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Intelligent Data Analytics for Terror Threat Prediction: Architectures, Methodologies, Techniques, and Applications [Hardback]

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  • Formāts: Hardback, 352 pages, height x width x depth: 10x10x10 mm, weight: 454 g
  • Izdošanas datums: 09-Feb-2021
  • Izdevniecība: Wiley-Scrivener
  • ISBN-10: 1119711096
  • ISBN-13: 9781119711094
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Intelligent Data Analytics for Terror Threat Prediction: Architectures, Methodologies, Techniques, and ApplicationsPalielināt
  • Formāts: Hardback, 352 pages, height x width x depth: 10x10x10 mm, weight: 454 g
  • Izdošanas datums: 09-Feb-2021
  • Izdevniecība: Wiley-Scrivener
  • ISBN-10: 1119711096
  • ISBN-13: 9781119711094
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781119711094.html
Keywords:
"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. The aim of data analytics is to prevent threats before they happen using classical statistical issues, machine learning, artificial intelligence, rule induction methods, neural networks, fuzzy logic, and stochasticsearch methods on various data sources, including social media, GPS devices, video feed from street cameras; and license plate readers, travel and credit card records and the news media, as well as government and proprietary systems. Intelligent data analytics ensures efficient data mining techniques to solve criminal investigations. Prediction of future terrorist attacks according to city, type of attack, target and weapon, claim mode, and motive for attack through classification techniques will facilitate the decision-making process of security organizations so as to learn from previously stored attack information; and then rate the targeted sectors/areas accordingly for security measures. By using intelligent data analytics models with multiple levelsof representation, raw to higher abstract level representation can be learned at each level of the system. Algorithms based on intelligent data analytics have demonstrated great performance in a variety of areas, including data visualization, data pre-processing (fusion, editing, transformation, filtering, and sampling), data engineering, database mining techniques, tools and applications, etc"--

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.

Preface xv
1 Rumor Detection and Tracing its Source to Prevent Cyber-Crimes on Social Media
1(30)
Ravi Kishore Devarapalli
Anupam Biswas
1.1 Introduction
2(2)
1.2 Social Networks
4(3)
1.2.1 Types of Social Networks
4(3)
1.3 What Is Cyber-Crime?
7(2)
1.3.1 Definition
7(1)
1.3.2 Types of Cyber-Crimes
7(1)
1.3.2.1 Hacking
7(1)
1.3.2.2 Cyber Bullying
7(1)
1.3.2.3 Buying Illegal Things
8(1)
1.3.2.4 Posting Videos of Criminal Activity
8(1)
1.3.3 Cyber-Crimes on Social Networks
8(1)
1.4 Rumor Detection
9(6)
1.4.1 Models
9(1)
1.4.1.1 Naive Bayes Classifier
10(3)
1.4.1.2 Support Vector Machine
13(1)
1.4.2 Combating Misinformation on Instagram
14(1)
1.5 Factors to Detect Rumor Source
15(7)
1.5.1 Network Structure
15(1)
1.5.1.1 Network Topology
16(1)
1.5.1.2 Network Observation
16(2)
