Designing Intelligent Intrusion Detection System Using Deep Learning

Abstract

In order to increase the effectiveness and precision of cyber threat detection in computer networks, deep learning techniques are being applied in the construction of intelligent intrusion detection systems (IDS). The suggested IDS can automatically learn and recognize complicated patterns and characteristics from network traffic data by utilizing the capabilities of deep neural networks. Convolutional neural networks (CNNs), recurrent neural networks (RNNs), or deep belief networks (DBNs) are some examples of deep learning architectures that can be built as part of the system design. These models can then be trained on labelled data to distinguish between legitimate and nefarious network behavior. In order to detect both well-known and new threats, the IDS concentrates on capturing both known attack signatures and unusual behaviors. Deep learning models are well suited for real-time intrusion detection in dynamic network environments because they can generalize and adapt to changing threats. By using methods like transfer learning, dimensionality reduction, and visualization, the system also handles issues like feature extraction, scalability, and interpretability. The suggested IDS seeks to achieve higher detection rates, lower false positive rates, and improved resilience against evasion strategies through thorough experimentation and review. The ultimate objective is to create an intelligent IDS that accurately detects and mitigates hostile activity to successfully protect computer networks, improving the overall security posture of the network infrastructure.