## 1 Introduction
### Paper Motivation, Contribution, and Organization  
As edge and industrial IoT systems grow in complexity and ubiquity, the attack surface continues to expand, making these networks prime targets for increasingly sophisticated cyber threats. Traditional intrusion detection methods often rely on static signatures or shallow heuristics, which fall short in capturing the dynamic and evolving patterns characteristic of real-world attack scenarios. Moreover, high-dimensional, imbalanced, and noisy IIoT data pose significant challenges to standalone deep learning models, resulting in unstable training, poor generalization, and high false-positive rates. There is a critical need for intrusion detection frameworks that can balance robustness, adaptability, and scalability, especially in edge environments where computational resources are limited and real-time responses are essential. This study is motivated by the convergence of three complementary capabilities: the temporal learning strength of transformers, the synthetic data generation capacity of generative adversarial networks, and the denoising and anomaly sensitivity of autoencoders. When combined within a unified architecture and enhanced through metaheuristic optimization, these components offer a promising path toward resilient, adaptive, and resource-aware intrusion detection for IIoT. We aim to address the limitations of current models by proposing a hybrid, optimized framework that learns both contextual dependencies and data distributions, while being capable of self-tuning for optimal performance across heterogeneous datasets and evolving attack vectors. The primary contributions of this paper are as follows:  
* We propose a novel Transformer-GAN-AE hybrid framework for intrusion detection in Edge and IIoT environments, combining the strengths of three complementary deep learning components.
* We integrate an enhanced Chimp Optimization Algorithm (IChoA) that introduces a fifth elite agent (Balancer) for adaptive hyperparameter tuning across all model modules, improving convergence stability and model generalization.
* We utilize three diverse and comprehensive datasets, WUSTL-IIoT-2021, EdgeIIoTset, and TON_IoT, to evaluate our model's robustness and cross-domain performance under both binary and multiclass classification scenarios.
* Our architecture is rigorously benchmarked against six state-of-the-art baselines, including CNN, DBN, GAN, TST, BERT, and XGBoost, demonstrating consistent outperformance across accuracy, recall, and AUC metrics.
* We conduct detailed ablation and convergence analysis to demonstrate the individual and synergistic contributions of the Transformer, GAN, and AE modules, as well as the impact of optimization via IChoA.  
The remainder of the paper is structured as follows: Section 2 details the methodology, covering the design and integration of the Transformer, GAN, AE, and IChoA modules. Section 3 outlines the experimental setup and presents the evaluation metrics and baseline models used for comparison. Section 4 discusses the results, including performance benchmarks, ablation studies, and convergence behavior. Finally, Section 5 concludes the paper and outlines future research directions.