1.5.2 Diffusion Models
18(1)
1.5.2.1 SI Model
18(1)
1.5.2.2 SIS Model
19(1)
1.5.2.3 SIR Model
19(1)
1.5.2.4 SIRS Model
20(1)
1.5.3 Centrality Measures
21(1)
1.5.3.1 Degree Centrality
21(1)
1.5.3.2 Closeness Centrality
21(1)
1.5.3.3 Betweenness Centrality
22(1)
1.6 Source Detection in Network
22(5)
1.6.1 Single Source Detection
23(1)
1.6.1.1 Network Observation
23(2)
1.6.1.2 Query-Based Approach
25(1)
1.6.1.3 Anti-Rumor-Based Approach
26(1)
1.6.2 Multiple Source Detection
26(1)
1.7 Conclusion
27(4)
References
28(3)
2 Internet of Things (IoT) and Machine to Machine (M2M) Communication Techniques for Cyber Crime Prediction
31(26)
Jaiprakash Narain Dwivedi
2.1 Introduction
32(1)
2.2 Advancement of Internet
33(1)
2.3 Internet of Things (IoT) and Machine to Machine (M2M) Communication
34(4)
2.4 A Definition of Security Frameworks
38(1)
2.5 M2M Devices and Smartphone Technology
39(2)
2.6 Explicit Hazards to M2M Devices Declared by Smartphone Challenges
41(2)
2.7 Security and Privacy Issues in IoT
43(5)
2.7.1 Dynamicity and Heterogeneity
43(1)
2.7.2 Security for Integrated Operational World with Digital World
44(1)
2.7.3 Information Safety with Equipment Security
44(1)
2.7.4 Data Source Information
44(1)
2.7.5 Information Confidentiality
44(1)
2.7.6 Trust Arrangement
44(4)
2.8 Protection in Machine to Machine Communication
48(4)
2.9 Use Cases for M2M Portability
52(1)
2.10 Conclusion
53(4)
References
54(3)
3 Crime Predictive Model Using Big Data Analytics
57(22)
Hemanta Kumar Bhuyan
Subhendu Kumar Parti
3.1 Introduction
58(2)
3.1.1 Geographic Information System (GIS)
59(1)
3.2 Crime Data Mining
60(3)
3.2.1 Different Methods for Crime Data Analysis
62(1)
3.3 Visual Data Analysis
63(2)
3.4 Technological Analysis
65(4)
3.4.1 Hadoop and Map Reduce
65(1)
3.4.1.1 Hadoop Distributed File System (HDFS)
65(1)
3.4.1.2 MapReduce
65(2)
3.4.2 Hive
67(1)
3.4.2.1 Analysis of Crime Data using Hive
67(1)
3.4.2.2 Data Analytic Module With Hive
68(1)
3.4.3 Sqoop
68(1)
3.4.3.1 Pre-Processing and Sqoop
68(1)
3.4.3.2 Data Migration Module With Sqoop
68(1)
3.4.3.3 Partitioning
68(1)
3.4.3.4 Bucketing
68(1)
3.4.3.5 R-Tool Analyse Crime Data
69(1)
3.4.3.6 Correlation Matrix
69(1)
3.5 Big Data Framework
69(3)
3.6 Architecture for Crime Technical Model
72(1)
3.7 Challenges
73(1)
3.8 Conclusions
74(5)
References
75(4)
4 The Role of Remote Sensing and GIS in Military Strategy to Prevent Terror Attacks
79(16)
Sushobhan Majumdar
4.1 Introduction
80(1)
4.2 Database and Methods
81(1)
4.3 Discussion and Analysis
82(1)
4.4 Role of Remote Sensing and GIS
83(1)
4.5 Cartographic Model
83(4)
4.5.1 Spatial Data Management
85(1)
4.5.2 Battlefield Management
85(1)
4.5.3 Terrain Analysis
86(1)
4.6 Mapping Techniques Used for Defense Purposes
87(1)
4.7 Naval Operations
88(1)
4.7.1 Air Operations
89(1)
4.7.2 GIS Potential in Military
89(1)
4.8 Future Sphere of GIS in Military Science
89(2)
4.8.1 Defense Site Management
90(1)
4.8.2 Spatial Data Management
90(1)
4.8.3 Intelligence Capability Approach
90(1)
4.8.4 Data Converts Into Information
90(1)
4.8.5 Defense Estate Management
91(1)
4.9 Terrain Evolution
91(1)
4.9.1 Problems Regarding the Uses of Remote Sensing and GIS
91(1)
