DeepShield: A Hybrid Deep Learning Approach for Effective Network Intrusion Detection

In today's rapidly evolving digital landscape, ensuring the security of networks and systems has become more crucial than ever before. The ever-present threat hackers intruders attempting to disrupt compromise online services highlights pressing need for robust measures. With continuous advancement systems, new dangers arise, but so do innovative solutions. One such solution is implementation Network Intrusion Detection Systems (NIDSs), which play a pivotal role in identifying potential threats computer by categorizing network traffic. However, effectiveness an intrusion detection system lies its ability prepare data identify critical attributes necessary constructing classifiers. light this, this paper proposes, DeepShield, cutting-edge NIDS that harnesses power deep learning leverages hybrid feature selection approach optimal performance. DeepShield consists three essential steps: selection, rule assessment, detection. By combining strengths machine technologies, developed excels detecting intrusions. process begins capturing packets from network, are then carefully preprocessed reduce their size while retaining information. These refined fed into algorithm, employs characteristics learn test patterns. Simulation results demonstrate superiority over previous approaches. achieves exceptional level accuracy malicious attacks, as evidenced outstanding performance on widely recognized CSE-CIC-DS2018 dataset.