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تشخیص نفوذ در شبکه های رایانه‌ای با استفاده از درخت تصمیم و کاهش ویژگی ها | ||
| پدافند الکترونیکی و سایبری | ||
| دوره 9، شماره 3 - شماره پیاپی 35، آذر 1400، صفحه 99-108 اصل مقاله (691.9 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| نویسنده | ||
| علی اکبر تجری سیاه مرزکوه* | ||
| استادیار،گروه علوم کامپیوتر، دانشگاه گلستان ،گرگان، ایران | ||
| تاریخ دریافت: 05 آذر 1399، تاریخ بازنگری: 23 اسفند 1399، تاریخ پذیرش: 21 فروردین 1400 | ||
| چکیده | ||
| امروزه نیاز به سیستمهای تشخیص نفوذ مبتنی بر ناهنجاری به‌دلیل ظهور حملات جدید و افزایش سرعت اینترنت بیشتر از قبل احساس میشود. معیار اصلی برای تعیین اعتبار یک سیستم تشخیص نفوذ کارآمد، تشخیص حملات با دقّت بالا است. سیستمهای موجود علاوه بر ناتوانی در مدیریت رو به رشدحملات،دارای نرخهای بالای تشخیص مثبت و منفی نادرست نیز میباشند. در این مقاله از ویژگیهایدرخت تصمیمID3 برای سیستمهای تشخیص نفوذ مبتنی بر ناهنجاری استفاده میشود. همچنین از دو روش انتخاب ویژگی برای کاهش میزان دادههای استفاده شده برای تشخیص و دستهبندی استفاده میشود. برای ارزیابی الگوریتم پیشنهادی از مجموعه داده KDD Cup99 استفاده شده است. نتایج آزمایش نشان دهنده میزان دقّت تشخیص برای حملهDoS به میزان89/99% و به‌طورمیانگین میزان دقّت 65/94% برای کلّیه حملات با استفاده از درخت تصمیم است که بیانگر مقادیر بهتر نسبت به کارهای قبلی است. | ||
| کلیدواژه‌ها | ||
| تشخیص نفوذ؛ درخت تصمیم؛ خوشه بندی k-means؛ حمله ی DoS؛ مجموعه داده KDD Cup99 | ||
| عنوان مقاله [English] | ||
| Intrusion Detection in Computer Networks using Decision Tree and Feature Reduction | ||
| نویسندگان [English] | ||
| Aliakbar Tajari Siahmarzkooh | ||
| Assistant Professor, Department of Computer Science, Golestan University, Gorgan, Iran | ||
| چکیده [English] | ||
| Today, the need for anomaly-based intrusion detection systems is felt more than ever due to the emergence of new attacks and the increase in Internet speed. The main criterion for determining the validity of an efficient intrusion detection system is the detection of attacks with high accuracy. In addition to inability of existing systems to manage growing attacks, also they have high rates of positive and negative misdiagnosis. This paper uses the ID3 decision tree features for anomaly-based intrusion detection systems. Two feature selection methods are also used to reduce the amount of used data for the detection and categorization. The KDD Cup99 dataset was used to evaluate the proposed algorithm. The test results show a detection accuracy of 99.89% for the DoS attack and an average accuracy of 94.65% for all attacks using the decision tree, indicating better values than previous tasks. | ||
| کلیدواژه‌ها [English] | ||
| intrusion detection, decision tree, k-means clustering, DoS attack, KDD Cup99 dataset | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 939 تعداد دریافت فایل اصل مقاله: 615 |
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