4.9.2 Recommendations
92(1)
4.10 Conclusion
92(3)
References
93(2)
5 Text Mining for Secure Cyber Space
95(24)
Supriya Raheja
Geetika Munjal
5.1 Introduction
95(2)
5.2 Literature Review
97(4)
5.2.1 Text Mining With Latent Semantic Analysis
100(1)
5.3 Latent Semantic Analysis
101(1)
5.4 Proposed Work
102(2)
5.5 Detailed Work Flow of Proposed Approach
104(7)
5.5.1 Defining the Stop Words
106(1)
5.5.2 Stemming
107(2)
5.5.3 Proposed Algorithm: A Hybrid Approach
109(2)
5.6 Results and Discussion
111(4)
5.6.1 Analysis Using Hybrid Approach
111(4)
5.7 Conclusion
115(4)
References
115(4)
6 Analyses on Artificial Intelligence Framework to Detect Crime Pattern
119(14)
R. Arshath Raja
N. Yuvaraj
N.V. Kousik
6.1 Introduction
120(1)
6.2 Related Works
121(1)
6.3 Proposed Clustering for Detecting Crimes
122(2)
6.3.1 Data Pre-Processing
123(1)
6.3.2 Object-Oriented Model
124(1)
6.3.3 MCML Classification
124(1)
6.3.4 GAA
124(1)
6.3.5 Consensus Clustering
124(1)
6.4 Performance Evaluation
124(7)
6.4.1 Precision
125(1)
6.4.2 Sensitivity
125(6)
6.4.3 Specificity
131(1)
6.4.4 Accuracy
131(1)
6.5 Conclusions
131(2)
References
132(1)
7 A Biometric Technology-Based Framework for Tackling and Preventing Crimes
133(28)
Ebrahim A.M. Alrahawe
Vikas T. Humbe
G.N. Shinde
7.1 Introduction
134(1)
7.2 Biometrics
135(9)
7.2.1 Biometric Systems Technologies
137(4)
7.2.2 Biometric Recognition Framework
141(1)
7.2.3 Biometric Applications/Usages
142(2)
7.3 Surveillance Systems (CCTV)
144(7)
7.3.1 CCTV Goals
146(1)
7.3.2 CCTV Processes
146(3)
7.3.3 Fusion of Data From Multiple Cameras
149(1)
7.3.4 Expanding the Use of CCTV
149(1)
7.3.5 CCTV Effectiveness
150(1)
7.3.6 CCTV Limitations
150(1)
7.3.7 Privacy and CCTV
150(1)
7.4 Legality to Surveillance and Biometrics vs. Privacy and Human Rights
151(2)
7.5 Proposed Work (Biometric-Based CCTV System)
153(5)
7.5.1 Biometric Surveillance System
154(1)
7.5.1.1 System Component and Flow Diagram
154(2)
7.5.2 Framework
156(2)
7.6 Conclusion
158(3)
References
159(2)
8 Rule-Based Approach for Botnet Behavior Analysis
161(20)
Supriya Raheja
Geetika Munjal
Jyoti Jangra
Rakesh Garg
8.1 Introduction
161(2)
8.2 State-of-the-Art
163(3)
8.3 Bots and Botnets
166(5)
8.3.1 Botnet Life Cycle
166(1)
8.3.2 Botnet Detection Techniques
167(1)
8.3.3 Communication Architecture
168(3)
8.4 Methodology
171(4)
8.5 Results and Analysis
175(2)
8.6 Conclusion and Future Scope
177(4)
References
177(4)
9 Securing Biometric Framework with Cryptanalysis
181(28)
Abhishek Goel
Siddharth Gautam
Nitin Tyagi
Nikhil Sharma
Martin Sagayam
9.1 Introduction
182(2)
9.2 Basics of Biometric Systems
184(8)
9.2.1 Face
185(1)
9.2.2 Hand Geometry
186(1)
9.2.3 Fingerprint
187(1)
9.2.4 Voice Detection
187(1)
9.2.5 Iris
188(1)
9.2.6 Signature
189(1)
9.2.7 Keystrokes
189(3)
9.3 Biometric Variance
192(1)
9.3.1 Inconsistent Presentation
192(1)
9.3.2 Unreproducible Presentation
192(1)
9.3.3 Fault Signal/Representational Accession
193(1)
9.4 Performance of Biometric System
193(2)
9.5 Justification of Biometric System
195(1)
9.5.1 Authentication ("Is this individual really the authenticate user or not?")
195(1)
9.5.2 Recognition ("Is this individual in the database?")
196(1)
