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افزایش امنیت گذرواژه دیجیتالی با استفاده از یادگیری ماشین: تحلیلی مقایسهای بر الگوریتمهای طبقهبندی | ||
| پدافند الکترونیکی و سایبری | ||
| دوره 13، شماره 4 - شماره پیاپی 52، دی 1404، صفحه 57-72 اصل مقاله (1.24 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.47176/ECDJ.2025.1652 | ||
| نویسندگان | ||
| مهناز درودی1؛ سید حسن مرتضوی زارچ* 2؛ فاطمه زارع مهرجردی3؛ محسن سرداری زارچی4 | ||
| 1کارشناسی ارشد گروه کامپیوتر, دانشکده فنی و مهندسی, دانشگاه میبد, میبد, ایران | ||
| 2استادیار گروه کامپیوتر , دانشکده فنی و مهندسی ,دانشگاه میبد ، میبد ، ایران | ||
| 3استادیار گروه کامپیوتر، دانشکده فنی و مهندسی، دانشگاه میبد، میبد، ایران | ||
| 4دانشیار گروه کامپیوتر، دانشکده فنی و مهندسی، دانشگاه میبد، میبد، ایران | ||
| تاریخ دریافت: 27 مهر 1404، تاریخ بازنگری: 30 آبان 1404، تاریخ پذیرش: 20 آذر 1404 | ||
| چکیده | ||
| در عصر دیجیتال، رمزهای عبور همچنان یکی از رایجترین و حیاتیترین روشهای احراز هویت در سامانههای اطلاعاتی به شمار میآیند. با این حال، انتخاب و استفاده از رمزهای عبور ضعیف و غیرایمن، همچون رمزعبورهای ساده، کوتاه یا تکراری، تهدیدی جدی برای امنیت سایبری محسوب میشود. این پژوهش با هدف بهبود دقت در ارزیابی سطح امنیت رمزهای عبور، چارچوبی مبتنی بر یادگیری ماشین ارائه میدهد. روش پیشنهادی شامل سه مرحلهی اصلی پیشپردازش، استخراج ویژگیهای جدید و طبقهبندی رمزهای عبور است. در این پژوهش، مجموعهای شامل 669880 رمز عبور مورد تحلیل قرار گرفته است. ابتدا در مرحله پیشپردازش مراحلی مانند حذف دادههای گمشده، کدگذاری متن به عدد و متعادلسازی دادههای کلاسها با استفاده از الگوریتم SMOTE انجام شده است. بعد از اینکه دادهها تمیز شدند از هر رمز عبور ده ویژگی ساختاری جدید استخراج شده است. در نهایت دادهها به دو دسته آموزش و آزمایش تقسیم شده و دستهبندی سطح امنیتی رمزهای عبور در سه کلاس ضعیف، متوسط و قوی با استفاده از الگوریتمهای یادگیری ماشین مانند درخت تصمیم، جنگل تصادفی، XGBoost و رگرسیون لجستیک و غیره صورت گرفته است. نتایج تجربی نشان داد که الگوریتم جنگل تصادفی و روش XGBoost با معیار F1 به ترتیب با مقدار 99.665% و 99.642% درصد عملکرد برتری نسبت به سایر مدلها داشتهاند. دستاورد این تحقیق، ارائه مدلی کارآمد و مقیاسپذیر برای شناسایی نقاط ضعف رمزهای عبور و ارتقاء سیاستهای امنیتی در سامانههای دیجیتال است که میتواند به شکل مؤثری در طراحی رمزهای عبور مقاوم، کاهش ریسک نفوذ، و ارتقاء سطح امنیت سایبری نقشآفرینی کند. | ||
| کلیدواژهها | ||
| یادگیری ماشین؛ امنیت سایبری؛ قدرت رمزعبور؛ جنگل تصادفی | ||
| موضوعات | ||
| امنیت اطلاعات، رمزنگاری، پنهان نگاری، پروتکل ها و استانداردها | ||
| عنوان مقاله [English] | ||
| Enhancing Digital Password Security Using Machine Learning: A Comparative Analysis of Classification Algorithms | ||
| نویسندگان [English] | ||
| Mahnaz dorodi1؛ Seyed Hasan Mortazavi Zarch2؛ Fatemeh Zare Mehrjardi3؛ Mohsen Sardari Zarchi4 | ||
| 1Master of Science, Department of Computer Science, Faculty of Engineering, Meybod University, Meybod, Iran | ||
| 2Assistant Professor of Computer Department, Faculty of Technology and Engineering, Meybod University, Meybod, Iran | ||
| 3Assistant Professor of Computer Department, Faculty of Technology and Engineering, Meybod University, Meybod, Iran | ||
| 4Associate Professor of Computer Department, Faculty of Technology and Engineering, Meybod University, Meybod, Iran | ||
| چکیده [English] | ||
| In the digital age, passwords remain one of the primary methods of authentication in information systems. Despite their critical role in protecting personal and organizational data, the use of weak passwords such as those that are overly simple, short, or repetitive poses a significant threat to cybersecurity. This study proposes a machine learning-based approach to enhance the accuracy of password security assessment. The proposed method consists of three main steps: preprocessing, new feature extraction and password classification. In this study, a dataset with 669,880 passwords is analyzed. First, in the preprocessing step, operations such as removing missing data, encoding text to number, and fixing the problem of unbalanced classes using smote are performed. After data cleaning, 10 new features are extracted from each password. Finally, the processed dataset is partitioned for training and testing operations and strength classification of passwords are predicted using various machine learning classifiers such as Decision tree, Random Forest, XGBoost, Logistic regression and others. Experimental results shows that the Random Forest and XGBoost algorithms achieved the highest F1-score with values of 99.665% and 99.642% respectively rather than other models. The outcome of this research is a scalable and efficient framework for identifying password vulnerabilities and reinforcing security policies in digital systems. This framework can play a key role in designing robust passwords and mitigating the risk of unauthorized access. | ||
| کلیدواژهها [English] | ||
| Machine Learning, Cybersecurity, Strength Password, Random Forest | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 100 تعداد دریافت فایل اصل مقاله: 45 |
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