طبقه‌بندی و شناسایی وب سایتهای فیشینگ به کمک مجموعه قوانین فازی و الگوریتم اصلاح‌شده بهینه‌سازی صفحات شیبدار
محورهای موضوعی : مهندسی برق و کامپیوتر
مجید عبدالرزاق نژاد
1
(دانشگاه بزرگمهر قائنات)
کلید واژه: انتخاب هوشمند ویژگی استنتاج فازی الگوریتم بهینه‌سازی صفحات شیبدار شناسایی وب سایتهای فیشینگ طبقه‌بندی,
چکیده مقاله :
یکی از تهدیدات پیش روی توسعه فناوری اطلاعات در فضای مجازی، سرقت اطلاعات شخصی و مالی کاربران می‌باشد که این تهدید امنیتی، فیشینگ نامیده می‌شود. بررسی و تحلیل روش‌های موجود نشان می‌دهد که ایجاد انعطاف‌پذیری در انتخاب ویژگی‌های اثرگذار در فرایند شناسایی وب سایتهای فیشینگ، پویاسازی رفتار الگوریتم طبقه‌بندی کننده وب سایتهای هدف و نیز امکان تحلیل و کنترل حجم گسترده‌ای از وب سایت‌ها مورد توجه قرار نگرفته است. لذا در این مقاله به منظور تحقق هم‌زمان سه هدف یادشده، ابتدا مکانیزمی بر اساس طراحی یک آستانه تغییر برای کاهش انعطاف‌پذیر ویژگی‌های مورد ارزیابی در شناسایی وب سایتهای فیشینگ تعریف شده است. سپس با حافظه‌مند نمودن الگوریتم بهینه‌سازی صفحات شیبدار، کاهش نرم اثر حافظه بر عملکرد الگوریتم در تکرارهای بالا و نیز تعریف 12 قانون فازی در یک سیستم استنتاج فازی اقدام به پویاسازی هوشمند این الگوریتم به منظور طبقه‌بندی وب سایتهای جامعه ارزیابی به سه طبقه قانونی، مشکوک و فیشینگ می‌نماید. نتیجه پیاده‌سازی رویکرد هوشمند جدید پیشنهادی بر روی داده محک استاندارد در این حوزه و نیز مقایسه عملکرد این الگوریتم با عملکرد بهترین الگوریتم‌های موجود، نشان از تحقق اهداف سه‌گانه فوق‌الذکر برای این تحقیق را دارد.
One of the most important factors influencing the development of information technology on internet is steal the customer information. This security threat is known as phishing. With regarding to review and analysis of the published methods, lake of create the flexibility to effective attribute selection in the procedure of phishing websites detection, non- dynamic behavior of classification algorithm on target websites and also no attention to reduce the amount of computation for the large number of websites are the main gaps of these methods. To achieve the above-mentioned objectives, a new dynamic mechanism is planned to flexible attribute reduction based on designing threshold change of assessment in this paper. Then inclined planes optimization algorithm is memorized based soft reducing the effect of the embedded memory though high iterations and 12 fuzzy rules are defined in a fuzzy inference system for intelligent dynamiting the algorithm. The experimental results of the proposed intelligent algorithm and the comparison the algorithms with the best available algorithms; demonstrate the ability of the modified inclined planes optimization algorithm to detect phishing websites and satisfy the above mentioned objectives.
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