9.5.3 Concealing ("Is this a needed person?")
196(1)
9.6 Assaults on a Biometric System
196(3)
9.6.1 Zero Effort Attacks
197(1)
9.6.2 Adversary Attacks
198(1)
9.6.2.1 Circumvention
198(1)
9.6.2.2 Coercion
198(1)
9.6.2.3 Repudiation
198(1)
9.6.2.4 DoB (Denial of Benefit)
199(1)
9.6.2.5 Collusion
199(1)
9.7 Biometric Cryptanalysis: The Fuzzy Vault Scheme
199(4)
9.8 Conclusion & Future Work
203(6)
References
205(4)
10 The Role of Big Data Analysis in Increasing the Crime Prediction and Prevention Rates
209(12)
Galal A.A. L-Rummana
Abdulrazzaq H. A. Al-Ahdal
G.N. Shinde
10.1 Introduction: An Overview of Big Data and Cyber Crime
210(1)
10.2 Techniques for the Analysis of BigData
211(5)
10.3 Important Big Data Security Techniques
216(3)
10.4 Conclusion
219(2)
References
219(2)
11 Crime Pattern Detection Using Data Mining
221(16)
Dipalika Das
Maya Nayak
11.1 Introduction
221(1)
11.2 Related Work
222(2)
11.3 Methods and Procedures
224(3)
11.4 System Analysis
227(3)
11.5 Analysis Model and Architectural Design
230(3)
11.6 Several Criminal Analysis Methods in Use
233(2)
11.7 Conclusion and Future Work
235(2)
References
235(2)
12 Attacks and Security Measures in Wireless Sensor Network
237(32)
Nikhil Sharma
Ila Kaushik
Vikash Kumar Agarwal
Bharat Bhushan
Aditya Khamparia
12.1 Introduction
238(1)
12.2 Layered Architecture of WSN
239(2)
12.2.1 Physical Layer
239(1)
12.2.2 Data Link Layer
239(1)
12.2.3 Network Layer
240(1)
12.2.4 Transport Layer
240(1)
12.2.5 Application Layer
241(1)
12.3 Security Threats on Different Layers in WSN
241(5)
12.3.1 Threats on Physical Layer
241(1)
12.3.1.1 Eavesdropping Attack
241(1)
12.3.1.2 Jamming Attack
242(1)
12.3.1.3 Imperil or Compromised Node Attack
242(1)
12.3.1.4 Replication Node Attack
242(1)
12.3.2 Threats on Data Link Layer
242(1)
12.3.2.1 Collision Attack
243(1)
12.3.2.2 Denial of Service (DoS) Attack
243(1)
12.3.2.3 Intelligent Jamming Attack
243(1)
12.3.3 Threats on Network Layer
243(1)
12.3.3.1 Sybil Attack
243(1)
12.3.3.2 Gray Hole Attack
243(1)
12.3.3.3 Sink Hole Attack
244(1)
12.3.3.4 Hello Flooding Attack
244(1)
12.3.3.5 Spoofing Attack
244(1)
12.3.3.6 Replay Attack
244(1)
12.3.3.7 Black Hole Attack
244(1)
12.3.3.8 Worm Hole Attack
245(1)
12.3.4 Threats on Transport Layer
245(1)
12.3.4.1 De-Synchronization Attack
245(1)
12.3.4.2 Flooding Attack
245(1)
12.3.5 Threats on Application Layer
245(1)
12.3.5.1 Malicious Code Attack
245(1)
12.3.5.2 Attack on Reliability
246(1)
12.3.6 Threats on Multiple Layer
246(1)
12.3.6.1 Man-in-the-Middle Attack
246(1)
12.3.6.2 Jamming Attack
246(1)
12.3.6.3 Dos Attack
246(1)
12.4 Threats Detection at Various Layers in WSN
246(6)
12.4.1 Threat Detection on Physical Layer
247(1)
12.4.1.1 Compromised Node Attack
247(1)
12.4.1.2 Replication Node Attack
247(1)
12.4.2 Threat Detection on Data Link Layer
247(1)
12.4.2.1 Denial of Service Attack
247(1)
12.4.3 Threat Detection on Network Layer
248(1)
12.4.3.1 Black Hole Attack
248(1)
12.4.3.2 Worm Hole Attack
248(1)
12.4.3.3 Hello Flooding Attack
249(1)
12.4.3.4 Sybil Attack
249(1)
12.4.3.5 Gray Hole Attack
250(1)
12.4.3.6 Sink Hole Attack
