عرض عادي

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

بواسطة:المساهم (المساهمين):نوع المادة : نصنصاللغة: الإنجليزية السلاسل:SpringerBriefs on cyber security systems and networksالناشر:Singapore : Springer, 2018وصف:xvii, 79 pages: illustrations ; 24 cmنوع المحتوى:
  • text
نوع الوسائط:
  • unmediated
نوع الناقل:
  • volume
تدمك:
  • 9789811314438
  • 9789811314445
  • 9811314446
الموضوع:تصنيف مكتبة الكونجرس:
  • Q325.5 .K56 2018
المحتويات:
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
ملخص: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
المقتنيات
نوع المادة المكتبة الحالية رقم الطلب رقم النسخة حالة تاريخ الإستحقاق الباركود
كتاب كتاب UAE Federation Library | مكتبة اتحاد الإمارات General Collection | المجموعات العامة Q325.5 .K56 2018 (إستعراض الرف(يفتح أدناه)) C.1 Library Use Only | داخل المكتبة فقط 30020000207212

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

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