Intrusion Detection and Classification Using Machine Learning
سال انتشار: 1401
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 1,702
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شناسه ملی سند علمی:
EITCONF01_160
تاریخ نمایه سازی: 24 خرداد 1401
چکیده مقاله:
In the modern society, people increasingly use networks services. Due to rising in attacks and intrusion in network and its consequences and financial losses, Networksecurity is a critical necessity for networks. Intrusion detection systems try to protect the network from illegal access. Abuse detection and anomaly detection are two majortypes of these systems. In this paper, we propose a machine learning method to detect the intrusions in network. Moreover, the learned model can classify the intrusions intofour class, including DoS ،U۲R ،R۲L, and prob. NSL-KDD is a standard dataset for intrusions in networks that used in this paper. First, we carried out the feature selectionand preprocessing on data to prepare the data for learning phase. Then,a model was developed for intrusion detection using support vector machine (SVM). Experimentalresults confirm that our method could successfully detect and classify the intrusions in network with accuracy of ۹۹%.
کلیدواژه ها:
نویسندگان
Babak Eamaeilpour Ghouchani
Department of Computer Engineering,Quchan Branch,Islamic Azad University,Quchan,Iran
Benyamin Mohammadian Khazineh
Abadan university of medical sciences,Abadan , Iran ۳. Master of Artificial Intelligence, Ferdowsi University of Mashhad
Seyed Mohammad Esmaeil kazemi
Bachelor of Computer Science, Software Trend, Hakim Nezami Institute of Higher Education, Quchan
Mohammad Kazem Beshkani
کارشناسی مهندسی کامپیوتر گرایش نرم افزار موسسه آموزش عالی حکیم نظامی قوچان