یک رویکرد کاهش ابعاد مبتنی بر يادگیری عمیق و الگوریتم كوركور بال سياه برای تشخیص بدافزار اندروید
الموضوعات : electrical and computer engineering
محسن اقبالی
1
,
محمدرضا ملاخليلی ميبدی
2
,
کمال میرزایی
3
1 - گروه مهندسی کامپیوتر، واحد میبد، دانشگاه آزاد اسلامی، میبد، ایران
2 - گروه مهندسی کامپیوتر، واحد میبد، دانشگاه آزاد اسلامی، میبد، ایران
3 - دانشگاه آزاد اسلامی واحد میبد
الکلمات المفتاحية: یادگیری ماشین, یادگیری عمیق, انتخاب ویژگی, کاهش ابعاد, الگوریتم بهینهسازی كوركور بال سياه, بدافزار اندروید.,
ملخص المقالة :
امروزه با افزایش تعداد دستگاههای تلفن همراه، بدافزارهای مخرب برای پلتفرم اندروید نیز گسترشیافتهاند. این بدافزارها با روشهای پیچیدهتری نوشتهشدهاند که تشخیص آنها دشوار است. برای تشخیص آنها از روشهای یادگیری ماشین و یادگیری عمیق استفاده میشود زیرا توانایی تشخیص الگوهای پیچیده بدافزار را دارند. یکی از چالشهای تشخیص بدافزار با روشهای یادگیری ماشین و یادگیری عمیق، ابعاد زیاد نمونههای آموزشی است. در این مقاله برای کاهش دادن ابعاد نمونههای آموزشی در تشخیص بدافزارهای اندروید یک نسخه دودویی از الگوریتم كوركور بال سياه ارایه میشود. در روش پیشنهادی در مرحله اول با الگوریتم كوركور بال سياه، ویژگیهای بدافزار استخراج میشود و این ویژگیها تحویل شبکه عصبی LSTM میشود. نقش LSTM طبقهبندی نمونهها به بدافزار و نرمافزار در اندروید است. برای افزایش دقت LSTM فراپارامترهای آن با استفاده از الگوریتم بهینهسازی محاسبات ریاضی نیز بهینهسازی میشود. آزمایشها در مجموعهداده 2017CICandMal نشانداد دقت، حساسیت و صحت روش پیشنهادی به ترتیب 63/98%، 29/98% و 48/97% است. در رویکرد پیشنهادی اگر از متعادلسازی با روش GAN برای مجموعهداده 2017CICandMal استفادهشود آنگاه متوسط دقت، حساسیت و صحت روش پیشنهادی به مقادیر 62/99%، 93/98% و 52/98% افزایشداده میشود. آزمایشها نشانمیدهد که روش پیشنهادی نسبت به روشهای کاهش ابعاد نظیر الگوریتم بهینهسازی وال، الگوریتم بهینهسازی شاهین و الگوریتم بهینهسازی کرکس دقت بیشتری در تشخیص بدافزار دارد. روش پیشنهادی نسبت به شبکه عصبی LSTM دقت بیشتری در حدود 16/4 درصد دارد.
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