250(1)
12.4.4 Threat Detection on the Transport Layer
251(1)
12.4.4.1 Flooding Attack
251(1)
12.4.5 Threat Detection on Multiple Layers
251(1)
12.4.5.1 Jamming Attack
251(1)
12.5 Various Parameters for Security Data Collection in WSN
252(4)
12.5.1 Parameters for Security of Information Collection
252(1)
12.5.1.1 Information Grade
252(1)
12.5.1.2 Efficacy and Proficiency
253(1)
12.5.1.3 Reliability Properties
253(1)
12.5.1.4 Information Fidelity
253(1)
12.5.1.5 Information Isolation
254(1)
12.5.2 Attack Detection Standards in WSN
254(1)
12.5.2.1 Precision
254(1)
12.5.2.2 Germane
255(1)
12.5.2.3 Extensibility
255(1)
12.5.2.4 Identifiability
255(1)
12.5.2.5 Fault Forbearance
255(1)
12.6 Different Security Schemes in WSN
256(8)
12.6.1 Clustering-Based Scheme
256(1)
12.6.2 Cryptography-Based Scheme
256(1)
12.6.3 Cross-Checking-Based Scheme
256(1)
12.6.4 Overhearing-Based Scheme
257(1)
12.6.5 Acknowledgement-Based Scheme
257(1)
12.6.6 Trust-Based Scheme
257(1)
12.6.7 Sequence Number Threshold-Based Scheme
258(1)
12.6.8 Intrusion Detection System-Based Scheme
258(1)
12.6.9 Cross-Layer Collaboration-Based Scheme
258(6)
12.7 Conclusion
264(5)
References
264(5)
13 Large Sensing Data Flows Using Cryptic Techniques
269(22)
Hemanta Kumar Bhuyan
13.1 Introduction
270(1)
13.2 Data Flow Management
271(2)
13.2.1 Data Flow Processing
271(1)
13.2.2 Stream Security
272(1)
13.2.3 Data Privacy and Data Reliability
272(1)
13.2.3.1 Security Protocol
272(1)
13.3 Design of Big Data Stream
273(4)
13.3.1 Data Stream System Architecture
273(1)
13.3.1.1 Intrusion Detection Systems (IDS)
274(1)
13.3.2 Malicious Model
275(1)
13.3.3 Threat Approaches for Attack Models
276(1)
13.4 Utilization of Security Methods
277(3)
13.4.1 System Setup
278(1)
13.4.2 Re-Keying
279(1)
13.4.3 New Node Authentication
279(1)
13.4.4 Cryptic Techniques
280(1)
13.5 Analysis of Security on Attack
280(1)
13.6 Artificial Intelligence Techniques for Cyber Crimes
281(3)
13.6.1 Cyber Crime Activities
282(1)
13.6.2 Artificial Intelligence for Intrusion Detection
282(2)
13.6.3 Features of an IDPS
284(1)
13.7 Conclusions
284(7)
References
285(6)
14 Cyber-Crime Prevention Methodology
291(21)
Chandra Sekhar Biswal
Subhendu Kumar Pani
14.1 Introduction
292(5)
14.1.1 Evolution of Cyber Crime
294(2)
14.1.2 Cybercrime can be Broadly Defined as Two Types
296(1)
14.1.3 Potential Vulnerable Sectors of Cybercrime
296(1)
14.2 Credit Card Frauds and Skimming
297(2)
14.2.1 Matrimony Fraud
297(1)
14.2.2 Juice Jacking
298(1)
14.2.3 Technicality Behind Juice Jacking
299(1)
14.3 Hacking Over Public WiFi or the MITM Attacks
299(7)
14.3.1 Phishing
300(2)
14.3.2 Vishing/Smishing
302(1)
14.3.3 Session Hijacking
303(1)
14.3.4 Weak Session Token Generation/Predictable Session Token Generation
304(1)
14.3.5 IP Spoofing
304(1)
14.3.6 Cross-Site Scripting (XSS) Attack
305(1)
14.4 SQLi Injection
306(1)
14.5 Denial of Service Attack
307(2)
14.6 Dark Web and Deep Web Technologies
309(2)
14.6.1 The Deep Web
309(1)
14.6.2 The Dark Web
310(1)
14.7 Conclusion
311(1)
References 312(1)
Index 313
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.