Improved Network Intrusion Detection System Using Deep Learning

Cybersecurity has become an ever-pressing concern in today’s interconnected world. Network intrusion detection systems (NIDS) play a pivotal role in safeguarding digital assets by identifying and mitigating malicious activities. In this paper, we present an extensive study on network intrusion detection, focusing on the application of machine learning and deep learning algorithms to enhance the accuracy and efficiency of intrusion detection. Our experimentation revealed promising outcomes. The selected algorithms and models demonstrated significant improvements in the detection of network intrusions. Notably, the CNN achieved an accuracy of 98.8 percent, surpassing traditional machine learning approaches. These results indicate the potential for advanced deep learning techniques in enhancing network security.