Cybersecurity

Name: Cybersecurity | Cyber Defense, Privacy and Cyber Warfare
Brand: De Gruyter
Price: 149.95 EUR
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Cyber Defense, Privacy and Cyber Warfare

George Dimitoglou Leonidas Deligiannidis Hamid R. Arabnia(Editor)

De Gruyter (Publisher)

1st Edition

Published on 4. December 2025

XIV, 414 pages

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978-3-11-143654-8 (ISBN)

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Content

Intro
Preface
Contents
List of contributing authors
Game-based testing for active cyberdefense and cyberdeception
1 Introduction
2 Previous work
2.1 Military cyberspace operations
2.2 Active defenses in cyberspace and cyberdeception
2.3 Game analysis
3 The CCAT tool, an active-defense planner
3.1 Experiment design
3.2 Scenario components and testing
3.3 Possible actions
3.4 Costs and benefits of options
3.5 Training the models
3.6 Average final scores of the CCAT experiments
4 Decepgame, a planner against advanced persistent threats
4.1 Game design
4.1.1 Player generation
4.1.2 Action profiles and player specification generation
4.2 Implementation of the APT game
4.3 Results of the decepgame APT simulation
4.4 Discussion of the decepgame results
5 Precalculating defender tactics from attacker variables
6 Conclusions
References
Graph-ensemble methods for generating malware behavioral signatures
1 Introduction
1.1 Natural language processing approaches
1.2 Introduction to graph neural networks
1.3 Application of graph neural networks in anomaly detection
2 Methodology
2.1 Datasets
2.2 Model architectures
2.2.1 Feature concatenation
2.2.2 Embedding concatenation
2.3 Heterogeneous graphs
2.4 Graph feature importance and signatures
2.5 Model performance metrics
3 Results and discussion
3.1 Analysis of malicious behavior
3.2 Analysis of unknown binaries
3.3 Ensemble model complexity
References
Efficient cyber threat detection on SCADA systems using feature-grouped generative adversarial networks
1 Introduction
2 Related works
3 Generative adversarial networks
3.1 Generative models
3.2 GAN structure
3.3 Weaknesses of GANs
3.4 Loss function
4 Measuring performance
5 Evaluation setup
6 Grouping dataset
7 Model architecture
8 Results
9 Discussion
10 Conclusion
References
Applying artificial intelligence techniques to intrusion detection systems in serial-based industrial networks
1 Introduction
2 Background
2.1 Industrial networks
2.2 Security issues in industrial networks
2.3 Intrusion detection systems (IDSs)
3 AI techniques applied in IDS for industrial networks
4 IDS in industrial serial-based networks
5 Conclusion
References
A hybrid intelligent intrusion detection system
1 Introduction
2 Related works
3 Proposed HIIDS
4 The evaluation and analysis
4.1 Time analysis
4.2 Precision and accuracy
4.3 Data loss
5 Evaluation
6 Conclusion and future work
References
PwnPilot: could an adversary be pair programming with our most trusted software engineers?
1 Introduction
2 Background and related work
2.1 Potential benefits and risks of AI code assistants
2.2 Evaluating correctness, quality, and robustness of AI-generated code
2.3 Evaluating security of AI-generated code
2.4 A surreptitious adversary paired with every programmer?
2.5 Positioning PwnPilot within a taxonomy of generative AI misuse cases
3 PwnPilot: security smells, covert poisoning, and theoretically undetectable backdoors
3.1 Threat analysis: many paths to PwnPilot
4 A new hope: current mitigation options and future research directions
4.1 Building a truly trusted model
4.2 Trusting commercial enhancements
4.3 Automated static code analysis
4.4 AI-driven N-version programming (AID-NVP)
4.5 AI-accelerated automated testing
4.6 Diverse, multi-AI feedback loops
4.7 Improving AI interactions, prompt engineering, and prompt injection prevention
4.8 Using attack techniques for good: model extraction for validation
4.9 Future symbolic abstractions for robust, secure models
5 Technology readiness level (TRL) of PwnPilot mitigations options in 2024
5.1 An overview of technology readiness levels (TRLs)
5.2 TRLs for PwnPilot mitigation options in 2024
6 Final reflections on trusting trust in the age of AI pair programmers
