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BL
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| Record Number
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889909
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| Main Entry
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Kim, Kwangjo
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| Title & Author
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Network Intrusion Detection using Deep Learning : : a Feature Learning Approach /\ Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja.
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| Publication Statement
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Singapore :: Springer,, 2018.
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| Series Statement
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Springer briefs on cyber security systems and networks
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| Page. NO
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1 online resource
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| ISBN
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9789811314445
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: 9789811314452
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: 9811314446
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: 9811314454
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9789811314438
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9811314438
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| Bibliographies/Indexes
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Includes bibliographical references and index.
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| Contents
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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.
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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.
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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.
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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.
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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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| Abstract
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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.
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| Subject
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Computer security.
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Data mining.
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Intrusion detection systems (Computer security)
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Machine learning.
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Artificial intelligence.
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Computer security.
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Computer security.
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COMPUTERS-- General.
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Data mining.
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Data mining.
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Databases.
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Intrusion detection systems (Computer security)
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Machine learning.
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WAP (wireless) technology.
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| Dewey Classification
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006.3/1
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| LC Classification
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Q325.5
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| Added Entry
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Aminanto, Muhamad Erza
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Tanuwidjaja, Harry Chan
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