب برابر 67/0 ارائه می­کند.

کلیدواژه‌ها


عنوان مقاله [English]

P2P Botnet Detection Using Deep Learning Method

نویسندگان [English]

  • M. Asadi 1
  • S. Parsa 2
  • M. A. Jabreil Jamali 3
  • V. Majidnezhad 3
1 Ph.D. Student of Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar ------------------------------------------------------ Faculty member of of Department of Computer Engineering, Khamneh Branch,
2 Associate Professor, Department of Computer, Science and Technology University, Tehran, Iran
3 Assistant Professor, Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran
چکیده [English]

A Botnet is a set of infected computers and smart devices on the Internet that are controlled remotely by a Botmaster to perform various malicious activities like distributed denial of service attacks(DDoS), sending spam, click-fraud and etc. When a Botmaster communicates with its own Bots, it generates traffic that  analyzing this traffic to detect the traffic of the Botnet can be one of the influential factors for intrusion  detection systems (IDS). In this paper, the long short term memory (LSTM) method is proposed to classify P2P Botnet activities. The proposed approach is based on the characteristics of the transfer control protocol (TCP) packets and the performance of the method is evaluated using both ISCX and ISOT datasets. The experimental results show that our proposed approach has a high capability in identifying P2P network activities based on evaluation criteria. The proposed method offers a 99.65% precision rate, a 96.32% accuracy rate and a recall rate of 99.63% with a false positive rate (FPR) of 0.67%.
 

کلیدواژه‌ها [English]

  • Botnet
  • Botnet Detection
  • Deep Learning
  • Recurrent Neural Network (RNN)
  • Long Short Term Memory (LSTM)
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