Review of Deep Learning - Based Intrusion Detection Systems for IoT Environments
This review examines the use of deep learning techniques in Intrusion Detection Systems (IDS) for IoT environments. The rapid expansion of IoT devices has led to increased vulnerability to cyber-attacks, making effective IDS crucial. The paper provides an overview of deep learning models such as Convolutional Neural Networks (CNNs) and others, highlighting their application in detecting network intrusions. The study aims at reviewing the effectiveness of deep learning models in IoT environments, identify the technical challenges and limitations, and suggest future research directions. Key findings from the different DL techniques such as CNNs, Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Generative Adversarial Networks (GANs) shows a high level of robustness in mitigating against different kinds of cyber threats. In IoT environment, data could appeared imbalanced in most cases which could lead to biased models that perform poorly in detecting rare but critical security threats. However, creating standardized and comprehensive datasets for IoT intrusion detection can facilitate better benchmarking and comparison of different deep learning approaches. While deep learning models significantly improve detection accuracy and adaptability, they also face challenges related to computational constraints, data quality, and vulnerability to adversarial attacks. The review underscores the need for lightweight and robust models that can operate within the limited resources of IoT devices. The review concludes that ongoing advancements in deep learning, such as federated learning and edge computing, hold promise for enhancing IDS in IoT. However, further research is necessary to address existing gaps, including the development of standardized datasets and improved resilience against sophisticated cyber threats.
Download Paper