Intelligent Data Analytics for Terror Threat Prediction

1 Cover

2 Title Page

3 Copyright

4 Preface

5 1 Rumor Detection and Tracing its Source to Prevent Cyber-Crimes on Social Media 1.1 Introduction 1.2 Social Networks 1.3 What Is Cyber-Crime? 1.4 Rumor Detection 1.5 Factors to Detect Rumor Source 1.6 Source Detection in Network 1.7 Conclusion References

6 2 Internet of Things (IoT) and Machine to Machine (M2M) Communication Techniques for Cyber Crime Prediction 2.1 Introduction 2.2 Advancement of Internet 2.3 Internet of Things (IoT) and Machine to Machine (M2M) Communication 2.4 A Definition of Security Frameworks 2.5 M2M Devices and Smartphone Technology 2.6 Explicit Hazards to M2M Devices Declared by Smartphone Challenges 2.7 Security and Privacy Issues in IoT 2.8 Protection in Machine to Machine Communication 2.9 Use Cases for M2M Portability 2.10 Conclusion References

7 3 Crime Predictive Model Using Big Data Analytics 3.1 Introduction 3.2 Crime Data Mining 3.3 Visual Data Analysis 3.4 Technological Analysis 3.5 Big Data Framework 3.6 Architecture for Crime Technical Model 3.7 Challenges 3.8 Conclusions References

8 4 The Role of Remote Sensing and GIS in Military Strategy to Prevent Terror Attacks 4.1 Introduction 4.2 Database and Methods 4.3 Discussion and Analysis 4.4 Role of Remote Sensing and GIS 4.5 Cartographic Model 4.6 Mapping Techniques Used for Defense Purposes 4.7 Naval Operations 4.8 Future Sphere of GIS in Military Science 4.9 Terrain Evolution 4.10 Conclusion References

9 5 Text Mining for Secure Cyber Space 5.1 Introduction 5.2 Literature Review 5.3 Latent Semantic Analysis 5.4 Proposed Work 5.5 Detailed Work Flow of Proposed Approach 5.6 Results and Discussion 5.7 Conclusion References

10 6 Analyses on Artificial Intelligence Framework to Detect Crime Pattern 6.1 Introduction 6.2 Related Works 6.3 Proposed Clustering for Detecting Crimes 6.4 Performance Evaluation 6.5 Conclusions References

11 7 A Biometric Technology-Based Framework for Tackling and Preventing Crimes 7.1 Introduction 7.2 Biometrics 7.3 Surveillance Systems (CCTV) 7.4 Legality to Surveillance and Biometrics vs. Privacy and Human Rights 7.5 Proposed Work (Biometric-Based CCTV System) 7.6 Conclusion References

12 8 Rule-Based Approach for Botnet Behavior Analysis 8.1 Introduction 8.2 State-of-the-Art 8.3 Bots and Botnets 8.4 Methodology 8.5 Results and Analysis 8.6 Conclusion and Future Scope References

13 9 Securing Biometric Framework with Cryptanalysis 9.1 Introduction 9.2 Basics of Biometric Systems 9.3 Biometric Variance 9.4 Performance of Biometric System 9.5 Justification of Biometric System 9.6 Assaults on a Biometric System 9.7 Biometric Cryptanalysis: The Fuzzy Vault Scheme 9.8 Conclusion & Future Work References

14 10 The Role of Big Data Analysis in Increasing the Crime Prediction and Prevention Rates 10.1 Introduction: An Overview of Big Data and Cyber Crime 10.2 Techniques for the Analysis of BigData 10.3 Important Big Data Security Techniques 10.4 Conclusion References

15 11 Crime Pattern Detection Using Data Mining 11.1 Introduction 11.2 Related Work 11.3 Methods and Procedures 11.4 System Analysis 11.5 Analysis Model and Architectural Design 11.6 Several Criminal Analysis Methods in Use 11.7 Conclusion and Future Work References

16 12 Attacks and Security Measures in Wireless Sensor Network 12.1 Introduction 12.2 Layered Architecture of WSN 12.3 Security Threats on Different Layers in WSN 12.4 Threats Detection at Various Layers in WSN 12.5 Various Parameters for Security Data Collection in WSN 12.6 Different Security Schemes in WSN 12.7 Conclusion References

17 13 Large Sensing Data Flows Using Cryptic Techniques 13.1 Introduction 13.2 Data Flow Management 13.3 Design of Big Data Stream 13.4 Utilization of Security Methods 13.5 Analysis of Security on Attack 13.6 Artificial Intelligence Techniques for Cyber Crimes 13.7 Conclusions References

