تشخیص حمله کانال جانبی بر اساس ماشین بردار پشتیبان بهبودیافته
محورهای موضوعی : شبکه
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کلید واژه: الگوریتم یوزپلنگ, ماشین بردار پشتیبان, تشخیص حملات کانال جانبی,
چکیده مقاله :
در مقالات از تکنیک‌های مختلفی از یادگیری ماشین، در حوزه‌های تشخیص حملات کانال جانبی استفاده شده است. یکی از روش‌های مناسب برای این منظور ماشین بردار پشتیبان است. در جدیدترین تحقیقات تشخیص حمله کانال جانبی از روش بازپخت فلزات برای شبیه‌سازی و بهبود ماشین بردار پشتیبان استفاده شده است. تنظیم دقیق پارامترهای ماشین بردار پشتیبان برای تشخیص حملات کانال جانبی از اهمیت بالایی برخوردار است که جزء مسائل NP-Hard محسوب می‌شود و استفاده از روش‌های فراابتکاری راه‌حل مناسبی برای این مسائل است. الگوریتم‌های جستجوگر باقدرت همگرایی بالایی که دارند، می‌توانند به مقادیر مناسبی برای پارامترها برسند و در نتیجه صحت تشخیص حملات را افزایش دهند. الگوریتم فراابتکاری یوزپلنگ دارای قدرت همگرایی بالاتری نسبت به روش‌های بهینه‌سازی از جمله روش بازپخت دارند. در این مقاله برای اولین‌بار از روش یوزپلنگ برای بهبود روش ماشین بردار پشتیبان در تشخیص حملات کانال جانبی استفاده شده است. نتایج شبیه‌سازی از مجموعه‌داده DPA Contest v4 استفاده شده است که نشان می‌دهد روش پیشنهادی توانسته، نتایج صحت تشخیص را نسبت به روش ماشین بردار پشتیبان و نسخه بهبودیافته آن یعنی الگوریتم بازپخت شبیه‌سازی‌شده تا ۱% بهبود دهد.
In the articles, various machine learning techniques have been used in the fields of detecting side channel attacks. One of the suitable methods for this purpose is support vector machine. In the latest side channel attack detection research, the metal annealing method is used to simulate and improve the support vector machine. Accurate setting of support vector machine parameters is of great importance to detect side channel attacks, which is considered part of NP-hard problems, and using meta-heuristic methods is a suitable solution for these problems. Search algorithms with high convergence power can reach suitable values for parameters and thus increase the accuracy of attack detection. Cheetah meta-heuristic algorithm has higher convergence power than optimization methods including annealing method. In this article, the cheetah method is used for the first time to improve the support vector machine method in detecting side channel attacks. The simulation results have been used from the DPA Contest v4 dataset, which shows that the proposed method has been able to improve the results of the detection accuracy compared to the support vector machine method and its improved version, the simulated annealing algorithm, by 1%.
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