References
How to attack a far galaxy and beyond
1 Introduction
2 Background
2.1 Side-channel attacks
2.2 Neural networks
3 CRISTALS-Kyber
3.1 Algorithm
3.2 Determiner leakage attack
3.3 Plaintext checking oracle
3.4 Multi-bit error injection attack
3.5 Other attacks
3.6 AI attacks against Kyber in short
4 CRYSTALS-Dilithium
4.1 Algorithm
4.2 Number-theoretic transform attack
4.3 Bit-unpacking function attack
4.4 Other attacks
4.5 AI attacks against Dilithium in short
5 FALCON
5.1 Algorithm
5.2 Floating point multiplication
5.3 Base sampler
5.4 AI attacks against FALCON in short
6 SPHINCS+
6.1 Algorithm
6.2 One-time signature schemes
6.3 AI attacks against SPHINCS^ {\mplus} in short
7 AI analysis
8 Conclusions
References
Injecting uniform chaotic sequences into an ANN's learning fabric to reduce overfitting
1 Introduction
2 Related works
2.1 Conventional techniques
2.2 Entropy-based techniques
2.3 Chaos injection versus random noise injection
3 Proposed work
3.1 Improved multiparametric tent map (MTM)
3.2 The influence of initial values on uniformity of chaotic sequences
3.3 Generate chaotic values based on dataset parameters
4 Experimental results
5 Conclusions and future work
References
Effectiveness of machine learning and deep learning in cybersecurity
1 Introduction
2 Literature survey
3 Cyber security: application of ML algorithms
3.1 Shallow learning
3.2 Deep learning
4 Machine learning algorithms applications
5 Evaluation
5.1 Shallow versus deep learning
5.2 Specific detectors versus general
5.3 Vulnerability to adversarial attacks
5.4 Selection of a machine learning algorithm
6 Conclusion
References
Quantum-enhanced cyber threat detection with mini-batch optimization
1 Introduction
2 Related works
3 Quantum binary classification research in cybersecurity
4 Experimental setup and results
4.1 Experimental setup
4.2 Experimental results
5 Conclusion and future work
References
Future of auditable AI systems
1 Introduction
2 Theoretical basics
2.1 Functionality of neural networks
2.2 Artificial intelligence challenges, attacks, and defenses
2.2.1 Definition
2.2.2 Classification of AI
2.2.3 AI challenges
2.2.4 Defense mechanisms
2.3 Auditing artificial intelligence
2.3.1 Forensic audit
2.3.2 Aims of auditing AI
2.3.3 eXplainable artificial intelligence (XAI)
2.3.4 Current possibilities to audit AI
3 Methods
3.1.1 Implementation of the continual learning model
3.1.2 Logging the weights of a continual learning AI system
3.1.3 Weight extraction
3.1.4 Logging visualization
3.1.5 Hashing the output
4 Results
4.1 Logging the weights of continual learning AI systems
4.1.1 Memory size of the weights log file versus training run
4.1.2 Memory size of the weights log file versus number of neurons
4.1.3 Proportion of zeros in the delta log file versus training run
4.2 Current possibilities of auditing intelligent systems
4.2.1 Memory size of the weights log file versus training run
4.2.2 Memory size of the weights log file versus number of neurons
4.2.3 Proportion of zeros in the delta log file versus training run
5 Discussion
5.1 Current possibilities of auditing artificial intelligence
5.1.1 eXplainable artificial intelligence
5.1.2 Statistical methods
5.1.3 Digital forensic readiness
5.1.4 Standardization
5.2 Possibilities of logging continual learning
5.2.1 eXplainable artificial intelligence
5.2.2 Auditing autonomous systems
5.2.3 Auditing text and image processing
5.2.4 Summary
5.3 Toward current research
5.3.1 Research projects
5.3.2 Transparency
5.3.3 Forensic audit
5.4 Artificial intelligence act and legal challenges
5.4.1 Risk classification
5.4.2 Logging
6 Conclusion
6.1 Statistical method
6.2 Forensic audit
6.3 Artificial intelligence act
6.4 Problem statement
References
Virtual cybersecurity testbeds for industrial Internet of Things
1 Introduction
2 Case study
2.1 Method
2.1.1 Virtual industrial testbed for cybersecurity
2.1.2 Denial-of-service (DoS) attack
3 Results
3.1 Virtual industrial testbed for cybersecurity
3.1.1 Testbed setup with VirtualBox
3.1.2 Testbed setup with Hyper-V
3.1.3 Testbed setup with VMware workstation Pro 16
3.2 Denial-of-service (DoS) attack
4 Discussion and conclusion
