عنوان

Network Intrusion Detection using Deep Learning :

شابک

9789811314445

شابک

9789811314452

شابک

9811314446

شابک

9811314454

شابک اشتباه

9789811314438

شابک اشتباه

9811314438

عنوان اصلي

Network Intrusion Detection using Deep Learning :

نام عام مواد

[Book]

ساير اطلاعات عنواني

a Feature Learning Approach /

نام نخستين پديدآور

Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja.

محل نشرو پخش و غیره

Singapore :

نام ناشر، پخش کننده و غيره

Springer,

تاریخ نشرو بخش و غیره

2018.

نام خاص و کميت اثر

1 online resource

عنوان فروست

Springer briefs on cyber security systems and networks

متن يادداشت

Includes bibliographical references and index.

متن يادداشت

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.

متن يادداشت

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.

متن يادداشت

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.

متن يادداشت

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.

متن يادداشت

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.

بدون عنوان

0

بدون عنوان

8

بدون عنوان

8

بدون عنوان

8

بدون عنوان

8

متن يادداشت

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.

منبع سفارش / آدرس اشتراک

Springer Nature

شماره انبار

com.springer.onix.9789811314445

شماره استاندارد بين المللي کتاب و موسيقي

9789811314438

شماره استاندارد بين المللي کتاب و موسيقي

9789811314452

موضوع مستند نشده

Computer security.

موضوع مستند نشده

Data mining.

موضوع مستند نشده

Intrusion detection systems (Computer security)

موضوع مستند نشده

Machine learning.

موضوع مستند نشده

Artificial intelligence.

موضوع مستند نشده

Computer security.

موضوع مستند نشده

Computer security.

موضوع مستند نشده

COMPUTERS-- General.

موضوع مستند نشده

Data mining.

موضوع مستند نشده

Data mining.

موضوع مستند نشده

Databases.

موضوع مستند نشده

Intrusion detection systems (Computer security)

موضوع مستند نشده

Machine learning.

موضوع مستند نشده

WAP (wireless) technology.

موضوع مستند نشده

COM-- 000000

موضوع مستند نشده

UR

موضوع مستند نشده

UR

شماره

ويراست

شماره رده

مستند نام اشخاص تاييد نشده

Kim, Kwangjo

مستند نام اشخاص تاييد نشده

Aminanto, Muhamad Erza

مستند نام اشخاص تاييد نشده

Tanuwidjaja, Harry Chan

تاريخ عمليات

20200823224852.0

قواعد فهرست نويسي ( بخش توصيفي )

pn

نام الکترونيکي

نوع ماده

[Book]

تكميل شده

Y