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Produktbild: Digital Forensics and Cyber Crime
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Digital Forensics and Cyber Crime 16th EAI International Conference, ICDF2C 2025, Miami, FL, USA, November 17–19, 2025, Proceedings,Part I

Aus der Reihe Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

89,99 €

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

12.08.2026

Abbildungen

XII, 191 illus., 146 illus. in color., farbige Illustrationen, schwarz-weiss Illustrationen

Herausgeber

Kim-Kwang Raymond Choo + weitere

Verlag

Springer

Seitenzahl

588

Maße (L/B)

23,5/15,5 cm

Sprache

Englisch

ISBN

978-3-032-22541-2

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

12.08.2026

Abbildungen

XII, 191 illus., 146 illus. in color., farbige Illustrationen, schwarz-weiss Illustrationen

Herausgeber

Verlag

Springer

Seitenzahl

588

Maße (L/B)

23,5/15,5 cm

Sprache

Englisch

ISBN

978-3-032-22541-2

Herstelleradresse

Springer International Publishing AG
Gewerbestr. 11
6330 Cham
Schweiz
Url: www.springer.com

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  • Produktbild: Digital Forensics and Cyber Crime

  • .- AI and Machine Learning in Digital Forensics: Papers applying AI, LLMs, and deep learning to detect anomalies,enhance forensics, or improve model reliability.

    .- Fine-Tuning Large Language Models for Anomaly Detection in Distributed System Logs.

    .- Generative AI-Driven Anomaly Detection in Soil Electrical Conductivity Using Temporal Autoencoders.

    .- Autoencoder-Based Intrusion Detection: A Hybrid Deep Learning Approach.

    .- Memory-Recall-Based Watermarking for Data Misuse Detection in Large Language Models.

    .- Comparative Study of Quantum and Classical Layers in Hybrid Quantum Neural Networks.

    .- Automated Injury Severity Assessment Using Knowledge Grounded Large Language Models.

    .- A Machine Learning Approach for Intrusion Detection in Drone Communication Networks.

    .- MRES-S: Multi-scale Deep Learning Network for Hardware Trojan Detection.

    .- MSMC-MobileNet: An Automated Multi-Scale and Multi-Contextual MobileNetv3 for Malware Detection Based on IoT.

    .- LLM4PDF: Semantic-Aware Malicious PDF Detection Using LLMs.

    .- LMBE: Unsupervised Detection of Lateral Movement via User Behavior Embedding.

    .- Digital Forensics and Incident Response: Focused on forensic investigation, evidence recovery, data analysis, and structured workflows.

    .- Analyzing Digital Forensic Data Using Process Mining Techniques: A Case Study.

    .-  Digital Forensic Investigation of Social Robots: Zenbo,Zenbo Jr., and Misty II as Case Studies.

    .- iOS Cookie Forensics with Autopsy Tool.

    .- What’s Next, Cloud? A Forensic Framework for Analyzing Self-Hosted Cloud Storage Solutions.

    .- Improving the forensic integrity of Mark-of-the-Web (MOTW) files.

    .-  A Methodology for Event Log Generation from Unstructured Digital Forensics Data.

    .- Toward Structured Memory Forensics: A MITRE ATT&CK-Aligned Workflow for Malware Investigation.

    .- MALDroid: An Explainable Android Malware Detection Framework Leveraging Temporal and Semantic Contextual Features.

    .-  A Real-Time Face Swap Detection Model for Video Chatting Scams.

    .-  LLM-Assisted Digital Forensic Investigations of Prompt Injection Attacks: Evidence Analysis and Representation.

    .- Integration of NLP in Digital Forensics: A Pilot Study of Practitioner Perceptions on Chat Data Analysis Tools.

    .- An LLM-Driven Iterative Workflow for Ontological Mapping of Digital Forensic Artifacts.

    .- Adversarial Machine Learning and Attack Detection: Addressing adversarial AI, attack simulation, and defensive mechanisms.

    .- Securing Federated Learning: A Hybrid Defense Against Poison Injection Attacks in LLM.

    .- Temporal Sparse Black-Box Adversarial Attack on Deepfake Video Detection Models.

    .- A Reward-driven Automated Webshell Malicious-code Generator for Red-teaming.

    .- Detecting Container Escape Attacks via Graph Neural Networks on System Call Graphs.

    .- ShellSight-LLM: Detecting Successful Webshell Intrusions via Optimized LLM.

    .- Threshold-driven: Reversible Adversarial Face Examples via Latent Diffusion Model.

    .-  Talking Like a Phisher: LLM-Based Attacks on Voice Phishing Classifiers.

    .- Deepfake Forensics Adapter: A Dual-Stream Network for Generalizable Deepfake Detection.

    .- Cyber Threat Intelligence and Malware Analysis: Focus on malware detection, honeynets, phishing, traffic analysis, and intrusion defense.

    .- FedHAP-MTD: Personalized Federated Malicious Traffic Detection Based on Hierarchical Updating and Adaptive Learning.

    .- Validation of IP Reputations Through Redirection of Unsolicited Network Traffic to an Interactive Honeynet.

    .- A Malicious IoT Traffic Detection Method Based on Recursive Feature Addition Using Graph Neural Network.