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020			_a1394196466 _qelectronic book
020			_a9781394196456 _qelectronic book
020			_a1394196458 _qelectronic book
020			_a9781394196470 _qelectronic book
020			_a1394196474 _qelectronic book
020			_a9781394196463 _q(electronic bk.)
020			_z9781394196449 _qhardcover
020			_z139419644X _qhardcover
024	7		_a10.1002/9781394196470 _2doi
035			_a(OCoLC)1427498230
037			_a9781394196449 _bO'Reilly Media
037			_a10494576 _bIEEE
040			_aDLC _beng _erda _cDLC _dYDX _dORMDA _dIEEEE _dOCLCO _dDG1 _dOCLCQ _dN$T
041			_aeng
042			_apcc
050	0	4	_aQA76.9.A25 _bA674 2024
082	0	0	_a006.3 _223/eng/20240324
245	0	0	_aApplying artificial intelligence in cyber security analytics and cyber threat detection / _cedited by Shilpa Mahajan, Mehak Khurana, Vania Vieira Estrela.
264		1	_aHoboken, New Jersey : _bJohn Wiley & Sons, Inc., _c[2024]
300			_a1 online resource (xxxiv, 333 pages) : _billustrations (some color)
336			_atext _btxt _2rdacontent.
337			_acomputer _bc _2rdamedia.
338			_aonline resource _bcr _2rdacarrier.
340			_2rdacc _0http://rdaregistry.info/termList/RDAColourContent/1003.
504			_aIncludes bibliographical references and index.
505	0		_aTable of Contents About the Editors xvii List of Contributors xxi Preface xxv Acknowledgment xxvii Disclaimer xxix Note for Readers xxxi Introduction xxxiii Part I Artificial Intelligence (AI) in Cybersecurity Analytics: Fundamental and Challenges 1 1 Analysis of Malicious Executables and Detection Techniques 3 Geetika Munjal and Tushar Puri 1.1 Introduction 3 1.2 Malicious Code Classification System 5 1.3 Literature Review 5 1.4 Malware Behavior Analysis 8 1.5 Conventional Detection Systems 11 1.6 Classifying Executables by Payload Function 12 1.7 Result and Discussion 13 1.8 Conclusion 15 2 Detection and Analysis of Botnet Attacks Using Machine Learning Techniques 19 Supriya Raheja 2.1 Introduction 19 2.2 Literature Review 20 2.3 Botnet Architecture 21 2.4 Methodology Adopted 24 2.5 Experimental Setup 27 2.6 Results and Discussions 28 2.7 Conclusion and Future Work 30 3 Artificial Intelligence Perspective on Digital Forensics 33 Bhawna and Shilpa Mahajan 3.1 Introduction 33 3.2 Literature Survey 34 3.3 Phases of Digital Forensics 35 3.4 Demystifying Artificial Intelligence in the DigitalWorld 36 3.5 Application of Machine Learning in Digital Forensics Investigations 39 3.6 Implementation of Artificial Intelligence in Forensics 40 3.7 Pattern Recognition Using Artificial Intelligence 40 3.8 Applications of AI in Criminal Investigations 42 3.9 Conclusion 43 4 Review on Machine Learning-based Traffic Rules Contravention Detection System 45 Jahnavi and Urvashi 4.1 Introduction 45 4.2 Technologies Involved in Smart Traffic Monitoring 47 4.3 Literature Review 50 4.4 Comparison of Results 59 4.5 Conclusion and Future Scope 59 5 Enhancing Cybersecurity Ratings Using Artificial Intelligence and DevOps Technologies 63 Vishwas Pitre, Ashish Joshi, Satya Saladi, and Suman Das 5.1 Introduction 63 5.2 Literature Review 66 5.3 Proposed Methodology 67 5.4 Results 75 5.5 Conclusion and Future Scope ofWork 84 Part II Cyber Threat Detection and Analysis Using Artificial Intelligence and Big Data 87 6 Malware Analysis Techniques in Android-Based Smartphone Applications 89 Geetika Munjal, Avi Chakravarti, and