تشخيص حملات در اينترنت اشيا با استفاده از الگوريتم هاي مبتني بر طبقه بند ترکيبي و بهينه سازي شاهين هريس
محورهای موضوعی : مجله فناوری اطلاعات در طراحی مهندسی
محمدحسین اختیاری
1
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,
مهدی جعفری
2
,
مهدیه اسلامی
3
1 - گروه مهندسی برق، واحد کرمان، دانشگاه آزاد اسلامی، کرمان، ايران
2 - گروه مهندسی برق، واحد کرمان، دانشگاه آزاد اسلامی، کرمان، ايران
3 - گروه مهندسی برق، واحد کرمان، دانشگاه آزاد اسلامی، کرمان، ايران
کلید واژه: تشخيص ناهنجاري, تشخيص نفوذ, شاهين هريس, داده کاوي,
چکیده مقاله :
شبکههاي کامپيوتري به دليل آسيب پذير بودن، در معرض انواع مختلفي از حملات ميباشد. به دليل بسياري از ويژگي هاي ترافيک شبکه ، مدل هاي يادگيري ماشيني براي شناسايي حملات زمانبر ميباشد. در اين مقاله هدف بر اين است يک روش جديد براي تشخيص نفوذ حملات ارايه شود. روش جديد براي تشخيص نفوذ شبکه، استفاده از تکنيک يادگيري ماشين و همچنين الگوريتم بهينهسازي شاهين هريس به منظور افزيش دقت تشخيص در تشخيص نفود در شبکههاي کامپيوتري است. روش پيشنهادي به اين صورت است که ابتدا به کمک طبقه بندهاي منفرد طبقه بندي انجام ميشود و سپس با استفاده از طبقهبند نهايي عمل تشخيص انجام ميشود. روش پيشنهادي روي مجموعه داده NSL-KDD مورد آزمايش قرار مي گيرد. براي ارزيابي روش پيشنهادي از معيارهاي دقت، فراخواني و صحت استفاده ميگردد. دقت روش پيشنهادي در بهترين حالت آزمايش بالاي 98 درصد بوده که اعتبار آن بر اساس مقايسه با مراجع ديگر قابل تاييد است. محقق گرامی بمنظور صرفه جویی در وقت شما در این کنفرانس چکیده و اصل را همزمان دریافت می نماید .
Computer networks are exposed to various types of attacks due to their vulnerability. Due to many characteristics of network traffic, machine learning models are time-consuming to identify attacks. In this article, the aim is to provide a new method to detect the penetration of attacks. The new method for detecting network intrusion is to use machine learning technique and Shahin Harris optimization algorithm in order to increase the accuracy of detection in detecting intrusion in computer networks.The proposed method is that first classification is done with the help of individual classifications and then diagnosis is done using the final classification. The proposed method is tested on the NSL-KDD dataset. Accuracy, recall and correctness criteria are used to evaluate the proposed method. The accuracy of the proposed method is above 98% in the best test mode, and its validity can be confirmed based on comparison with other sources.
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