Hybrid Detection: Enhancing Network & Server Intrusion Detection Using Deep Learning
Vishnu Kurnala, Swaraj Abhishek Naik, Dhanush Chowdary Surapaneni, Ch. Bharathsimha Reddy
Abstract
In the ever-evolving landscape of cybersecurity threats, Intrusion detection systems are critical in protecting network and server infrastructure in the ever-changing spectrum of cybersecurity threats. This research introduces a hybrid detection approach that uses deep learning techniques to improve intrusion detection accuracy and efficiency. The proposed prototype combines the strength of the XGBoost and MaxPooling1D algorithms within an ensemble model, resulting in a stable and effective solution. Through the fusion of these methodologies, the hybrid detection system achieves superior performance in identifying and mitigating various types of intrusions. This paper provides an overview of the prototype's architecture, discusses the benefits of using deep learning in intrusion detection, and presents experimental results showcasing the system's efficacy.