Utkarsh Sharma 6.1 Introduction 89 6.2 Malware Analysis Techniques 93 6.3 Hybrid Analysis 102 6.4 Result 102 6.5 Conclusion 103 7 Cyber Threat Detection and Mitigation Using Artificial Intelligence -- A Cyber-physical Perspective 107 Dalmo Stutz, Joaquim T. de Assis, Asif A. Laghari, Abdullah A. Khan, Anand Deshpande, Dhanashree Kulkarni, Andrey Terziev, Maria A. de Jesus, and Edwiges G.H. Grata 7.1 Introduction 107 7.2 Types of Cyber Threats 109 7.3 Cyber Threat Intelligence (CTI) 116 7.4 Materials and Methods 119 7.5 Cyber-Physical Systems Relying on AI (CPS-AI) 121 7.6 Experimental Analysis 126 7.7 Conclusion 129 8 Performance Analysis of Intrusion Detection System Using ML Techniques 135 Paridhi Pasrija, Utkarsh Singh, and Mehak Khurana 8.1 Introduction 135 8.2 Literature Survey 136 8.3 ML Techniques 137 8.4 Overview of Dataset 140 8.5 Proposed Approach 142 8.6 Simulation Results 143 8.7 Conclusion and Future Work 148 9 Spectral Pattern Learning Approach-based Student Sentiment Analysis Using Dense-net Multi Perception Neural Network in E-learning Environment 151 Laishram Kirtibas Singh and R. Renuga Devi 9.1 Introduction 151 9.2 RelatedWork 152 9.3 Proposed Implementation 153 9.4 Result and Discussion 159 9.5 Conclusion 163 10 Big Data and Deep Learning-based Tourism Industry Sentiment Analysis Using Deep Spectral Recurrent Neural Network 165 Chingakham Nirma Devi and R. Renuga Devi 10.1 Introduction 165 10.2 RelatedWork 166 10.3 Materials and Method 168 10.4 Result and Discussion 173 10.5 Conclusion 176 Part III Applied Artificial Intelligence Approaches in Emerging Cybersecurity Domains 179 11 Enhancing Security in Cloud Computing Using Artificial Intelligence (AI) 181 Dalmo Stutz, Joaquim T. de Assis, Asif A. Laghari, Abdullah A. Khan, Nikolaos Andreopoulos, Andrey Terziev, Anand Deshpande, Dhanashree Kulkarni, and Edwiges G.H. Grata 11.1 Introduction 181 11.2 Background 184 11.3 Identification Function (IF) 185 11.4 Protection Function (PF) 191 11.5 Detection Function (DF) 196 11.6 Response Function (RF) 200 11.7 Recovery Function (RcF) 205 11.8 Analysis, Discussion and Research Gaps 205 11.9 Conclusion 209 12 Utilization of Deep Learning Models for Safe Human-Friendly Computing in Cloud, Fog, and Mobile Edge Networks 221 Diego M.R. Tudesco, Anand Deshpande, Asif A. Laghari, Abdullah A. Khan, Ricardo T. Lopes, R. Jenice Aroma, Kumudha Raimond, Lin Teng, and Asiya Khan 12.1 Introduction 221 12.2 Human-Centered Computing (HCC) 223 12.3 Improving Cybersecurity Through Deep Learning (DL) Models: AI-HCC Systems 229 12.5 Discussion 238 12.6 Conclusion 239 13 Artificial Intelligence for Threat Anomaly Detection Using Graph Databases -- A Semantic Outlook 249 Edwiges G.H. Grata, Anand Deshpande, Ricardo T. Lopes, Asif A. Laghari, Abdullah A. Khan, R. Jenice Aroma, Kumudha Raimond, Shoulin Yin, and Awais Khan Jumani 13.1 Introduction 249 13.2 KGs in Cybersecurity 252 13.3 CSKG Construction Methodologies 254 13.3.1 CSKG Building Flow 255 13.3.2 CS Ontology 255 13.3.3 CS Entities Extraction 256 13.3.4 Relations Extraction of CS Entities 257 13.4 Datasets 258 13.5 Application Scenarios 259 13.5.1 CSA and Security Assessment 259 13.5.2 CTs’ Discovery 260 13.5.3 Attack Probing 261 13.5.4 Clever Security Operation 264 13.5.5 Smart Decision-Making 265 13.5.6 Vulnerability