Adaptive AI for Intrusion Detection Using Hybrid Deep Learning Architectures to Minimize False Positives and Evolving Threats
Rajendar Thatikanti, P Praveen · 2025
Network security depends on intrusion detection systems (IDS), but conventional methods frequently have trouble adjusting to changing cyber threats and achieving low false positive rates. By creating an adaptive hybrid deep learning architecture that incorporates Long Short-Term Memory (LSTM) networks for identifying temporal patterns in network traffic data and Convolutional Neural Networks (CNNs) for feature extraction, this research tackles these issues. To provide resilience against new threats, the suggested model integrates an adaptive learning mechanism that allows it to continuously update itself with real-time data. Using data preprocessing approaches to maximize performance, the CNN-LSTM model is trained on benchmark datasets and then fine-tuned using recent attack data. The results demonstrate a 95% detection accuracy and a 25% decrease in false positives, greatly surpassing both standalone deep learning models and conventional machine learning. The results show that the model can enhance detection accuracy and adjust to changing network conditions; real-time deployment and zero-day attack detection will be the main areas of future research.