Security and Privacy in Communication Networks: 22nd EAI International Conference, SecureComm 2026, Lancaster, UK, July 21–24, 2026, Proceedings, Part II

Security and Privacy in Communication Networks by Cao, Yinzhi; Luo, Bo; Meng, Weizhi;

22nd EAI International Conference, SecureComm 2026, Lancaster, UK, July 21–24, 2026, Proceedings, Part II

Sorozatcím: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering;

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A termék adatai:

Kiadó Springer Nature Switzerland
Megjelenés dátuma 2026. szeptember 1.

ISBN 9783032327666
Kötéstípus Puhakötés
Terjedelem oldal
Méret 235x155 mm
Nyelv angol
Illusztrációk Approx. 700 p.

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Adatkezelés a számítógépes rendszerekben Operációs rendszerek és grafikus felhasználói felületek Számítógépes hálózatok általában A számítógép és az információs technológia hatása a társadalomra

Hosszú leírás:

This two-volume set LNICST 704 - 705 constitutes the proceedings of the 22nd EAI International Conference on Security and Privacy in Communication Networks, SecureComm 2026, held in Lancaster, UK, during July 21–24, 2026.

The 50 full papers and 1 poster paper included in these volumes were carefully reviewed and selected from 143 submissions. They are organized in the following topical sections:

Part I: Authentication, Biometrics & Human Factors; Mobile/Network Security & Measurement; Cryptanalysis, Post-Quantum & Smart-Contract Incentives; Secure & Privacy-Preserving Federated Learning; Blockchain Protocols, Mining & Cross-Chain Systems; Cryptography: Credentials, FE, FHE & PIR; Post-Quantum & Lattice Cryptography; and Applied Crypto for Authentication & Data Hiding.

Part II: AI for Threat Detection & Network Defense; Network Measurement, Fingerprinting & Routing Anomalies; System & Software Security; LLMs and LLM-assisted Analysis; Privacy-Preserving Analytics & Energy Trading; Work-in-Progress session; and Posters.

Több

Tartalomjegyzék:

.- AI for Threat Detection & Network Defense.

.- C2Detect: A Deep Learning based Real-Time Detection of Stealthy Command and Control (C2) Activities from HTTPS Traffic.

.- Hostmon: MoE-Orchestrated Multi-Agent Reinforcement Learning for DDoS Defense.

.- Empirical Evaluation and Benchmarking of Hybrid ML/XAI Models for HTTP-layer DDoS Detection.

.- Adaptive and Uncertainty-Aware Intrusion Detection with Hybrid Meta-Learning under Proxy-Adversarial Exposure.

.- Network Measurement, Fingerprinting & Routing Anomalies.

.- 6ixmap: A Study on Active-Passive Measurement and Asset Exposure in the IPv6 Address Space.

.- SF-EET: Online BGP Anomaly Detection via Structural Frequency Tracking and Emerging Edge Identification.

.- SatRFID-Net: A Robust RF Fingerprinting Framework for Anomaly Detection in Satellite Communications.

.- System & Software Security.

.- ConfigWiz: Automating Privilege Configuration for Containerized Applications.

.- Janus: Coordinating Vulnerability Prevention and Exploit-chain Mitigation for Containerized CI/CD Pipelines.

.- BinGraph: Deep Structural-Semantic Learning for Vulnerability Detection in Cross-Architecture Obfuscated Binaries and IoT Firmware.

.- LLMs and LLM-assisted Analysis.

.- LADE: LLM-Assisted Advanced Persistent Threat Detection and Explanation.

.- SNIFFER: Early Threat Detection via Chain-of-Thought of Sequence Generation and Context Alignment.

.- Hide Your Malicious Goal Into Benign Narratives: Jailbreak Large Language Models through Carrier Articles.

.- Understanding Phishing Scams in the Steam Ecosystem.

.- Privacy-Preserving Analytics & Energy Trading.

.- Federated Graph Analytics with Privacy: The Case of Parallel Chain Detection.

.- P2NC-DET: A Privacy-Preserving and Network Constrained Decentralized Energy Trading Scheme based on Blockchain.

.- Z-RegStore: A Physical-Constraint-Aware and Regulatory Compliant Privacy-Preserving Energy Settlement Scheme for Microgrids.

.- A Trustworthy Incentive Scheme Based on Goal Gradient Effect to Improve Data Accuracy and Coverage in Vehicular Crowdsensing.

.- Work-in-Progress session.

.- Q-RobHFL: A QKD-Enhanced and Adversarially Robust Hierarchical Federated Learning Framework for IoMT.

.- Enhancing Privacy in Explainable AI by Applying Quantum Machine Learning.

.- Security-aware Resource Allocation for Fog Computing using MDP.

.- Localizing Hot Pixels in Android Malware Images.

.- Posters.

.- Poster: SHAP-RM : An XAI Framework for Evaluating Machine Learning Reliability.