شناسایی تزریق داده کاذب در سامانه قدرت با استفاده از روش‌های یادگیری عمیق مبتنی بر خودرمزگذار

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه آموزشی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه لرستان، خرم آباد، لرستان، ایران

2 دانشیار، گروه برق، دانشکده فنی و مهندسی، دانشگاه لرستان، خرم آباد، لرستان، ایران

3 استادیار، گروه آموزشی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه لرستان، خرم آباد، لرستان، ایران

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

عنوان مقاله [English]

A survey on detecting false data injection in power systems with auto-encoder based deep learning methods

نویسندگان [English]

  • mohammad bakhshipour 1
  • farhad namdari 2
  • mohammad bagher dowlatshahi 3
1 PhD student, Computer Education Department, Technical and Engineering Faculty, Lorestan University, Khorram Abad, Lorestan, Iran
2 Associate Professor, Electrical Department, Technical and Engineering Faculty, Lorestan University, Khorram Abad, Lorestan, Iran
3 Assistant 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

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مراجع

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پدافند الکترونیکی و سایبری
دوره 10، شماره 2 - شماره پیاپی 38
شماره پیاپی 38، فصلنامه تابستان
مهر 1401
صفحه 11-17

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سابقه مقاله

  • تاریخ دریافت: 09 بهمن 1399
  • تاریخ بازنگری: 16 فروردین 1401
  • تاریخ پذیرش: 20 آذر 1400
  • تاریخ انتشار: 01 مهر 1401

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