| Acknowledgments |
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ix | |
| Introduction |
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xi | |
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1 Computer Security with Artificial Intelligence, Machine Learning, and Data Science Combination: What? How? Why? And Why Now and Together? |
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1 | (56) |
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1.1 The Current Security Landscape |
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1 | (6) |
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1.2 Computer Security Basic Concepts |
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7 | (2) |
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1.3 Sources of Security Threats |
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9 | (4) |
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1.4 Attacks Against IoT and Wireless Sensor Networks |
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13 | (5) |
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1.5 Introduction into Artificial Intelligence, Machine Learning, and Data Science |
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18 | (13) |
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1.6 Fuzzy Logic and Systems |
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31 | (4) |
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35 | (8) |
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1.8 Artificial Neural Networks (ANN) |
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43 | (7) |
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1.9 Genetic Algorithms (GA) |
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50 | (1) |
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1.10 Hybrid Intelligent Systems |
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51 | (6) |
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52 | (1) |
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53 | (1) |
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54 | (3) |
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2 Firewall Design and Implementation: How to Configure Knowledge for the First Line of Defense? |
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57 | (52) |
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2.1 Firewall Definition, History, and Functions: What Is It? And Where Does It Come From? |
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57 | (8) |
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2.2 Firewall Operational Models or How Do They Work? |
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65 | (5) |
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2.3 Basic Firewall Architectures or How Are They Built Up? |
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70 | (5) |
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2.4 Process of Firewall Design, Implementation, and Maintenance or What Is the Right Way to Put All Things Together? |
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75 | (7) |
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2.5 Firewall Policy Formalization with Rules or How Is the Knowledge Presented? |
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82 | (14) |
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2.6 Firewalls Evaluation and Current Developments or How Are They Getting More and More Intelligent? |
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96 | (13) |
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104 | (2) |
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106 | (1) |
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107 | (2) |
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3 Intrusion Detection Systems: What Do They Do Beyond the First Line of Defense? |
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109 | (68) |
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3.1 Definition, Goals, and Primary Functions |
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109 | (4) |
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3.2 IDS from a Historical Perspective |
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113 | (3) |
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3.3 Typical IDS Architecture Topologies, Components, and Operational Ranges |
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116 | (5) |
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3.4 IDS Types: Classification Approaches |
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121 | (10) |
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3.5 IDS Performance Evaluation |
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131 | (5) |
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3.6 Artificial Intelligence and Machine Learning Techniques in IDS Design |
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136 | (23) |
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3.7 Intrusion Detection Challenges and Their Mitigation in IDS Design and Deployment |
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159 | (4) |
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3.8 Intrusion Detection Tools |
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163 | (14) |
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172 | (2) |
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174 | (1) |
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175 | (2) |
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4 Malware and Vulnerabilities Detection and Protection: What Are We Looking for and How? |
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177 | (70) |
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4.1 Malware Definition, History, and Trends in Development |
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177 | (5) |
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4.2 Malware Classification |
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182 | (32) |
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214 | (2) |
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4.4 Software Vulnerabilities |
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216 | (3) |
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4.5 Principles of Malware Detection and Anti-malware Protection |
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219 | (10) |
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4.6 Malware Detection Algorithms |
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229 | (8) |
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237 | (10) |
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240 | (2) |
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242 | (1) |
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243 | (4) |
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5 Hackers versus Normal Users: Who Is Our Enemy and How to Differentiate Them from Us? |
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247 | (68) |
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5.1 Hacker's Activities and Protection Against |
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247 | (26) |
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5.2 Data Science Investigation of Ordinary Users' Practice |
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273 | (15) |
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5.3 User's Authentication |
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288 | (13) |
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5.4 User's Anonymity, Attacks Against It, and Protection |
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301 | (14) |
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309 | (1) |
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310 | (1) |
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311 | (4) |
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6 Adversarial Machine Learning: Who Is Machine Learning Working For? |
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315 | (22) |
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6.1 Adversarial Machine Learning Definition |
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315 | (1) |
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6.2 Adversarial Attack Taxonomy |
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316 | (4) |
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320 | (2) |
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6.4 Investigation of the Adversarial Attacks Influence on the Classifier Performance Use Case |
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322 | (5) |
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6.5 Generative Adversarial Networks |
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327 | (10) |
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333 | (1) |
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334 | (1) |
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335 | (2) |
| Index |
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337 | |