Enhancing Cloud-Based IDS using Deep Learning and Real-Time Threat Modeling

The increasingly dynamic and advanced cyberattacks, alongside the large number of them, have contributed to a higher and higher level of incompetency of traditional intrusion detection systems (IDS) in the protection of the modern network infrastructure. Although deep-learning methods are helpful and have a high learning capacity for spotting intricate patterns in network data, they are nonetheless vulnerable to hyperparameter training. In order to address this problem, we will provide SwarmDL-IDS, a novel intrusion detection system that optimizes the detection capability by tuning the model's parameters using particle swarm optimization (PSO). These crucial hyperparameters—learning rate, number of layers, and batch size—will be automatically adjusted by the suggested method, enabling the deep neural network's algorithm to learn effectively across potentially disparate input features. In comparison to both the conventional and baseline forms, the studies conducted on the data sets used as benchmarks NSL-KDD and CIC-IDS2017 demonstrate that SwarmDL-IDS is distinguished by a substantial degree of detection accuracy. Moreover, the optimization solution through PSO curtail the training time and enhance model stability. Their findings confirm the efficiency of using swarm intelligence with deep learning which makes SwarmDL-IDS an intelligent and scalable solution to improve the cyber defense system in constantly changing and high-risk settings.