4.1 Findings
4.2 Future work
4.3 Conclusion
References
Security verification of authenticated encryption with associated data under chosen message attack assumption using Tamarin prover
1 Introduction
1.1 Background
1.2 Contributions
1.2.1 Proposed method
1.2.2 Case studies
1.3 Related works
1.4 Structure of this paper
2 Preliminaries
2.1 Tamarin prover description and verification result
3 Attack models subject to formalization
3.1 Digital signature of MITM attack model
3.2 EUF-CMA model
3.3 IND-CPA model
4 Formal verification of the digital signatures of the MITM attack, EUF-CMA, and IND-CPA models
4.1 Formalization: digital signature of MITM attack model
4.2 Formalization: EUF-CMA model
4.3 Formalization: IND-CPA model
5 Formal verification of AEAD
5.1 Formalization: AEAD for MthE
5.1.1 EUF-CMA model formalization: AEAD for MthE
5.1.2 IND-CPA model formalization: AEAD for MthE
5.2 Formalization: AEAD for encrypt-then-MAC
5.2.1 EUF-CMA model formalization: AEAD for encrypt-then-MAC
5.2.1 IND-CPA model formalization: AEAD for encrypt-then-Mac
6 Discussion of evaluation results
6.1 Evaluation result: AEAD
6.2 Limitation
6.3 Proof using heuristic oracle
7 Conclusion and future works
Appendix
7.1 Verification result of AEAD MthE
7.2 Verification result of AEAD EthM
7.3 Verification result of AEAD MthE with heuristic oracle
References
Security and privacy challenges in Internet of Medical Things (IoMT) using RFID and sensor nodes
1 Introduction
2 Objective
3 Literature review
4 Related works
5 Methodology
5.1 Devices
6 Challenges and issues
6.1 Monitoring and effectiveness
6.1.1 Patient discomfort
6.2 Energy consumption
6.3 Privacy considerations
7 Data analysis and validation
8 Conclusion
9 Future directions
References
Empowering users with an effective tool for social media spam detection
1 Introduction
2 Related works
2.1 Text preprocessing
2.2 Domain names
2.3 Detecting spam in Twitter
2.4 Detecting spam in Facebook
2.5 Activity of spammers
2.6 Detecting video spam
2.7 Detecting spam across social networks
3 Proposed method
4 Classifiers
5 Design and implementation
6 Evaluations
6.1 Performance evaluation
6.2 Functional evaluation
7 Conclusions and future work
References
Comparative analysis of email digital forensics tools validation
1 Introduction
2 Background
3 Tools and technology
3.1 Email forensics software tools
3.2 Email dataset
3.3 Hardware
4 Experiment methodology
4.1 Inbox repair tool (Scanpst.exe)
4.2 OS Forensics
4.3 Kernel for Outlook PST repair
4.4 SysTools Outlook Recovery
4.5 Paraben's Electronic Evidence Examiner (E3)
5 Experiment result analysis
6 Conclusion and future work
References
An implementation of a web platform for training in phishing attack detection using cognitive security, cognitive psychology, and game theory
1 Introduction
2 Theoretical framework
2.1 Social engineering attacks
2.2 Phishing attacks by email
2.2.1 Phishing tactics
2.2.2 Cognitive security
2.2.3 Game theory with decision trees
2.3 Cognitive psychology
2.4 Perception of risk
2.5 Description of the tools for the development of the web platform
2.5.1 Tools for backend
2.5.1.1 Java
2.5.1.2 Spring boot framework
2.6 Tools for the front end
2.6.1 Angular
2.6.2 Visual Studio Code
2.7 Database
2.7.1 MongoDB atlas
2.8 Deployment
2.8.1 Microsoft Azure
3 Related work
4 Methodological design
4.1 Stages of the DSR applied in the present research work
4.1.1 Development methodology
4.1.1.1 Scrum
4.1.1.2 Web platform design
4.2 Architecture design
4.3 Design of the risk perception questionnaire
4.4 Development of the web platform
4.4.1 First iteration
4.4.2 Second iteration
4.4.3 Third Iteration
4.5 Development of the algorithm using game theory with decision tree
4.5.1 Scenario definition
4.6 Development of the algorithm using game theory with decision trees
4.7 Evaluation of results
4.8 Paths of users on the platform
4.8.1 Average of the final score of the users
4.8.1.1 Phishing detection level by age
4.8.1.2 Comparison of the level of risk perception with the level of phishing detection
4.8.1.3 Analysis of user comments when selecting the response
5 Conclusions
6 Future work
References
Index
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