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شناسایی تزریق داده کاذب در سامانه قدرت با استفاده از روشهای یادگیری عمیق مبتنی بر خودرمزگذار | ||
| پدافند الکترونیکی و سایبری | ||
| دوره 10، شماره 2 - شماره پیاپی 38، مهر 1401، صفحه 11-17 اصل مقاله (1.14 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| نویسندگان | ||
| محمد بخشی پور1؛ فرهاد نامداری* 2؛ محمدباقر دولتشاهی3 | ||
| 1دانشجوی دکتری، گروه آموزشی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه لرستان، خرم آباد، لرستان، ایران | ||
| 2دانشیار، گروه برق، دانشکده فنی و مهندسی، دانشگاه لرستان، خرم آباد، لرستان، ایران | ||
| 3استادیار، گروه آموزشی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه لرستان، خرم آباد، لرستان، ایران | ||
| تاریخ دریافت: 09 بهمن 1399، تاریخ بازنگری: 16 فروردین 1401، تاریخ پذیرش: 20 آذر 1400 | ||
| چکیده | ||
| در دهه گذشته، تعداد حملات سایبری به‌منظور هدف قرار دادن سامانههای قدرت که سبب خسارات فیزیکی و اقتصادی میگردد، افزایش یافته است. حملات تزریق داده کاذب، از جمله حملات سایبری میباشند که بر سامانه نظارت شبکههای برق اثر میگذارد. حملات با تزریق داده کاذب، با دستکاری در تخمین حالت سامانه قدرت، سبب به خطر انداختن شبکه قدرت میشود، همچنین به تازگی برقدزدی یکی از اهداف تزریق داده کاذب قرار گرفته است. روشهای یادگیری ماشینی، یکی از راهکارهای تشخیص دادههای کاذب است. در این مقاله، ابتدا با استفاده از روش خودرمزگذار عمیق، ابعاد مسئله، تعداد ورودی برای طبقهبندی مسئله و شناسایی، کاهش یافته و سپس با استفاده از روش بردار ماشین پشتیبانی و آموزش دادهها، عمل شناسایی انجام شده است. روش تشخیص، برای سامانههای ۱۴ و ۱۱۸ شینه IEEE مورد بررسی و مقایسه قرار گرفته و دقت هر روش بر اساس نتایج شبیهسازی طبقهبندی شده و همچنین به‌منظور اثربخشی روش پیشنهادی، با تغییر در تعداد دادههای تحت آموزش، تأثیر تغییر در دقت شناسایی ارزیابی شده است که نتایج حاکی از اثر بخشی روش پیشنهادی می‌باشد. | ||
| کلیدواژه‌ها | ||
| داده کاذب؛ حملات سایبری؛ یادگیری عمیق؛ کاهش ابعاد مسئله | ||
| عنوان مقاله [English] | ||
| A survey on detecting false data injection in power systems with auto-encoder based deep learning methods | ||
| نویسندگان [English] | ||
| mohammad bakhshipour1؛ farhad namdari2؛ mohammad bagher dowlatshahi3 | ||
| 1PhD student, Computer Education Department, Technical and Engineering Faculty, Lorestan University, Khorram Abad, Lorestan, Iran | ||
| 2Associate Professor, Electrical Department, Technical and Engineering Faculty, Lorestan University, Khorram Abad, Lorestan, Iran | ||
| 3Assistant Professor, Department of Computer Education, Technical and Engineering Faculty, Lorestan University, Khorram Abad, Lorestan, Iran | ||
| چکیده [English] | ||
| The number of cyber-attacks affecting power systems and leading to physical and economic damages has grown rapidly over the last decade. Among the most significant types of cyber-attacks, are the class of false data injection attacks (FDIAs) which affect the power network monitoring systems. FDIAs endanger the power grid with manipulating the power system state estimation (SE). Also, the electricity theft has recently become another purpose of the FDAIs. Machine learning based methods are known as one of the FDIAs detection approaches. In this paper, first, using the deep auto-encoder method, the dimensions of the problem and the number of data entry for problem classification and detection are reduced. Then, by employing the support vector machine (SVM) approach and the data learning method, the procedure of cyber-attack detection is formed. Also, the precision of the proposed approach is improved by changing the number of data being trained. The presented method is evaluated on the IEEE 14 and 118 bus systems. The obtained simulation results demonstrate that the new method can successfully be applied for an accurate and effective detection of FDIAs. | ||
| کلیدواژه‌ها [English] | ||
| False data, cyber-attacks, deep learning, problem dimension reduction | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 965 تعداد دریافت فایل اصل مقاله: 332 |
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