Cybersecurity and secure information systems: challenges and solutions in smart environments

Preface......Page 6
About This Book......Page 8
Contents......Page 9
Security and Privacy in Smart City Applications and Services: Opportunities and Challenges......Page 11
 2 Basics and Background......Page 12
  2.2 Factors Influencing Information Security......Page 16
 3 Security Challenges and Open Problems in Smart Cities......Page 17
 4 Privacy Concerns in Smart Cities......Page 21
 References......Page 24
 1 Introduction......Page 26
  2.2 Issues in Communication Layer......Page 27
 3 Proposed Framework for Securing IoT Environment......Page 28
 4 Proposed Multilevel Encryption for Securing the Cloud Layer......Page 29
 5 Result and Discussion......Page 34
 6 Conclusion......Page 35
 References......Page 36
An Efficient Image Encryption Scheme Based on Signcryption Technique with Adaptive Elephant Herding Optimization......Page 39
 2 Literature Review......Page 40
  2.1 Purpose of Image Security......Page 41
  3.1 Image Security Using Signcryption Algorithm......Page 42
  3.2 Optimal Key Selection Using AEHO......Page 43
 4 Result and Analysis......Page 46
 5 Conclusions and Future Scope......Page 47
 References......Page 49
Time Split Based Pre-processing  with a Data-Driven Approach for Malicious URL Detection......Page 51
 1 Introduction......Page 52
 2 Related Works......Page 54
 5 Methodology......Page 55
  5.2 Semantics of Vector Representation......Page 56
  5.4 Distributed Representation......Page 57
  5.5 Machine Learning Algorithms and Deep Learning Architectures......Page 58
 6 Experiments......Page 62
  6.1 Experiments on Random Split and Time Split Data with Machine Learning and Deep Learning Techniques......Page 63
  6.2 Overall Result......Page 67
  7.1 Representation......Page 68
  7.2 Hidden Layers......Page 69
 8 System Architecture......Page 70
 9 Conclusion......Page 71
 References......Page 72
Optimal Wavelet Coefficients Based Steganography for Image Security with Secret Sharing Cryptography Model......Page 74
 1 Introduction......Page 75
 2 Literature Review......Page 76
  3.2 Domain Transform......Page 78
  3.3 Visual Cryptography......Page 82
  3.4 Cryptographic Technique for SS Security......Page 84
 4 Result and Analysis......Page 86
 5 Conclusion......Page 90
 References......Page 91
Deep Learning Framework for Cyber Threat Situational Awareness Based  on Email and URL Data Analysis......Page 93
 1 Introduction......Page 94
  2.1 Electronic-Mail (Email)......Page 96
  2.2 Uniform Resource Locator (URL)......Page 97
  3.1 Related Work on Spam and Phishing Email Detection......Page 98
  3.2 Related Works on Malicious and Phishing URL Detection......Page 100
  3.3 Related Works on Image Spam Detection......Page 102
  4.1 Text Representation......Page 103
  4.2 Methods......Page 105
 5 Description of Dataset......Page 111
 6 Experiments on Spam and Phishing Detection......Page 113
  6.1 Experiments on Spam and Phishing Email Detection......Page 114
  6.2 Experiments on Spam and Phishing URL Detection......Page 118
  6.3 Experiments on Email Categorization......Page 122
  6.4 Experiments on Image Spam Detection......Page 124
 7 DeepSpamPhishNet (DSPN)......Page 126
 References......Page 127
 1 Introduction......Page 131
  2.1 Classical Machine Learning (CML)......Page 133
  2.2 Modern Machine Learning......Page 135
  3.1 DeepID: Detailed Analysis of Classical Machine Learning Algorithms and Deep Learning Architectures for Intrusion Detection......Page 141
  3.2 DeepTrafficNet: Deep Learning Framework for Network Traffic Analysis......Page 153
