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شاهزاده فاضلی, سیدابوالفضل, قوه ندوشن, اعظم, زارع پور احمدآبادی, جمال. (1403). بهبود سرعت سیستم تشخیص نفوذ از طریق کاهش حجم داده‌ها با استفاده از DBSCAN مبتنی بر هسته. پدافند الکترونیکی و سایبری, 12(4), 89-102.
سیدابوالفضل شاهزاده فاضلی; اعظم قوه ندوشن; جمال زارع پور احمدآبادی. "بهبود سرعت سیستم تشخیص نفوذ از طریق کاهش حجم داده‌ها با استفاده از DBSCAN مبتنی بر هسته". پدافند الکترونیکی و سایبری, 12, 4, 1403, 89-102.
شاهزاده فاضلی, سیدابوالفضل, قوه ندوشن, اعظم, زارع پور احمدآبادی, جمال. (1403). 'بهبود سرعت سیستم تشخیص نفوذ از طریق کاهش حجم داده‌ها با استفاده از DBSCAN مبتنی بر هسته', پدافند الکترونیکی و سایبری, 12(4), pp. 89-102.
شاهزاده فاضلی, سیدابوالفضل, قوه ندوشن, اعظم, زارع پور احمدآبادی, جمال. بهبود سرعت سیستم تشخیص نفوذ از طریق کاهش حجم داده‌ها با استفاده از DBSCAN مبتنی بر هسته. پدافند الکترونیکی و سایبری, 1403; 12(4): 89-102.

بهبود سرعت سیستم تشخیص نفوذ از طریق کاهش حجم داده‌ها با استفاده از DBSCAN مبتنی بر هسته

پدافند الکترونیکی و سایبری
مقاله 8، دوره 12، شماره 4 - شماره پیاپی 48، بهمن 1403، صفحه 89-102    اصل مقاله (1.06 M)
نوع مقاله: مقاله پژوهشی
نویسندگان
سیدابوالفضل شاهزاده فاضلی* 1؛ اعظم قوه ندوشن2؛ جمال زارع پور احمدآبادی3
1دانشیار، دانشگاه یزد، یزد،ایران
2کارشناسی ارشد،دانشگاه یزد،یزد،ایران
3استادیار، دانشگاه یزد،یزد،ایران
تاریخ دریافت: 14 مهر 1403،  تاریخ بازنگری: 22 آذر 1403،  تاریخ پذیرش: 23 دی 1403 
چکیده
اینترنت اشیاء یک فناوری به‌سرعت در حال تکامل است که دستگاه‌های فیزیکی را از طریق سیستم‌های شبکه‌ای به هم متصل می‌کند. بااین‌حال، همان‌طور که اینترنت اشیاء به گسترش خود ادامه می‌دهد، چالش‌های امنیتی مختلفی را ایجاد می‌کند که نیازمند راه‌حل‌های مناسب برای محافظت از اطلاعات حساس و حریم خصوصی کاربران است. این مقاله بر روی بهبود سرعت سیستم تشخیص نفوذ به‌عنوان یک راه‌حل حیاتی برای امنیت اینترنت اشیاء تمرکز دارد. در سیستم‌های تشخیص نفوذ، وجود حجم زیاد داده موجب کاهش سرعت یادگیری می‌شود. در این مقاله، الگوریتم خوشه‌بندی DBSCAN با افزودن پارامتر حداقل همسایگی جهت کاهش هدفمند نمونه‌ها اصلاح شده است، که سعی در افزایش سرعت سیستم تشخیص نفوذ و کاهش زمان و هزینه یادگیری دارد. تنظیم پارامترهای DBSCAN اصلاح‌شده با الگوریتم ژنتیک انجام می‌شود. نتایج آزمایش‌ها بر روی مجموعه‌داده Kaggle و NSL_KDD نشان می‌دهد که مدل پیشنهادی قادر است با کاهش تا 80٪ از حجم داده‌ها، دقت طبقه‌بندی را برای مجموعه‌داده Kaggle بالای 96٪ و برای مجموعه‌داده NSL_KDD بالای 51/92٪ حفظ نماید. همچنین، زمان محاسبات برای مجموعه‌داده Kaggle از ms09/458 بهms 21/47 و برای مجموعه‌داده NSL_KDD ازms 2/995 بهms 60/223، کاهش‌یافته است. به این ترتیب، با وجود بهبود در سرعت و کاهش زمان و هزینه، عملکرد مطلوب مدل حفظ شده است.
کلیدواژه‌ها
اینترنت اشیاء؛ سیستم تشخیص نفوذ؛ الگوریتم درخت طبقه‌بندی و رگرسیون؛ DBSCAN؛ RN_DBSCAN؛ مجموعه داده Kaggle؛ مجموعه داده NSL_KDD
موضوعات
امنیت داده
عنوان مقاله [English]
Improving the speed of the intrusion detection system performance by reducing the data volume using kernel-based DBSCAN
نویسندگان [English]
Seyed Abolfazl Shahzadeh Fazeli1؛ Azam Ghoveh Nodoushan2؛ Jamal Zarepour-Ahmadabadi3
1Associate Professor, Yazd University, Yazd, Iran
2Master's degree, Yazd University, Yazd, Iran
3Assistant Professor, Yazd University, Yazd, Iran
چکیده [English]
The Internet of Things (IoT) is a rapidly evolving technology that connects physical devices through networked systems. However, as IoT continues to expand, it poses various security challenges that require appropriate solutions to protect sensitive information and user privacy. This paper focuses on improving the speed of intrusion detection systems (IDS) as a critical solution for IoT security. In IDS, the large volume of data can slow down the learning process. In this paper, the DBSCAN clustering algorithm is modified by adding a minimum neighborhood parameter to reduce data samples in a targeted manner, aiming to enhance the speed of IDS and reduce learning time and costs. The parameters of the modified DBSCAN are tuned using a genetic algorithm. Experimental results on the Kaggle and NSL_KDD datasets demonstrate that the proposed model can maintain classification accuracy above 96% for the Kaggle dataset and above 92.51% for the NSL_KDD dataset, even with up to an 80% reduction in data volume. Additionally, computation time for the Kaggle dataset decreased from 458.09 ms to 47.21 ms, and for the NSL_KDD dataset from 995.2 ms to 223.60 ms. Thus, despite improvements in speed and reductions in time and cost, the model's optimal performance is maintained.
کلیدواژه‌ها [English]
Internet of Things, Intrusion detection system, Clustering, Classification and regression tree algorithm, DBSCAN, Data reduction, RN_DBSCAN, Genetic algorithm, Kaggle dataset, NSL_KDD dataset
مراجع

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