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ارائه مدل جامع نیمه نظارتی تشخیص نفوذ مشارکتی مبتنی بر نمایه‌سازی رفتار شبکه با استفاده از مفهوم یادگیری عمیق و همبسته‌سازی فازی هشدارها | ||
| پدافند الکترونیکی و سایبری | ||
| دوره 9، شماره 3 - شماره پیاپی 35، آذر 1400، صفحه 165-186 اصل مقاله (1.88 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| نویسندگان | ||
| جواد وحیدی* 1؛ بهروز مینایی2؛ محمد احمدزاده3؛ علیرضا پور ابراهیمی4 | ||
| 1استادیار، دانشگاه علم و صنعت، تهران، ایران | ||
| 2دانشیار، دانشگاه علم و صنعت، تهران، ایران | ||
| 3دنشجوی دکتری، گروه مدیریت فناوری اطلاعات، دانشکده مدیریت و اقتصاد، دانشگاه آزاد اسلامی واحد علوم و تحقیقات، تهران، ایران | ||
| 4استادیار، دانشگاه آزاد اسلامی واحد کرج، کرج، ایران | ||
| تاریخ دریافت: 21 اسفند 1399، تاریخ بازنگری: 17 اردیبهشت 1400، تاریخ پذیرش: 18 خرداد 1400 | ||
| چکیده | ||
| امروزه سیستمهای تشخیص نفوذ اهمیت فوق العاده ای در تامین امنیت رایانه‌ها و شبکه‌های رایانه ای بر عهده دارند سیستمهای همبسته ساز در کنار سیستمهای تشخیص نفوذ قرار گرفته و با تحلیل و ترکیب هشدارهای دریافتی ازآن‌ها گزارش‌های مناسب برای بررسی و انجام اقدامات امنیتی تولید مینمایند یکی از مشکلاتی که سیستمهای تشخیص نفوذ با آن روبرو هستند، تولید حجم زیادی از هشدارهای غلط است، بنابراین یکی از مهمترین مسائل در سیستمهای همبسته ساز، وارسی هشدارهای دریافت شده از سیستم تشخیص نفوذ به منظور تشخیص هشدارهای مثبت کاذب از هشدارهای مثبت صحیح میباشد در این مقاله یک مدل جامع و کاربردی ارائه شده است که شامل یک سیستم تشخیص نفوذ ترکیبی برای وارسی جریان ترافیک بصورت برخط و یک سیستم همبسته ساز مبتنی بر یادگیری افزایشی برای وارسی هشدارها با کمک یادگیری فعال می باشد. تمرکز اصلی این پژوهش بر روی بهینه سازی کاربردی روشهای دسته‌بندی به منظور کاهش هزینه سازمانها و زمان متخصص امنیت برای در وارسی هشدارها میباشد. روش ارائه شده روی چند مجموعه داده تست معتبر آزمایش شده و نتایج حاصل بیانگر کارآمدی مدل پیشنهادی با دقت بالای 99 درصد و با نرخ مثبت کاذب بسیار پایین میباشد. | ||
| کلیدواژه‌ها | ||
| : سیستم تشخیص نفوذ مشارکتی؛ همبسته ساز؛ یادگیری افزایشی؛ یادگیری فعال؛ یادگیری برخط | ||
| عنوان مقاله [English] | ||
| A Comprehensive Semi-Suprvised Model for Collaborative Intrusion Detection Based on Network Behavior Profiling Using The Concept of Deep Learning and Fuzzy Correlation of Alerts along | ||
| نویسندگان [English] | ||
| javad vahidi1؛ b m2؛ mohammad ahmadzadeh3؛ a p4 | ||
| 1Assistant Professor, University of Science and Technology, Tehran, Iran | ||
| 2Associate Professor, University of Science and Technology, Tehran, Iran | ||
| 3PhD Student, Department of Information Technology Management, Faculty of Management and Economics, Islamic Azad University, Science and Research Branch, Tehran, Iran | ||
| 4Assistant Professor, Islamic Azad University, Karaj Branch, Karaj, Iran | ||
| چکیده [English] | ||
| Today, intrusion detection systems are extremely important in securing computers and computer networks. Correlated systems are next to intrusion detection systems by analyzing and combining the alarms received from them, appropriate reports for review and producing security measures. One of the problems face intrusion detection systems is generating a large volume of false alarms, so one of the most important issues in correlated systems is to check the alerts received by the intrusion detection system to distinguish true-positive alarms from false-positive alarms. The main focus of this research is on the applied optimization of classification methods to reduce the cost of organizations and security expert time in alert checking. The proposed Incrimental Intrusion Detetection Model using Correlator (IIDMC) is tested on a valid test dataset and the results show the efficiency of the proposed model and consequently its high accuracy. | ||
| کلیدواژه‌ها [English] | ||
| : Intrusion etection, Fuzzy Correlator, Incremental Online Learning, Active Learnin | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 473 تعداد دریافت فایل اصل مقاله: 338 |
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