5.1.5 Long Short-Term Memory Recurrent Neural Network5.2 Discriminative; 5.2.1 Deep Neural Network in Software-Defined Networks; 5.2.2 Recurrent Neural Network; 5.2.3 Convolutional Neural Network; 5.2.4 Long Short-Term Memory Recurrent Neural Network; 5.2.4.1 LSTM-RNN Staudemeyer; 5.2.4.2 LSTM-RNN for Collective Anomaly Detection; 5.2.4.3 GRU in IoT; 5.2.4.4 LSTM-RNN for DDoS; 5.3 Hybrid; 5.3.1 Adversarial Networks; 5.4 Deep Reinforcement Learning; 5.5 Comparison; References; 6 Deep Feature Learning; 6.1 Deep Feature Extraction and Selection; 6.1.1 Methodology; 6.1.2 Evaluation.
6.1.2.1 Dataset Preprocessing6.1.2.2 Experimental Result; 6.2 Deep Learning for Clustering; 6.2.1 Methodology; 6.2.2 Evaluation; 6.3 Comparison; References; 7 Summary and Further Challenges; References; Appendix A A Survey on Malware Detection from Deep Learning; A.1 Automatic Analysis of Malware BehaviorUsing Machine Learning; A.2 Deep Learning for Classification of Malware System Call Sequences; A.3 Malware Detection with Deep Neural Network Using Process Behavior; A.4 Efficient Dynamic Malware Analysis Based on Network Behavior Using Deep Learning.
A.5 Automatic Malware Classification and New Malware Detection Using Machine LearningA. 6 DeepSign: Deep Learning for Automatic Malware Signature Generation and Classification; A.7 Selecting Features to Classify Malware; A.8 Analysis of Machine-Learning Techniques Used in Behavior-Based Malware Detection; A.9 Malware Detection Using Machine-Learning-Based Analysis of Virtual Memory Access Patterns; A.10 Zero-Day Malware Detection; References.
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This book presents recent advances in intrusion detection systems (IDSs) using state-of-the-art deep learning methods. It also provides a systematic overview of classical machine learning and the latest developments in deep learning. In particular, it discusses deep learning applications in IDSs in different classes: generative, discriminative, and adversarial networks. Moreover, it compares various deep learning-based IDSs based on benchmarking datasets. The book also proposes two novel feature learning models: deep feature extraction and selection (D-FES) and fully unsupervised IDS. Further challenges and research directions are presented at the end of the book. Offering a comprehensive overview of deep learning-based IDS, the book is a valuable reerence resource for undergraduate and graduate students, as well as researchers and practitioners interested in deep learning and intrusion detection. Further, the comparison of various deep-learning applications helps readers gain a basic understanding of machine learning, and inspires applications in IDS and other related areas in cybersecurity.