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

Abstract

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