Advances in Malware and Data-Driven Network Security
- Length: 332 pages
- Edition: 1
- Language: English
- Publisher: Information Science Reference
- Publication Date: 2021-11-08
- ISBN-10: 1799877892
- ISBN-13: 9781799877899
- Sales Rank: #0 (See Top 100 Books)
Every day approximately three-hundred thousand to four-hundred thousand new malware are registered, many of them being adware and variants of previously known malware. Anti-virus companies and researchers cannot deal with such a deluge of malware – to analyze and build patches. The only way to scale the efforts is to build algorithms to enable machines to analyze malware and classify and cluster them to such a level of granularity that it will enable humans (or machines) to gain critical insights about them and build solutions that are specific enough to detect and thwart existing malware and generic-enough to thwart future variants. Advances in Malware and Data-Driven Network Security comprehensively covers data-driven malware security with an emphasis on using statistical, machine learning, and AI as well as the current trends in ML/statistical approaches to detecting, clustering, and classification of cyber-threats. Providing information on advances in malware and data-driven network security as well as future research directions, it is ideal for graduate students, academicians, faculty members, scientists, software developers, security analysts, computer engineers, programmers, IT specialists, and researchers who are seeking to learn and carry out research in the area of malware and data-driven network security.
Cover Title Page Copyright Page Book Series Mission Coverage Dedication Editorial Advisory Board Preface ORGANIZATION OF BOOK Acknowledgment Chapter 1: Machine Learning for Malware Analysis ABSTRACT INTRODUCTION MALWARE DETECTION APPROACH MACHINE LEARNING FOR MALWARE ANALYSIS CHALLENGES IN MACHINE LEARNING-BASED MALWARE ANALYSIS FUTURE DIRECTION CONCLUSION REFERENCES Chapter 2: Research Trends for Malware and Intrusion Detection on Network Systems ABSTRACT INTRODUCTION LITERATURE REVIEW APPROACH FOR ANALYSIS OF RESEARCH TRENDS FOR MALWARE AND INTRUSION DETECTION LATENT DIRICHLET ALLOCATION DATA GATHERING PRE-PROCESSING TOPIC MODELLING RESULT ANALYSIS DISCUSSION CONCLUSION REFERENCES Chapter 3: Deep-Learning and Machine-Learning-Based Techniques for Malware Detection and Data-Driven Network Security ABSTRACT INTRODUCTION REVIEW OF MALWARE TYPICAL MACHINE LEARNING ALGORITHMS DEEP LEARNING METHODS MACHINE LEARNING ENABLED MALWARE DETECTORS CONCLUSION REFERENCES Chapter 4: The Era of Advanced Machine Learning and Deep Learning Algorithms for Malware Detection ABSTRACT INTRODUCTION TYPICAL DEEP LEARNING ALGORITHMS SYSTEMATIC REVIEW IN THE DEEP LEARNING ALGORITHMS FOR MALWARE DETECTION CONCLUSION REFERENCES Chapter 5: Malware Detection in Industrial Scenarios Using Machine Learning and Deep Learning Techniques ABSTRACT INTRODUCTION INCIDENTS IN INDUSTRIAL CONTROL SYSTEMS INDUSTRIAL-ORIENTED DATASETS FOR ANOMALY DETECTION MACHINE LEARNING AND DEEP LEARNING SOLUTIONS TO DETECT CYBERATTACKS IN INDUSTRIAL SCENARIOS AN EXAMPLE OF A CYBERATTACK CAUSING A SAFETY THREAT CYBERSECURITY AND SAFETY MANAGEMENT CONCLUSION AND FUTURE WORK ACKNOWLEDGMENT REFERENCES KEY TERMS AND DEFINITIONS Chapter 6: Malicious Node Detection Using Convolution Technique ABSTRACT INTRODUCTION LITERATURE SURVEY PROPOSED WORK DATA FLOW DIAGRAMS IMPLEMENTATION RESULT CONCLUSION AND FUTURE SCOPE REFERENCES Chapter 7: Scalable Rekeying Using Linked LKH Algorithm for Secure Multicast Communication ABSTRACT INTRODUCTION BACKGROUND REVIEW OF LITERATURE PROPOSED WORK RESULT ANALYSIS CONCLUSION REFERENCES KEY TERMS AND DEFINITIONS Chapter 8: Botnet Defense System and White-Hat Worm Launch Strategy in IoT Network ABSTRACT INTRODUCTION BOTNET DEFENSE SYSTEM (BDS) STRATEGY SIMULATION EVALUATION SIMULATION EVALUATION CONCLUSION ACKNOWLEDGMENT REFERENCES Chapter 9: A Survey on Emerging Security Issues, Challenges, and Solutions for Internet of Things (IoTs) ABSTRACT INTRODUCTION TIMELINE OF IoT IoT ELEMENTS IoT ARCHITECTURE TAXONOMY OF ISSUES RELATED TO EACH LAYER ATTACKS ON DIFFERENT LAYERS TAXONOMY OF VARIOUS COUNTER MEASURES TO VARIOUS ATTACKS OPEN CHALLENGES AND FUTURE RESEARCH DIRECTIONS CONCLUSION REFERENCES Chapter 10: SecBrain ABSTRACT INTRODUCTION SECBRAIN FRAMEWORK: DESIGN, DEPLOYMENT AND EXPERIMENTS FUTURE RESEARCH DIRECTIONS CONCLUSION ACKNOWLEDGMENT REFERENCES KEY TERMS AND DEFINITIONS Chapter 11: A Study on Data Sharing Using Blockchain System and Its Challenges and Applications ABSTRACT INTRODUCTION OUR CONTRIBUTION MOTIVATION OVERVIEW OF BLOCKCHAIN TECHNOLOGY CLASSIFICATION OF BLOCKCHAIN PROPERTIES OF BLOCKCHAIN APPLICATION OF BLOCKCHAIN IN DATA SHARING THE ARCHITECTURE OF BLOCKCHAIN IN DATA SHARING LITERATURE SURVEY SURVEY BASED ON INCENTIVE MECHANISM SURVEY BASED ON ACCESS CONTROL AND ENCRYPTION METHOD SURVEY BASED ON HEALTHCARE DATA SHARING SCHEME SURVEY BASED ON IOT DATA SHARING SUMMARY OF EXISTING WORK ON DATA SHARING WITH BLOCKCHAIN SECURITY CHALLENGES OF BLOCKCHAIN SYSTEM CONCLUSION REFERENCES Chapter 12: Fruit Fly Optimization-Based Adversarial Modeling for Securing Wireless Sensor Networks (WSN) ABSTRACT INTRODUCTION MOTIVATION LITERATURE SURVEY DATA FLOW DIAGRAM IMPLEMENTATION DETAILS RESULTS AND OBSERVATIONS CONCLUSION AND FUTURE PLAN REFERENCES KEY TERMS AND DEFINITIONS Chapter 13: Cybersecurity Risks Associated With Brain-Computer Interface Classifications ABSTRACT INTRODUCTION BACKGROUND RISK ASSESSMENT OF BCI CLASSIFICATIONS FUTURE RESEARCH DIRECTIONS CONCLUSION ACKNOWLEDGMENT REFERENCES Compilation of References About the Contributors
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