Effectiveness of an Adaptive Deep Learning-Based Intrusion Detection System
William Eduardo Villegas-Ch, Jaime Govea, Rommel Gutierrez, Alexandra Maldonado Navarro, Aracely Mera-Navarrete · IEEE Access · 2024
Cybersecurity is characterized by its dynamism and complexity, with malicious actors continually developing new strategies to evade traditional intrusion detection systems. These systems, while once robust, now often need help to adapt to evolving threat tactics, resulting in a high incidence of false positives and inadequate response capabilities. This scenario presents a critical challenge: how can defenders stay one step ahead of threats without compromising operational efficiency and security effectiveness? The problem is the need for intrusion detection systems that react to known threats and anticipate and adapt to emerging threats in real-time. This work examines the implementation and effectiveness of an adaptive intrusion detection system using deep learning algorithms to strengthen cybersecurity. The research focused on evaluating the system’s ability to identify and neutralize cyber threats more efficiently and accurately than traditional methods. Quantitative analysis showed that AIDS significantly improved in several key metrics: precision increased by 12.5%, reaching 90%, while recall enhanced by 13.3%, reaching 85%. Furthermore, the F1-score experienced an increase of 12.9%, settling at 87.5%. Qualitative evaluations complemented these results through case studies and testimonials from IT staff, which corroborated the improvement in the detection and response to security incidents. The results reveal that the adaptive intrusion detection system, with its machine learning approach, not only improves threat detection and management but also optimizes operational efficiency, reducing false positives and accelerating response times.