ارائه یک روش جدید برای تشخیص نفوذ و رفتارهای مخرب در داده های حجیم

محورهای موضوعی : مهندسی برق و کامپیوتر

هما موحد نژاد ¹ (دانشکده مهندسی کامپیوتر، واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران)
محسن پورشعبان ² (دانشکده مهندسی کامپیوتر، واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران)
احسان یزدانی چمزینی ³ (دانشکده مهندسی کامپیوتر، واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران)
الهه همتی اشنی ⁴ (دانشکده مهندسی کامپیوتر، واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران)
مهدی شریفی ⁵ (دانشکده مهندسی کامپیوتر، واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران)

تاریخ دریافت : 1402/10/13 تاریخ پذیرش : 1403/02/04 تاریخ انتشار : 1403/12/29

کلید واژه: تشخیص نفوذ, رفتارهای مخرب, الگوریتم ماشین بردار پشتیبان, کلان داده¬ها, الگوریتم غذایابی باکتری,

چکیده مقاله :

امروزه حفظ امنیت اطلاعات و تشخیص نفوذ به¬منظور مقابله با رفتارهای مخرب در داده¬های حجیم از اهمیت بسزایی برخوردار است. در این مقاله یک روش ترکیبی نرم¬افزاری سخت¬افزاری برای تشخیص داده¬های مخرب ارائه شده است. در این روش سه فاکتور پیشرفت زمانی، تاریخچه کاربران و مقیاس¬پذیری لحاظ شده است. در روش پیشنهادی از تکنیک¬های ذخیره¬سازی و استخراج ویژگی جهت افزایش سرعت و کاهش حجم محاسبات استفاده می¬شود. همچنین برای طبقه¬بندی از الگوریتم ماشین بردار پشتیبان تغییر یافته و برای عملیات استخراج ویژگی‌ها از الگوریتم غذایابی باکتری بصورت موازی‌سازی شده، بهره برده شده است. نتایج نشان می‌دهد که الگوریتم پیشنهادی نسبت به سایر روش¬های مشابه، از نظر نرخ تشخیص 21%، نرخ مثبت کاذب 62%، دقت 15% و زمان اجرا 70% بهتر عمل می¬کند. کاهش زمان اجرا بیانگر آن است که برای اجرای الگوریتم به انرژی مصرفی کمتری نیاز است که در نتیجه می‌تواند علاوه بر صرفه‌جویی انرژی؛ جهت بکارگیری در سیستم‌های انرژی سبز نیز سودمند باشد.

چکیده انگلیسی:

Today, maintaining information security and intrusion detection is very important to deal with malicious behaviors in massive data. In this article, a hybrid method for detecting malicious data is presented wherein three factors of time progress, history of users and scalability are taken into account. The proposed method utilizes storage and feature extraction techniques to increase the speed and reduce the amount of calculations. In addition, the support vector machine algorithm has been modified for classification, and the parallelized bacterial foraging optimization algorithm has been used for feature extraction. The results show that the proposed algorithm outperforms the existing methods in terms of detection rate by 21%, false positive rate by 62%, accuracy by 15% and execution time by 70%. The reduction in execution time indicates that less energy is needed to run the algorithm which results in saving energy and can be beneficial for use in green energy systems.

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