<p>With the rapid expansion of cloud computing and the increasing complexity of network traffic, security has become one of the fundamental requirements of these infrastructures. In this context, intrusion detection plays an important role in identifying malicious activities and reducing the damage caused by cyber attacks. Despite recent advancements, many intrusion detection systems still face challenges such as the presence of redundant features, high correlation among data, and dependence on local optima, which can reduce their efficiency in real-world environments. In this research, a hybrid intelligent framework for intrusion detection in cloud environments is presented, based on feature dimensionality reduction using principal component analysis (PCA), classification with a multilayer perceptron (MLP) neural network, and evolutionary optimization of network parameters using a genetic algorithm (GA). In the proposed method, network traffic data are first normalized, then dimensionality reduction is applied to reduce computational complexity and eliminate redundancy and correlation among features. Subsequently, the neural network parameters are dynamically optimized by the genetic algorithm to prevent getting stuck in local optima. The experimental evaluation of the proposed framework was conducted on three standard and widely used datasets: NSL-KDD, CIC-IDS2017, and UNSW-NB15, which include various types of attacks such as DoS/DDoS, Probe, R2L, and U2R. The results show that the proposed method achieved an accuracy of 99.55% on the NSL-KDD dataset, 98.92% on the CIC-IDS2017 dataset, and 99.10% on the UNSW-NB15 dataset. In addition to providing stable performance across all three datasets, it simultaneously improved the Precision and Recall metrics and reduced the false positive rate. These results indicate the effectiveness of the proposed framework and its suitable generalizability for implementation in real-world intrusion detection scenarios in complex and dynamic cloud environments.</p>