Machine Learning for Cyber Security
- Length: 158 pages
- Edition: 1
- Language: English
- Publisher: de Gruyter
- Publication Date: 2022-12-05
- ISBN-10: 3110766736
- ISBN-13: 9783110766738
- Sales Rank: #0 (See Top 100 Books)
Without mathematics no science would survive. This especially applies to the engineering sciences which highly depend on the applications of mathematics and mathematical tools such as optimization techniques, finite element methods, differential equations, fluid dynamics, mathematical modelling, and simulation. Neither optimization in engineering, nor the performance of safety-critical system and system security; nor high assurance software architecture and design would be possible without the development of mathematical applications.
De Gruyter Series on the Applications of Mathematics in Engineering and Information Sciences (AMEIS) focusses on the latest applications of engineering and information technology that are possible only with the use of mathematical methods. By identifying the gaps in knowledge of engineering applications the AMEIS series fosters the international interchange between the sciences and keeps the reader informed about the latest developments.
Preface List of contributors Editor’s biography Preeti Malik, Varsha Mittal, Mohit Mittal, Kamika Chaudhary Differential privacy: a solution to privacy issue in social networks 1 Social media and its popularity 1.1 Pandemic marketing update 1.2 Pros and cons of using social media 2 Social network analysis 3 Privacy breaches in social networks 3.1 Identity disclosure 3.2 Attribute disclosure 3.3 Social link disclosure 3.4 Affiliation link closure 4 Privacy preservation methods 4.1 Anonymization 4.2 k–Anonymity 4.3 Homogeneity attack 4.4 Background knowledge attack 4.5 l-Diversity 4.6 t-Closeness 5 Differential privacy 5.1 Types of differential privacy 6 Privacy attacks in social network 6.1 Private attribute inference 6.2 User de-anonymization attack 7 Application of differential privacy in social network analysis 7.1 Degree distribution 7.2 Subgraph counting 7.3 Edge weights 8 Summary Abdul Rahman, Krishnadas Nanath Cracking Captcha using machine learning algorithms: an intersection of Captcha categories and ML algorithms 1 Introduction 2 Literature review 3 Category 1: theory-driven articles 4 Category 2: critical view of captcha 5 Category 3: supportive view of captcha 6 Category 4: other articles 7 Research method 8 Results and conclusion List of acronyms Kiran Aswal, Dinesh C. Dobhal, Umesh K. Tiwari, Heman Pathak The ransomware: an emerging security challenge to the cyberspace 1 Introduction 1.1 First-generation malware 1.2 Second-generation malware 2 Ransomware: an emerging threat to cyberspace 3 Evolution of the ransomware 4 Life cycle of the ransomware attack 4.1 Creation 4.2 Distribution 4.3 Execution 4.4 Destruction 4.5 Extortion 5 Handling a ransomware attack 5.1 Preventive measures to deal with ransomware attack 5.2 Detection and recovery of ransomware attack 6 Case study of WannaCry ransomware 6.1 Static and dynamic analysis of WannaCry 6.2 Life cycle of WannaCry 6.3 Indicator of compromise (IoC) 7 Conclusion Samuel Wedaj Kibret Property-based attestation in device swarms: a machine learning approach 1 Introduction 1.1 Remote attestation of embedded devices 1.2 Outline 2 Problem definition, attack model, and assumptions 3 The proposed approach: ML for property-based attestation 3.1 Property fingerprinting 3.2 ML for property-based attestation 3.3 Decentralized swarm attestation 4 Implementation and performance evaluation 4.1 Proof of concept: ARM-based implementation 4.2 Performance evaluation 5 Security considerations 5.1 Attack threat model perspective 5.2 Implementation approach perspective 6 Related work 7 Protocol extensions 7.1 Device recovery 7.2 Code update 8 Conclusion Notation Acronyms Sangeeta Mittal A review of machine learning techniques in cybersecurity and research opportunities 1 Introduction 2 Threats to cybersecurity and defense strategies 2.1 Threats to cybersecurity 2.2 Defense strategies for cybersecurity 3 Usage of machine learning in defense strategy implementations 4 Attack-wise ML-based defense strategies 4.1 Illicit access 4.2 Identity and information theft 4.3 Integrity attacks 4.4 Malware 4.5 Disruption of service 5 Limitations of machine learning-based solutions in cybersecurity 5.1 Data problems 5.2 Model problems 6 Opportunities for new ML paradigms 7 Conclusions Vasu Thakur, Vikas Kumar Roy, Nikhil Baliyan, Nupur Goyal, Rahul Nijhawan A framework for seborrheic keratosis skin disease identification using Vision Transformer 1 Introduction 2 Literature review 3 Data collection 4 Methodology 5 Convolutional neural network (CNN) 5.1 VGG-19 5.2 Inception v3 6 Vision Transformer 7 Results and discussion 7.1 Why Vision Transformer? 8 Conclusion Acronyms Preeti Malik, Ashwini Kumar Singh, Rohit Nautiyal, Swati Rawat Mapping AICTE cybersecurity curriculum onto CyBOK: a case study 1 Introduction 2 What is body of knowledge (BOK) 2.1 Cybersecurity initiatives by All India Council for Technical Education (AICTE) 2.2 Foundation of CyBOK in national certification program for academic degrees in cybersecurity in the United Kingdom 3 CyBOK mapping framework 3.1 Mapping resources 3.2 Mapping process by using the CyBOK mapping framework and mapping resources 4 Mapping AICTE curriculum for undergraduate program 5 Results and discussion 6 Conclusion Index De Gruyter series on the applications of mathematics in engineering and information sciences
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