Intelligent Cyber Defense Systems Using Deep Learning for Network Threat Detection
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The proliferation of network-based attacks has created a critical need for advanced, intelligent, and automated cyber defense systems. Traditional security solutions, such as firewalls and signature-based intrusion detection systems (IDS), are increasingly insufficient to counter the dynamic and sophisticated nature of modern cyber threats. This chapter explores the application of deep learning models for network threat detection, providing a comprehensive overview of the foundations, recent advances, and practical applications of these techniques. We delve into the use of various deep learning architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid models, for analyzing network traffic data and identifying malicious activities. A novel hybrid deep learning model is proposed for enhanced threat detection, and its performance is evaluated using the benchmark NSL-KDD dataset. The results demonstrate the superior accuracy and efficiency of deep learning-based approaches in comparison to traditional methods, highlighting their potential to revolutionize the field of cybersecurity. The chapter concludes with a discussion of the challenges and future research directions in this rapidly evolving domain.
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Deep Learning: Foundations, Advances, and Intelligent Applications
