Hybrid deep learning system for network anomaly detection based on feedback processes.

In the rapidly evolving landscape of digital communication, ensuring the reliability and security of networks is paramount, with the escalating volume and complexity of network traffic necessitating robust anomaly detection mechanisms to safeguard against potential threats and disruptions. This paper introduces a novel hybrid deep learning system designed for effective network anomaly detection, employing a two-stage approach. The first stage utilizes a Convolutional Neural Network (CNN) as a binary classifier to identify broad anomalous patterns in network traffic. Subsequently, the second stage employs a Deep Neural Network (DNN) as a multi-class classifier to further categorize the detected anomalies into specific classes. Notably, a unique feedback mechanism is incorporated into the system, leveraging false results, including false negatives and false positives, to iteratively refine the model through retraining and reclassification, thereby mitigating false results. An innovative aspect of the proposed approach lies in the incorporation of a feedback loop that addresses false results. False negatives and false positives identified during the classification process are used to augment the training dataset, allowing the system to learn from its own mistakes. The model is then retrained, and the classification process is repeated, resulting in a more resilient and adaptive anomaly detection system. To evaluate the performance of the proposed hybrid system, extensive experiments are conducted using the CICIDS2017 dataset, a widely recognized benchmark in the field of cybersecurity. Comparative analyses with existing methods showcase the superior capabilities of the hybrid system in terms of accuracy, false positive reduction, and adaptability to evolving network threats. The results indicate that the hybrid deep learning system not only outperforms traditional approaches but also exhibits an enhanced ability to learn and adapt from misclassifications, thereby continually improving its performance over time. In conclusion, the presented two-stage hybrid deep learning system, coupled with the adaptive feedback mechanism, represents a significant advancement in network anomaly detection, promising heightened accuracy and robustness in the ever-evolving landscape of cybersecurity. [ABSTRACT FROM AUTHOR]