Design and Implementation of an Efficient Intrusion Detection System Using Deep Learning for Enhanced Cybersecurity: A Comprehensive Survey

Abstract

The need to develop advanced DSS to identify both known and unknown threats has stemmed up due to the rising rate of cyber-attacks. Conventional IDS, such as signature and anomaly detection systems have a few shortcomings, such as large false-positive levels, inflexibility to zero- day attacks and low scalability. This paper surveys in detail the deep learning-based IDS techniques, and examines the contribution made by the deep learning-based IDS techniques to various environments, including Internet of Things, cloud computing and industrial control systems. It discusses the different architectures of CNN, RNN, LSTM, autoencoders and hybrid models in detail. The problems of the real-life implementation, benchmark datasets, and comparative studies with a focus on the gains of the detection accuracy and real-time response are outlined. Revenues of such improvement, there are still challenges that require improvement, such as the complexity of computation, the imbalance of the data, the vulnerability of adversarial and the limitations of deployment. The paper ends by recommending the trends of future investigation in the fields of lightweight model generation, federated learning and adversarial robust architecture, to next-generation IDS solutions.