Intelligent Data Analytics for Terror Threat Prediction (eBook, PDF)

Architectures, Methodologies, Techniques, and Applications
Redaktion: Pani, Subhendu Kumar; Zhang, Xiaobo; Pachori, Ram Bilas; Garg, Lalit; Singh, Sanjay Kumar

• Format: PDF

Produktbeschreibung

Intelligent data analytics for terror threat prediction is an emerging field of research at the intersection of information science and computer science, bringing with it a new era of tremendous opportunities and challenges due to plenty of easily available criminal data for further analysis. This book provides innovative insights that will help obtain interventions to undertake emerging dynamic scenarios of criminal activities. Furthermore, it presents emerging issues, challenges and management strategies in public safety and crime control development across various domains. The book will play a vital role in improvising human life to a great extent. Researchers and practitioners working in the fields of data mining, machine learning and artificial intelligence will greatly benefit from this book, which will be a good addition to the state-of-the-art approaches collected for intelligent data analytics. It will also be very beneficial for those who are new to the field and need to quickly become acquainted with the best performing methods. With this book they will be able to compare different approaches and carry forward their research in the most important areas of this field, which has a direct impact on the betterment of human life by maintaining the security of our society. No other book is currently on the market which provides such a good collection of state-of-the-art methods for intelligent data analytics-based models for terror threat prediction, as intelligent data analytics is a newly emerging field and research in data mining and machine learning is still in the early stage of development.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 352
• Erscheinungstermin: 31. Dezember 2020
• Englisch
• ISBN-13: 9781119711612
• Artikelnr.: 60938488

Autorenporträt

Subhendu Kumar Pani received his PhD from Utkal University Odisha, India in 2013. He is a professor in the Department of Computer Science & Engineering, Orissa Engineering College (OEC), Bhubaneswar, India. He has published more than 50 articles in international journals, authored 5 books and edited 2 volumes. Sanjay Kumar Singh is a professor in the Department of Computer Science and Engineering at the Indian Institute of Technology, Varanasi. He has published more than 130 international publications, 4 edited books and 2 patents. Lalit Garg received his PhD from the University of Ulster, UK in Computing and Information Engineering. He is a senior lecturer in Computer Information Systems, University of Malta, Malta. Ram Bilas Pachori received his PhD degree in Electrical Engineering from the Indian Institute of Technology (IIT) Kanpur, India in 2008. He is now a professor of Electrical Engineering, IIT Indore, India. He has more than 170 publications which include journal papers, conference papers, books, and book chapters. Xiaobo Zhang obtained his Master of Computer Science, Doctor of Engineering (Control Theory and Control Engineering) and is now working in the Internet of Things Department of Automation, Guangdong University of Technology, China. He has published more than 30 journal articles, edited 3 books, and has applied for more than 40 invention patents and obtained 6 software copyrights.

Inhaltsangabe

Preface xv

1 Rumor Detection and Tracing its Source to Prevent Cyber-Crimes on Social Media 1
Ravi Kishore Devarapalli and Anupam Biswas

1.1 Introduction 2

1.2 Social Networks 4

1.2.1 Types of Social Networks 4

1.3 What is Cyber-Crime? 7

1.3.1 Definition 7

1.3.2 Types of Cyber-Crimes 7

1.3.2.1 Hacking 7

1.3.2.2 Cyber Bullying 7

1.3.2.3 Buying Illegal Things 8

1.3.2.4 Posting Videos of Criminal Activity 8

1.3.3 Cyber-Crimes on Social Networks 8

1.4 Rumor Detection 9

1.4.1 Models 9

1.4.1.1 Naïve Bayes Classifier 10

1.4.1.2 Support Vector Machine 13

1.4.2 Combating Misinformation on Instagram 14

1.5 Factors to Detect Rumor Source 15

1.5.1 Network Structure 15

1.5.1.1 Network Topology 16

1.5.1.2 Network Observation 16

1.5.2 Diffusion Models 18

1.5.2.1 SI Model 18

1.5.2.2 SIS Model 19

1.5.2.3 SIR Model 19

1.5.2.4 SIRS Model 20

1.5.3 Centrality Measures 21

1.5.3.1 Degree Centrality 21

1.5.3.2 Closeness Centrality 21

1.5.3.3 Betweenness Centrality 22

1.6 Source Detection in Network 22

1.6.1 Single Source Detection 23

1.6.1.1 Network Observation 23

1.6.1.2 Query-Based Approach 25

1.6.1.3 Anti-Rumor-Based Approach 26

1.6.2 Multiple Source Detection 26

1.7 Conclusion 27

References 28

2 Internet of Things (IoT) and Machine to Machine (M2M) Communication Techniques for Cyber Crime Prediction 31
Jaiprakash Narain Dwivedi

2.1 Introduction 32

2.2 Advancement of Internet 33

2.3 Internet of Things (IoT) and Machine to Machine (M2M) Communication 34

2.4 A Definition of Security Frameworks 38

2.5 M2M Devices and Smartphone Technology 39

2.6 Explicit Hazards to M2M Devices Declared by Smartphone Challenges 41

2.7 Security and Privacy Issues in IoT 43

2.7.1 Dynamicity and Heterogeneity 43

2.7.2 Security for Integrated Operational World with Digital World 44

2.7.3 Information Safety with Equipment Security 44

2.7.4 Data Source Information 44

2.7.5 Information Confidentiality 44

2.7.6 Trust Arrangement 44

2.8 Protection in Machine to Machine Communication 48

2.9 Use Cases for M2M Portability 52

2.10 Conclusion 53

References 54

3 Crime Predictive Model Using Big Data Analytics 57
Hemanta Kumar Bhuyan and Subhendu Kumar Pani

3.1 Introduction 58

3.1.1 Geographic Information System (GIS) 59

3.2 Crime Data Mining 60

3.2.1 Different Methods for Crime Data Analysis 62

3.3 Visual Data Analysis 63

3.4 Technological Analysis 65

3.4.1 Hadoop and MapReduce 65

3.4.1.1 Hadoop Distributed File System (HDFS) 65

3.4.1.2 MapReduce 65

3.4.2 Hive 67

3.4.2.1 Analysis of Crime Data using Hive 67

3.4.2.2 Data Analytic Module With Hive 68

3.4.3 Sqoop 68

3.4.3.1 Pre-Processing and Sqoop 68

3.4.3.2 Data Migration Module With Sqoop 68

3.4.3.3 Partitioning 68

3.4.3.4 Bucketing 68

3.4.3.5 R-Tool Analyse Crime Data 69

3.4.3.6 Correlation Matrix 69

3.5 Big Data Framework 69

3.6 Architecture for Crime Technical Model 72

3.7 Challenges 73

3.8 Conclusions 74

References 75

4 The Role of Remote Sensing and GIS in Military Strategy