Advanced Deep Learning Framework for Anomaly Detection in Heterogeneous Networks Using Ensemble Methods and Nature-Inspired Optimization

Anomaly detection in heterogeneous networks has become critical for modern cybersecurity infrastructure. This paper presents an Advanced Ensemble Deep Learning Framework (AEDLF) that integrates Convolutional Neural Networks (CNN), VGG-19, ResNet, nature-inspired optimization algorithms, and reinforcement learning to achieve superior anomaly detection performance. The framework addresses the limitations of traditional machine learning approaches by employing deep feature extraction combined with Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and other bio-inspired algorithms for intelligent feature selection. We evaluate our approach on three benchmark datasets: KDD Cup 1999 (small and full variants), and IDS 2018, achieving state-of-the-art results with 99.67% accuracy, 99.56% sensitivity, and 99.34% specificity. The proposed AEDLF reduces false positives by 43.9% through optimized feature dimensionality reduction and executes inference in 298.45ms. Additionally, we integrate generative AI components for adversarial robustness, prompt engineering for explainability, and federated learning for privacy-preserving distributed detection. This paper contributes novel insights into multi-modal attack detection, including advanced handling of Brute-force, Heartbleed, Botnet, DoS, DDoS, Web attacks, and Infiltration variants.

Keywords: Anomaly Detection, Deep Learning, Ensemble Methods, Feature Selection, Nature-Inspired Algorithms, Reinforcement Learning, Generative AI, Network Security, Federated Learning, CNN.