Proposing a Model for Detecting Intrusion Network Attacks Using Machine Learning Techniques | ||
| Journal of Education and Science | ||
| Article 8, Volume 31, Issue 3, September 2022, Pages 99-109 PDF (549.19 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.33899/edusj.2022.133867.1240 | ||
| Authors | ||
| Teba Ali Jasem Ali; Muna Jawhar* | ||
| software Department, University of Mosul, Mosul, Iraq | ||
| Abstract | ||
| At the present time, the reliance on computers is increasing in all aspects of life, so it is necessary to protect computer networks and computing resources from complex attacks against the network. This is done by building tools, applications, and systems that detect attacks or anomalies adapting to ever-changing architectures and dynamically changing threats. Providing network security is one of the most important things in network communications, more networks grow and the more devices are added to the network, need more requirement to provide network security, a network security system is necessary to protect devices and data of network users, helps protect information shared on the network, protection of people's personal information and helps prevent users from falling victim to pirates The goal of this paper is to build a Network Intrusion Detection System (NIDS) based on deep learning techniques such as Convolutional Neural Network (CNN), which demonstrated its efficiency in predicting, classifying, and extracting high-level features in network traffic. | ||
| Keywords | ||
| Network security,,; ,،,؛deep learning,,; ,،,؛Convolutional Neural Network | ||
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