  3.3 Deep-Droid-Net (DDN): Applying Deep Learning Approaches for Android Malware Detection......Page 158
 4 Conclusion and Future Works......Page 163
 References......Page 164
Improved DGA Domain Names Detection and Categorization Using Deep Learning Architectures with Classical Machine Learning Algorithms......Page 167
 1 Introduction......Page 168
  2.1 Domain Name System (DNS)......Page 171
  2.2 Domain Fluxing (DF)......Page 172
  2.4 Deep Learning Architectures (DLAs)......Page 173
 3 Related Works Based on Deep Learning Architectures for DGA Analysis......Page 177
  4.2 Data Set for Domain Name Statistical Analyzer......Page 178
 5 Statistical Measures......Page 180
  6.1 Proposed Architecture......Page 181
  6.2 Experiments, Results and Observations......Page 182
  7.1 Proposed Architecture......Page 183
  7.2 Experiments, Results and Observations......Page 185
 8 Proposed Architecture: I-DGA-DC-Net......Page 188
 9 Conclusion, Discussions and Future Works......Page 195
 References......Page 196
Secure Data Transmission Through Reliable Vehicles in VANET Using Optimal Lightweight Cryptography......Page 199
 2 Literature Survey......Page 200
  3.1 Routing Scenario with Clustering Model......Page 201
  3.2 LWC for Security Analysis......Page 204
  4.1 Implementation Parameter......Page 206
 5 Conclusion......Page 207
 References......Page 208
 1 Introduction......Page 211
 2 Previous Research......Page 213
 3 Analysis of Some Attacks of Data in Information Systems and Smart Cities Applications......Page 214
  3.1 Non-persistent XSS Attack......Page 215
  3.2 Persistent XSS Attack......Page 216
  3.4 Cross-Site Falsification Request (CSRF)......Page 217
  3.5 Demonstration of a Concrete Executive Script in Smart Environment......Page 219
 4 Verifying Data Integrity and Authenticity in Smart Environments......Page 224
  4.1 Authentication with Hash Functions and Ways to “Break” Hash Digest in Python......Page 225
  4.3 Data Hiding Using Kali Linux Environment and Python Programming Language......Page 226
  4.4 Information Leakage......Page 229
 5 Conclusion and Further Works......Page 231
 References......Page 232
Clustering Based Cybersecurity Model for Cloud Data......Page 233
 2 Literature Survey......Page 234
 3 Overview of Encryption in Cloud Security......Page 235
  4.1 Data Collection......Page 236
  4.3 Cloud Service Model......Page 237
  4.4 Blowfish Encryption (BE): Proposed Security Model......Page 238
 5 Result Analysis......Page 241
 6 Conclusion......Page 244
 References......Page 245
A Detailed Investigation and Analysis  of Deep Learning Architectures  and Visualization Techniques for Malware Family Identification......Page 247
 1 Introduction......Page 248
 2 Related Work......Page 250
  4.1 Convolutional Neural Network (CNN)......Page 252
  4.2 Long Short Term Memory (LSTM)......Page 253
  4.3 Activation Function......Page 254
  4.4 Classical Machine Learning Classifiers......Page 255
 5 Proposed Architecture......Page 259
  7.1 Experimental Analysis......Page 261
  7.2 Performance Metrics......Page 263
  7.3 Results......Page 265
 8 Conclusion......Page 289
 References......Page 290
 1 Introduction......Page 293
 2 Overview of the SOC......Page 294
 3 Education Goals......Page 296
 4 Event Classification and Triage......Page 298
  4.1 Honeypots......Page 299
 5 Cloud Security......Page 306
  5.1 Visualization, Prioritization, and Analysis......Page 308
 6 Remediation and Recovery......Page 311
  6.1 Assessment and Audit......Page 312
 7 Conclusions and Future Work......Page 313
 References......Page 314
Appendix: Marist College SOC and Related Facilities, Equipment, and Resources......Page 317