استفاده از الگوریتم رقابت استعماری اصلاح شده به منظور افزایش سرعت و دقت سیستم تشخیص نفوذ هوشمند
محورهای موضوعی : پردازش چند رسانه ای، سیستمهای ارتباطی، سیستمهای هوشمندمحمد نظرپور 1 , نوید نظافتی 2 , سجاد شکوهیار 3
1 - دانشجوی دکتری، مدیریت فناوری اطلاعات، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران
2 - استادیار، گروه مدیریت، دانشگاه شهید بهشتی، تهران، ایران
3 - دانشیار، گروه مدیریت، دانشگاه شهید بهشتی، تهران، ایران.
کلید واژه: فرمولاسیون انطباقی, قانون فازی, الگوریتم ICA, تشخیص حمله, شبکه عصبی,
چکیده مقاله :
در تمام سیستمهای پردازش اطلاعات، شناسایی حملات سایبری یک چالش اصلی محسوب می شود و با شناسایی به موقع حملات می‌توان اثرات آن را مسدود یا کم کرد. سیستم اینترنت اشیا نیز از این پدیده مستثنی نبوده و با پیشرفت رو به رشد این فناوری و گسترش زیرساخت های آن، نیاز به سیستم تشخیص نفوذ هوشمند با دقت و سرعت بالا یک امر ضروری است. شبکه‌های عصبی سیستمهای مدرنی هستند که از روش‌های محاسباتی نوین برای یادگیری ماشین، نمایش دانش و در نهایت استفاده از دانش کسب‌شده برای به حداکثر رساندن پاسخ‌های خروجی سیستمهای پیچیده استفاده می کنند. یکی از معایب استفاده از آموزش با روش های کلاسیک در شبکه های عصبی، گیرافتادن در نقاط بهینه محلی است. در این مقاله از الگوریتم فراابتکاری رقابت امپریال (ICA) برای آموزش شبکه های عصبی استفاده کرده، نشان دادیم که این الگوریتم در زمینه تشخیص نفوذ در سیستم اینترنت اشیا، می تواند عملکرد بسیار بهتری از منظر سرعت و دقت نسبت به روش های آموزشی کلاسیک داشته باشد .نتایج نشان می دهد روش پیشنهادی دارای دقت 90% می باشد که در مقایسه با روش شبکه عصبی کلاسیک که دارای دقت 75 درصد بوده عملکرد بهتری دارد.
Introduction: In recent decades, rapid development in the world of technology and networks has achieved, also there is a spread of Internet of thing services in all fields over the world. Piracy numbers have increased, also a lot of modern systems were penetrated. Thus the developing information security technologies to detect the new attack become an important requirement.Method: One of the most important information security technologies is an Intrusion Detection System (IDS) that uses machine learning and deep learning techniques to detect anomalies in the network. In all of the information processing systems, detecting cyber-attacks is one of the main challenges and its effects can be blocked or limited by timely detection of attacks. The IoT system is no exception to this phenomenon, and with the high development of this technology and the expansion of its infrastructure, the need for an intelligent intrusion detection system with high accuracy and speed is essential. Neural networks are modern systems and computational methods for machine learning, knowledge representation, and the application of acquired knowledge to maximize the output accuracy of complex systems. Neural networks have already been used to solve many problems related to pattern recognition, data mining, data compression and research is still underway with regards to intrusion detection systems. One of the disadvantages of using training with classical methods in neural networks is getting stuck in local optimal points. In this paper, we use the meta-heuristic algorithm of Imperial competition algorithm (ICA) to train neural networks and show that in the field of intrusion detection in the IoT system, it can show much better accuracy and speed to classical training methods.Results: Results show that our proposed method has 90% accuracy. This method has a better performance in comparison to classical neural network that has 75% accuracy.Discussion: In this article, we will show that the use of imperial competition evolutionary optimization algorithms instead of traditional methods can increase the accuracy of the IDS system. In addition, evolutionary optimization algorithms are zero order and less complicated than gradient methods. Therefore, using this method, in addition to reducing the cost of system implementation, can increase the speed and accuracy of intrusion detection. In addition, from reliability point of view, we will show that the ICA-based systems are more stable in different implementations.
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