Prediction and Supervision 266 13.5.7 Malware Acknowledgment and Analysis 267 13.5.8 Physical System Connection 267 13.5.9 Supplementary Reasoning Tasks 268 13.6 Discussion and Future Trends on CSKG 269 13.7 Conclusion 271 14 Security in Blockchain-Based Smart Cyber-Physical Applications Relying on Wireless Sensor and Actuators Networks 279 Maria A. de Jesus, Asif A. Laghari, Abdullah A. Khan, Awais Khan Jumani, Mohammad Shabaz, Anand Deshpande, R. Jenice Aroma, Kumudha Raimond, and Asiya Khan 14.1 Introduction 279 14.2 Methodology 282 14.3 GIBCS: An Overview 292 14.4 Blockchain Layer 294 14.5 Trust Management 296 14.6 Blockchain for Secure Monitoring Back-End 298 14.7 Blockchain-Enabled Cybersecurity: Discussion and Future Directions 300 14.8 Conclusions 301 15 Leveraging Deep Learning Techniques for Securing the Internet of Things in the Age of Big Data 311 Keshav Kaushik 15.1 Introduction to the IoT Security 311 15.2 Role of Deep Learning in IoT Security 316 15.3 Deep Learning Architecture for IoT Security 319 15.4 Future Scope of Deep Learning in IoT Security 322 15.5 Conclusion 323 References 323 Index 327
520			_a"Today, it's impossible to deploy effective cybersecurity technology without relying heavily on machine learning. With machine learning, cybersecurity systems can be analyzed using patterns and learn from them to help prevent similar attacks and respond to changing behavior. It can help cybersecurity teams to be more proactive in preventing threats and responding to active attacks in real time. In short, machine learning can make cybersecurity simpler, more proactive, less expensive, and far more effective. AI cybersecurity, with the support of machine learning, is set to be a powerful tool in the looming future. As with other industries, human interaction has long been essential and irreplaceable in security. While cybersecurity currently relies heavily on human input, we are gradually seeing technology become better at specific tasks than we are"-- _cProvided by publisher.
545	0		_aAbout the Author Shilpa Mahajan, PhD, is an Associate Professor in the School of Engineering and Technology at The NorthCap University, India. Mehak Khurana, PhD, is an Associate Professor in the School of Engineering and Technology at The NorthCap University, India. Vania Vieira Estrela, PhD, is a Professor with the Telecommunications Department of the Fluminense Federal University, Brazil.
650		0	_aComputer security _0https://id.loc.gov/authorities/subjects/sh90001862 _xTechnological innovations. _0https://id.loc.gov/authorities/subjects/sh2001009095.
650		0	_aArtificial intelligence. _0https://id.loc.gov/authorities/subjects/sh85008180.
650		0	_aComputer networks _xSecurity measures. _0https://id.loc.gov/authorities/subjects/sh94001277.
650		0	_aComputer crimes _xPrevention. _0https://id.loc.gov/authorities/subjects/sh2008117723.
655		4	_aElectronic books.
700	1		_aMahajan, Shilpa, _0https://id.loc.gov/authorities/names/no2022011728 _eeditor.
700	1		_aKhurana, Mehak, _0https://id.loc.gov/authorities/names/no2022011727 _eeditor.
700	1		_aEstrela, Vania Vieira, _0https://id.loc.gov/authorities/names/no2018113293 _eeditor.
856	4	0	_uhttps://onlinelibrary.wiley.com/doi/book/10.1002/9781394196470 _yFull text is available at Wiley Online Library Click here to view
942			_2ddc _cER