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    • Nyhet

    Digital Forensics and Cyber Crime

    16th EAI International Conference, ICDF2C 2025, Miami, FL, USA, November 17–19, 2025, Proceedings,Part I

    AvKim-Kwang Raymond Choo,Alexander Perez-Pons

    Häftad, Engelska, 2026

    Del 685 i serien Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

    973 kr

    Beställningsvara. Skickas inom 5-8 vardagar. Fri frakt över 249 kr.

    Beskrivning

    This two-volume set, LNICST 685 and 686, constitutes the refereed post-conference proceedings of the 16th EAI International Conference on Digital Forensics and Cyber Crime, ICDF2C 2025, Miami, FL, USA, during November 17–19, 2025.The 66 full papers and 1 poster paper included in these volumes were carefully reviewed and selected from 163 submissions. They have been organized in the following topical sections:Part I: AI and Machine Learning in Digital Forensics: Papers applying AI, LLMs, and deep learning to detect anomalies,enhance forensics, or improve model reliability; Digital Forensics and Incident Response: Focused on forensic investigation, evidence recovery, data analysis, and structured workflows; Adversarial Machine Learning and Attack Detection: Addressing adversarial AI, attack simulation, and defensive mechanisms; and Cyber Threat Intelligence and Malware Analysis: Focus on malware detection, honeynets, phishing, traffic analysis, and intrusion defense.Part II: Cyber Threat Intelligence and Malware Analysis: Focus on malware detection, honeynets, phishing, traffic analysis, and intrusion defense; Digital Evidence Management, Blockchain, and Privacy: Addressing blockchain forensics, data provenance, and privacy-preserving computation; Critical Infrastructure and Cyber-Physical Security: Cyber resilience in physical and industrial systems, including IoT, satellites, and SCADA; Legal, Policy, and AI Governance in Digital Forensics: Studies bridging AI governance, digital law, compliance, and socio-technical policy; and Posters & Demos.

    Produktinformation

    • Utgivningsdatum:2026-07-17
    • Mått:155 x 235 x 33 mm
    • Vikt:896 g
    • Format:Häftad
    • Språk:Engelska
    • Serie:Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
    • Antal sidor:588
    • Förlag:Springer Nature Switzerland AG
    • ISBN:9783032225412

    Utforska kategorier

    • Brottsutredning och kriminalteknik inom Samhälle och politik
    • IT-säkerhet inom Data och IT
    • Nätverk och kommunikation inom Data och IT

    Innehållsförteckning

    • .- 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.
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