بهبود عملکرد سیستم‌های تشخیص حملات فیشینگ مبتنی بر هم‌افزایی شبکه عصبی و الگوریتم علی بابا و چهل دزد
الموضوعات :
رضا عصاره
1
(استادیار، گروه مهندسی کامپیوتر، واحد یادگار امام خمینی (ره) شهر ری، دانشگاه آزاد اسلامی، شهر ری، ایران.)
یونس مباشری
2
()
الکلمات المفتاحية: حملات سایبری, حملات فیشینگ, یادگیری ماشین, شبکه عصبی پرسپترون چند لایه, الگوریتم علی بابا و چهل دزد,
ملخص المقالة :
یکی از حملات سایبری، حملات فیشینگ است که در سال‌های اخیر به‌سرعت افزایش یافته‌اند. استفاده از یادگیری ماشین برای آموزش سیستمی که پیام‌های فیشینگ را تشخیص می‌دهد، برای افزایش سطح امنیت در برابر حملات سایبری ضروری است. با استفاده از یافتن وزن و بایاس‌های شبکه عصبی از طریق الگوریتم علی بابا و چهل دزد می‌توان صفحات فیشینگ را با دقت بالایی شناسایی کرد. در روش پیشنهادی برای دسته‌بندی و تشخیص حملات فیشینگ از شبکه عصبی پرسپترون چند لایه استفاده می‌شود. وزن‌های شبکه عصبی پرسپترون چند لایه از طریق الگوریتم علی بابا و چهل دزد پیدا می‌شوند. نکته مهم، انتخاب روشی است که تابع هزینه با آن محاسبه شود که شامل 'MSE'، 'RMSE' و 'Accuracy' هستند. شبیه‌سازی روش پیشنهادی از طریق نرم‌افزار متلب انجام شده است. در مجموعه داده، ویژگی‌های مختلف مربوط به وب‌سایت‌های قانونی و فیشی را شناسایی کرده و ۱۳۵۳ وب‌سایت مختلف از منابع مختلف جمع‌آوری شده است. نتایج روش پیشنهادی با طرح پایه از نظر دقت، صحت، F1_Score و منحنی AUC-ROC مقایسه می‌شوند. مطابق با نتایج به دست آمده، دقت روش پیشنهادی نسبت به روش LR به میزان 4.91 درصد، نسبت به روش ماشین بردار پشتیبان به میزان 5.7 درصد، نسبت به روش K نزدیک‌ترین همسایه به میزان 3.72 درصد، نسبت به روش AdaBoost به میزان 9.03 درصد، نسبت به روش پرسپترون چند لایه به میزان 3.53 درصد، نسبت به روش J48 به میزان 2.46 درصد و نسبت به روش جنگل تصادفی به میزان 0.74 درصد بهبود داشته است. همچنین روش پیشنهادی نسبت به روش‌های ترکیبی الگوریتم‌های فراابتکاری و شبکه عصبی نیز بهبود داشته است. دقت روش پیشنهادی نسبت به روش EPO – ANN به میزان 1.3 درصد و همچنین نسبت به روش SSA – ANN به میزان 1.41 درصد بهبود داشته است.
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