Data Exfiltration Threats and Prevention Techniques

Machine Learning and Memory-Based Data Security

Inbunden, Engelska, 2023

1 459 kr

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Beskrivning

DATA EXFILTRATION THREATS AND PREVENTION TECHNIQUES Comprehensive resource covering threat prevention techniques for data exfiltration and applying machine learning applications to aid in identification and prevention Data Exfiltration Threats and Prevention Techniques provides readers the knowledge needed to prevent and protect from malware attacks by introducing existing and recently developed methods in malware protection using AI, memory forensic, and pattern matching, presenting various data exfiltration attack vectors and advanced memory-based data leakage detection, and discussing ways in which machine learning methods have a positive impact on malware detection. Providing detailed descriptions of the recent advances in data exfiltration detection methods and technologies, the authors also discuss details of data breach countermeasures and attack scenarios to show how the reader may identify a potential cyber attack in the real world. Composed of eight chapters, this book presents a better understanding of the core issues related to the cyber-attacks as well as the recent methods that have been developed in the field. In Data Exfiltration Threats and Prevention Techniques, readers can expect to find detailed information on: Sensitive data classification, covering text pre-processing, supervised text classification, automated text clustering, and other sensitive text detection approachesSupervised machine learning technologies for intrusion detection systems, covering taxonomy and benchmarking of supervised machine learning techniquesBehavior-based malware detection using API-call sequences, covering API-call extraction techniques and detecting data stealing behavior based on API-call sequencesMemory-based sensitive data monitoring for real-time data exfiltration detection and advanced time delay data exfiltration attack and detectionAimed at professionals and students alike, Data Exfiltration Threats and Prevention Techniques highlights a range of machine learning methods that can be used to detect potential data theft and identifies research gaps and the potential to make change in the future as technology continues to grow.

Produktinformation

Utgivningsdatum:2023-05-23
Mått:152 x 229 x 18 mm
Vikt:658 g
Format:Inbunden
Språk:Engelska
Antal sidor:288
Förlag:John Wiley & Sons Inc
ISBN:9781119898870

Utforska kategorier

Nätverk och kommunikation inom Data och IT
IT-säkerhet inom Data och IT

Mer om författaren

Zahir Tari is Professor at RMIT and Research Director of the RMIT Centre of Cyber Security Research and Innovation. Nasrin Sohrabi received a PhD in Computer Science from RMIT University, Australia. She is a Postdoctoral Research Fellow in Cloud, Systems and Security discipline, School of Computing Technologies, RMIT University and a core member of the RMIT Centre of Cyber Security Research and Innovation (CCSRI). She has several publications in highly ranked conferences and journals, including ICDE, IEEE Transactions on Services Computings, ACM Computing surveys, IEEE Transactions on Transportation systems, IEEE Transactions on Smart Grids. Yasaman Samadi is a PhD student in Computer Science at RMIT University, Australia and a researcher in Quantum Cybersecurity. Yasaman has a Master’s in Computer Architecture and worked as a quantum engineer at QBee. Jakapan Suaboot received his PhD in Cybersecurity from RMIT, Australia. He previously worked as a Lecturer for the Department of Computer Engineering from Prince of Songkla University, Phuket, Thailand.

Innehållsförteckning

About the Authors xvAcknowledgments xviiAcronyms xixAbstract xxi1 Introduction 11.1 Data Exfiltration Methods 31.2 Important Questions 71.3 Book Scope 91.4 Book Summary 111.5 Book Structure 152 Background192.1 Hidden Markov Model 192.2 Memory Forensics 242.3 Bag-of-Words Model 272.4 Sparse Distributed Representation 282.5 Summary 293 Data Security Threats 313.1 Data Security 323.2 Security vs. Protection vs. Privacy 353.3 Advanced Persistent Threats Attacks 363.4 Cybersecurity Threats 383.5 Conclusion 594 Use Cases Data Leakage Attacks 634.1 Most Significant Attacks 634.2 Top Infection Vectors 684.3 Top Threats of Recent Years 704.4 Malware Development Trends 714.5 Geographic Trends 754.6 Industry Trends 784.7 Conclusion 805 Survey on Building Block Technologies 835.1 Motivation 835.2 Background 875.3 Taxonomy 965.4 Supervised Learning Methods 985.5 Systematic Literature Review 1075.6 Evaluation of Supervised Learning Methods 1085.7 Key Open Problems 1255.8 Summary 1276 Behavior-Based Data Exfiltration Detection Methods 1416.1 Motivation 1416.2 Existing Methods 1446.3 Sub-Curve HMM Method 1486.4 Evaluation 1596.5 Experimental Results 1646.6 Discussion 1726.7 Summary 1737 Memory-Based Data Exfiltration Detection Methods 1817.1 Motivation 1817.2 Existing Methods 1837.3 Concepts 1867.4 Fast Lookup Bag-of-Words (FBoW) 1917.5 Evaluation 1997.6 Summary 2158 Temporal-Based Data Exfiltration Detection Methods 2218.1 Motivation 2218.2 Existing Methods 2238.3 Definitions 2258.4 Temporary Memory Bag-of-Words (TMBoW) 2298.5 Experimental Results 2348.6 Summary 2459 Conclusion 2499.1 Summary 2499.2 What Is Innovative in the Described Methods? 2519.3 What Is Next? 253Index 255