MEMFO-KELM: A Deep Learning Approach for Real-Time Intrusion Detection in Cloud Environments

Abstract

With the increasing importance of network security in cloud environments, effective intrusion detection systems (IDS) are crucial for safeguarding data transmission and protecting against cyber attacks. This research proposes a novel IDS model named Mutation Enhanced Moth-Flame Optimization based Kernel Extreme Learning Machine (MEMFO-KELM) algorithm. The objective is to address the challenges associated with cloud environments and enhance the accuracy of intrusion detection in real-time scenarios. The MEMFO-KELM algorithm incorporates advanced techniques to improve classification performance and ensure accurate detection. The deep learning model architecture includes convolutional layers for capturing complex patterns, recurrent layers for modelling temporal dependencies, and fully connected layers for feature extraction. L1 regularization (Lasso) is applied to the fully connected layers to enhance interpretability and reduce model complexity. Here, four datasets are represented to validate the performance with different attacks and normal classes are implemented: MQTT-IoT-IDS2020, CSE-CIC-IDS2018, NSL-KDD, and Apache Web Server. The proposed IDS model achieves remarkable performance, with an accuracy of 98.4%, a false alarm rate of 0.056, a detection rate of 98%, and a computational time of 120 s. These results highlight the effectiveness of the MEMFO-KELM algorithm in accurately detecting intrusions. Comparative analysis reveals that the proposed real-time IDS model demonstrates its superiority compared to other methods. By addressing the challenges of cloud environments and incorporating advanced techniques, the MEMFO-KELM IDS model contributes to enhanced network security, mitigates the risks associated with cyber attacks, and ensures the integrity of globally distributed resources.