IntruDetect: A Hybrid Deep Learning Approach for Intrusion Detection in Network Security

In today’s world, most of our activities depend on cloud or network-based environments. Modern networks produce substantial quantities of data through rapid connectivity and various devices. Cyberattacks are becoming more sophisticated due to modern AI technology exploiting weaknesses in systems, networks, and devices. This rapid evolution of cyber threats shows us the necessity of advanced intrusion detection systems (IDS), which should be capable of identifying known and unknown attacks with the highest accuracy and minimal false positive rates. Current intrusion detection systems encounter performance limitations and scalability challenges in the processing and analysis of the data. To address these issues, we have presented a hybrid deep learning model that integrates Convolutional Neural Network (CNN), Growth Optimizer Algorithm, and Recurrent Neural Network (RNN) to enhance intrusion detection capabilities. CNNs are used to extract spatial features from network traffic data, while the Growth Optimizer is used to select the most relevant features dynamically to improve model efficiency. To capture temporal dependencies in sequential data, we used RNN, specifically Gated Recurrent Units (GRUs), which can easily detect complex attack patterns. We utilized three benchmark datasets to assess this model: KDDCUP’99, NSL-KDD, and Bot-IoT. We obtained exceptional results after evaluating the model using these datasets, achieving 99.92% accuracy on KDDCUP’99, 99.76% accuracy on NSL-KDD, and 100.00% accuracy on Bot-IoT. This hybrid methodology outperformed most of the conventional machine learning techniques and current deep learning-based intrusion detection systems, providing a robust and scalable solution for contemporary cybersecurity issues.