ارزیابی مدل‌های یادگیری عمیق برای تولید داده آزمون در فازرهای مبتنی بر فایل

تقوی, محمد تقی; باقری, مسعود

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	ارزیابی مدل‌های یادگیری عمیق برای تولید داده آزمون در فازرهای مبتنی بر فایل
پدافند الکترونیکی و سایبری
دوره 10، شماره 2 - شماره پیاپی 38، مهر 1401، صفحه 57-73 اصل مقاله (1.41 M)
نوع مقاله: مقاله پژوهشی
نویسندگان
محمد تقی تقوی^* ¹؛ مسعود باقری²
¹دانشجوی دکتری، دانشگاه جامع امام حسین (ع)، تهران، ایران
²استادیار، دانشگاه جامع امام حسین (ع)، تهران، ایران
تاریخ دریافت: 21 اردیبهشت 1400، تاریخ بازنگری: 13 آذر 1400، تاریخ پذیرش: 18 مرداد 1401
چکیده
فازینگ به معنی اجرای مکرر برنامه تحت آزمون با ورودیهای تغییر یافته، با هدف یافتن آسیبپذیری است. در صورتی که ورودیهای برنامه تحت آزمون دارای ساختار پیچیدهای باشند، تولید ورودیهای تغییر یافته برای انجام فازینگ کار راحتی نیست. بهترین راه حل در این موارد، استفاده از ساختار ورودی برنامه تحت آزمون به‌منظور تولید دقیق داده آزمون است. مشکلی که وجود دارد این است که ممکن است مستندات ساختار ورودی برنامه تحت آزمون در دسترس نباشد. همچنین درک انسانی چنین ساختارهای پیچیدهای نیز بسیار مشکل، پرهزینه، زمانبر و مستعد خطای انسانی است. برای غلبه بر مشکلات فوق، استفاده از یادگیری ماشین و شبکههای عصبی عمیق به‌منظور یادگیری خودکار ساختارهای پیچیده ورودیهای برنامه و تولید داده آزمون متناسب با این ساختار پیشنهاد شده است. یکی از چالشهای اصلی در این زمینه، استفاده از مدل یادگیری متناسب با کاربرد مورد نظر است. در این مقاله، مدلهای یادگیری عمیق مناسب برای یادگیری و تولید داده آزمون در فازرهای مبتنی بر فایل مورد بررسی قرار گرفته است. همچنین با معرفی پارامترهای مناسب برای بررسی کارایی، ارزیابی مدلهای یادگیری عمیق انجام شده است. بر این اساس، شبکههای عصبی بازرخداد و مشتقات آن به‌عنوان بهترین مدلهای یادگیری عمیق برای دادههای متنی انتخاب شده است. همچنین پارامترهای مؤثر برای ارزیابی کارایی مدلهای یادگیری عمیق شامل زمان آموزش، میزان خطای مدلها در زمان آموزش و و زمان ارزیابی درنظر گرفته شده است. پارامتر میزان خطا به‌عنوان پارامتر اصلی، یک بار در مدلهای یادگیری عمیق مختلف با ساختار یکسان و یک بار در مدلهای یادگیری عمیق یکسان با ساختار متفاوت مورد ارزیابی قرار گرفته و بهترین مدل یادگیری عمیق انتخاب و معرفی شده است.
کلیدواژه‌ها
فازینگ؛ یادگیری عمیق؛ تولید داده آزمون متنی؛ ارزیابی کارایی
عنوان مقاله [English]
Evaluating Deep Learning Models for Test Data Generation In File Based Fuzzers
نویسندگان [English]
Mohammad Taghi Taghavi¹؛ Masood Bagheri²
¹PhD student, Imam Hossein University (AS), Tehran, Iran
²Assistant Professor, Imam Hossein University (AS), Tehran, Iran
چکیده [English]
Fuzzing means repeatedly running the program being tested, by modified inputs, with the aim of finding its vulnerabilities. If the program has a complex input structure, generating modified inputs for fuzzing is not an easy task. The best solution in such cases is to use the input structure of the program under test to produce accurate test data. The problem is that the input structure documentation of program under test may not be available. Human understanding of such complex structures is also hard to achieve, costly, time consuming, and prone to errors. To overcome to above problems, this research proposes the use of machine learning and deep neural networks, which automatically learn the complex structures of program inputs and generate test data tailored to this structure. One of main challenges in this field is choosing the appropriate deep learning model which suits the intended application. In this paper, suitable deep learning models for learning and test data generation in file-based fuzzers are studied. Also, the evaluation is performed by introducing and applying the appropriate performance evaluation parameters. So the recurrent neural network and its derivations are introduced as the best deep learning models for text data. Also, effective parameters considered for performance evaluation include the training time, loss value in training and evaluation time. The loss value as the main parameter is evaluated once in various deep learning models with same structure and again in the same deep learning models with various structures and the best deep learning model is selected and proposed.
کلیدواژه‌ها [English]
Fuzzing, Deep Learning, Text Test Data Generation, Performance Evaluation

مراجع
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ارزیابی مدل‌های یادگیری عمیق برای تولید داده آزمون در فازرهای مبتنی بر فایل