A DEEP AND MACHINE LEARNING COMPARATIVE APPROACH FOR NETWORKS INTRUSION DETECTION
Ali Raad Sameer, Osamah Mohammed Jasmim, Mohamed Omar Mohamed · ASIAN JOURNAL OF CONVERGENCE IN TECHNOLOGY · 2024
Intrusion detection is intergral section of firewalls and other attacks prevention applications that works side by side with the attack pouncing section. The strongest attack prevention application is that of wide range of attack pouncing capability. Recently, data driven models are used for this task which offers the required capability of multiple type of attack detection. In this paper, foucse given to establish an attack detection system that compatible with various datasets and able to draw similar perfromacne in attack flection. Multilayer perception (MLP), Convolutional neural network (CNN). Machine learning algorithms are also deployed such as Random Forest (RF) and Boosting algorithms such as XGBoost, AdaBoost and CatBoost. The MLP algorithm was realized with best intrusion detection performance, it yielded a higher accuracy in both dataset cases. Overall, the classification results on the UNSW-NB15 dataset suggest that machine learning algorithms can be successfully applied to network intrusion detection tasks, with various algorithms demonstrating high levels of accuracy in distinguishing between normal and malicious network traffic.