18 14 Cyber-Crime Prevention Methodology 14.1 Introduction 14.2 Credit Card Frauds and Skimming 14.3 Hacking Over Public WiFi or the MITM Attacks 14.4 SQLi Injection 14.5 Denial of Service Attack 14.6 Dark Web and Deep Web Technologies 14.7 Conclusion References

19 Index

20 End User License Agreement

1 Chapter 1 Table 1.1 Social network users [24]. Table 1.2 Dataset features [31].

2 Chapter 5Table 5.1 Similarity score of keyword ‘Authentication’ in various Document ID.Table 5.2 Similarity score of keyword ‘SQL injection’ in various documents.Table 5.3 Accuracy for searching cyber-attack related keywords using hybrid appr...

3 Chapter 6Table 6.1 Topics in the dataset.Table 6.2 Events present in the topics.Table 6.3 Precision.Table 6.4 Sensitivity.Table 6.5 Specificity.Table 6.6 Accuracy.

4 Chapter 8Table 8.1 Features extracted from Wireshark.Table 8.2 Rules generated.Table 8.3 Error rate.

5 Chapter 9Table 9.1 The representation schemes along with matching algorithms for Biometri...Table 9.2 Comparisons of Biometric Identifiers on the basis of various factors [...Table 9.3 Examples of apps using biometric recognizance [39, 40].Table 9.4 Advantages & disadvantages of biometric system on the basis of various...

6 Chapter 10Table 10.1 Four forms of knowledge discovery in crime cases.Table 10.2 Comparison of methodology.

7 Chapter 12Table 12.1 Benefits & Snag of security schemes in WSN.

8 Chapter 14Table 14.1 Functionality of USB charging cable.

1 Chapter 1 Figure 1.1 Social networks [23]. Figure 1.2 Classification of rumor and non-rumor. Figure 1.3 Rumor classification process. Figure 1.4 Naïve Bayes classifier. Figure 1.5 Hyperplane in 2-D and 3-D. Figure 1.6 Combating misinformation in Instagram [33]. Figure 1.7 Network topology. Figure 1.8 SI model. Figure 1.9 SIS model. Figure 1.10 SIR model. Figure 1.11 SIRS model. Figure 1.12 Centrality measures. Figure 1.13 Rumor source detection process.

2 Chapter 2 Figure 2.1 Advancement of Internet through ARPANET to IoT and M2M. Figure 2.2 Machine knowledge points of view for IoT through M2M with Cyber Secur... Figure 2.3 IoT Theoretical Top 10 Risks. Figure 2.4 Top 5 Functional Risks and Vulnerabilities. Figure 2.5 GSM-based modules with wireless connectivity.

3 Chapter 4Figure 4.1 Frame work of military GIS.Figure 4.2 Various applications of GIS in defense strategy.Figure 4.3 Cartographic model for land management in hilly area.Figure 4.4 Digital Elevation Model.Figure 4.5 Triangulated Irregular Network (TIN) Model.Figure 4.6 Hillshade analysis model for terrain analysis.

4 Chapter 5Figure 5.1 Broad steps followed in text mining.Figure 5.2 Process of text mining.Figure 5.3 Work flow of text mining.Figure 5.4 Detailed workflow of proposed approach.Figure 5.5 Process followed to obtain similarity score.Figure 5.6 Similarity and accuracy for the keyword ‘Authentication’.Figure 5.7 Ranking graph of document and similarity for keyword ‘SQL Injection’.Figure 5.8 Accuracy for searching vulnerable keywords.

5 Chapter 6Figure 6.1 Overall architecture of the proposed method.

6 Chapter 7Figure 7.1 General flow of biometric systems.Figure 7.2 Biometric traits.Figure 7.3 Biometric framework.Figure 7.4 Biometric applications.Figure 7.5 Soft biometric classification.Figure 7.6 Soft Biometric System Interface.Figure 7.7 Surveillance system.Figure 7.8 Accuracy recognition.Figure 7.9 Proposed Work Flow Diagram.Figure 7.10 Proposed Frame Work.Figure 7.11 Intelligent Identification System.

7 Chapter 8Figure 8.1 Botnet life cycle.Figure 8.2 Different botnet detection methods.Figure 8.3 Block diagram of proposed methodology.Figure 8.4 Decision tree obtained using proposed approach.Figure 8.5 Percentage accuracy of various machine learning model and proposed mo...