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انتخاب ویژگی و تشخیص نفوذ در شبکههای حسگر بی سیم با استفاده از یادگیری ماشین مفرط بدون نظارت (UELM) | ||
پدافند غیرعامل | ||
دوره 15، شماره 4 - شماره پیاپی 60، آذر 1403، صفحه 25-40 | ||
نوع مقاله: مقاله پژوهشی | ||
نویسندگان | ||
حمید طباطبایی* 1؛ سمیرا هادوی2 | ||
1دانشیار گروه مهندسی کامپیوتر، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران | ||
2کارشناسی ارشد گروه مهندسی کامپیوتر، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران | ||
تاریخ دریافت: 18 دی 1402، تاریخ بازنگری: 15 فروردین 1403، تاریخ پذیرش: 01 مهر 1403 | ||
چکیده | ||
امروزه سیستمهای کامپیوتری مبتنی بر شبکه، نقش حیاتی در جامعه مدرن امروزی دارند و به همین علت ممکن است هدف دشمن و یا نفوذ قرار گیرند. بهمنظور ایجاد امنیت کامل در یک سیستم کامپیوتری متصل به شبکه، استفاده از دیوار آتش و سایر مکانیزمهای جلوگیری از نفوذ همیشه کافی نیست و باید از سیستمهای دیگری به نام سیستمهای تشخیص نفوذ استفاده شود. بهدلیل وجود مشخصههای زیاد در دادههای مربوط به سیستمهای تشخیص نفوذ، جهت استفاده از مشخصههای مطلوب و موثر از الگوریتم یادگیری ماشین مفرط بدون نظارت استفاده میشود. جهت طبقهبندی دادهها از مدل UELM و ارزیابی عملکرد روش پیشنهادی، از پایگاه داده با رکوردهای واقعی تر NSL-KDD نسبت به سایر مجموعه دادگان تشخیص نفوذ، استفاده میگردد. نتایج آزمایشها نشاندهنده صحت 38/98 UELM در مقایسه با صحت 74/93 GWO است. دلیل این برتری، استفاده ازمدل مناسب در مسئله دستهبندی، تشخیص نفوذ، ساختار مستحکم و تعمیمپذیر شبکه عصبی بدون نظارت می باشد. | ||
کلیدواژهها | ||
انتخاب ویژگی؛ شبکههای عصبی مصنوعی؛ ماشین یادگیری مفرط بدون نظارت؛ تشخیص نفوذ | ||
عنوان مقاله [English] | ||
Feature selection and intrusion detection in wireless sensor networks with Unsupervised Extreme Learning Machine (UELM) | ||
نویسندگان [English] | ||
Hamid Tabatabaee1؛ samira hadavi2 | ||
1Associate Professor, Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran | ||
2Master of Science, Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran | ||
چکیده [English] | ||
Nowadays, network-based computer systems play a vital role in today's modern society, and for this reason, they may be the target of hostility or infiltration. In order to ensure complete security in a computer system connected to the network, using a firewall and other intrusion prevention mechanisms is not always enough. This need has led to the use of other systems called intrusion detection systems. An intrusion detection system can be considered a set of tools, methods, and documents that help identify, determine, and report unauthorized or unapproved activities on the network. Intrusion detection systems are created in the form of software and hardware systems, each with its own advantages and disadvantages. Due to the presence of many features in the data related to intrusion detection systems, this thesis focuses on selecting the desired and effective features using Unsupervised Extreme Learning Machine. A model for data classification is then presented using UELM. To evaluate the performance of the proposed method, the NSL-KDD database is used because it contains more realistic records than other intrusion detection datasets. The test results show that UELM achieves an accuracy of 98.38%, compared to GWO's accuracy of 93.74%. The superiority of UELM in classification and intrusion detection problems is attributed to its robust and generalizable structure as an unsupervised neural network. | ||
کلیدواژهها [English] | ||
Feature selection, Artificial Neural Networks, Unsupervised Extreme Learning Machine, Intrusion detection | ||
مراجع | ||
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