Network intrusion detection using Deep Learning : a feature learning approach / Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja

Includes bibliographical references and index

Intro; Preface; Acknowledgments; Contents; Acronyms; 1 Introduction; References; 2 Intrusion Detection Systems; 2.1 Definition; 2.2 Classification; 2.3 Benchmark; 2.3.1 Performance Metric; 2.3.2 Public Dataset; References; 3 Classical Machine Learning and Its Applications to IDS; 3.1 Classification of Machine Learning; 3.1.1 Supervised Learning; 3.1.1.1 Support Vector Machine; 3.1.1.2 Decision Tree; 3.1.2 Unsupervised Learning; 3.1.2.1 K-Means Clustering; 3.1.2.2 Ant Clustering; 3.1.2.3 (Sparse) Auto-Encoder; 3.1.3 Semi-supervised Learning; 3.1.4 Weakly Supervised Learning

3.1.5 Reinforcement Learning3.1.6 Adversarial Machine Learning; 3.2 Machine-Learning-Based Intrusion Detection Systems; References; 4 Deep Learning; 4.1 Classification; 4.2 Generative (Unsupervised Learning); 4.2.1 Stacked (Sparse) Auto-Encoder; 4.2.2 Boltzmann Machine; 4.2.3 Sum-Product Networks; 4.2.4 Recurrent Neural Networks; 4.3 Discriminative; 4.4 Hybrid; 4.4.1 Generative Adversarial Networks (GAN); References; 5 Deep Learning-Based IDSs; 5.1 Generative; 5.1.1 Deep Neural Network; 5.1.2 Accelerated Deep Neural Network; 5.1.3 Self-Taught Learning; 5.1.4 Stacked Denoising Auto-Encoder

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

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