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Computer Security. ESORICS 2024 International Workshops SECAI, DisA, CPS4CIP, and SecAssure, Bydgoszcz, Poland, September 16–20, 2024, Revised Selected Papers, Part II

Aus der Reihe Lecture Notes in Computer Science
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85,99 € UVP 96,29 €

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

01.04.2025

Abbildungen

XVI, 541 p. 113 illus., 94 illus. in color.

Herausgeber

Harsha Kalutarage + weitere

Verlag

Springer

Seitenzahl

541

Maße (L/B/H)

23,5/15,5/3 cm

Gewicht

838 g

Sprache

Englisch

ISBN

978-3-031-82361-9

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

01.04.2025

Abbildungen

XVI, 541 p. 113 illus., 94 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

541

Maße (L/B/H)

23,5/15,5/3 cm

Gewicht

838 g

Sprache

Englisch

ISBN

978-3-031-82361-9

Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: ProductSafety@springernature.com

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  • Produktbild: Computer Security. ESORICS 2024 International Workshops
  • Produktbild: Computer Security. ESORICS 2024 International Workshops

  • SECAI PAPERS:
    Feasibility Study for Supporting Static Malware Analysis Using LLM.- PSY: Posterior Sampling Based Privacy Enhancer in Large Language Models.- Systematic Bug Reproduction with Large Language Model.- BOTracle: A framework for Discriminating Bots and Humans.- Deep Learning for Network Anomaly Detection under Data Contamination: Evaluating Robustness and Mitigating Performance Degradation.- On Intrinsic Cause and Defense of Adversarial Examples in Deep Neural Networks.- Effects of Poisoning Attacks on Causal Deep Reinforcement Learning.- Generating Traffic-Level Adversarial Examples from Feature-Level Specifications.- PhishCoder: Efficient Extraction of Contextual Information from Phishing Emails.- .On the Robustness of Malware Detectors to Adversarial Samples.- Towards AI-Based Identification of Publicly Known Vulnerabilities.- Machine Learning-Based Secure Malware Detection using Features from Binary Executable Headers.- Improving Adversarial Robustness in Android Malware Detection by Reducing the Impact of Spurious Correlations.- Multi-Objective Evolutionary Algorithm for Automatic Generation of Adversarial Metamorphic Malware.- A RAG-Based Question-Answering Solution for Cyber-Attack Investigation and Attribution.
    DisA PAPERS:
    Recognition of Remakes and Fake Facial Images.- A Novel Method of Improving Intrusion Detection Systems Robustness Against Adversarial Attacks, through Feature Omission and a Committee of Classifiers.- Proposition of a Novel Type of Attacks Targeting Explainable AI Algorithms in Cybersecurity.- Data structures towards the recognition of fake news and disinformation written in Polish.
    CPS4CIP PAPERS:
    Characterizing Prediction Model Responses to Attack Inputs: A Study with Time-Series Power Consumption Data.- Best Practices - based Training for Improving Cybersecurity in Power Grids.- Proactive Cyber Security Strategies for Securing Critical National Infrastructure.- Weaponizing Disinformation Against Critical Infrastructures.
    SecAssure PAPERS:
    Compliance-driven CWE Assessment by Semantic Similarity.- Enabling Android Application Monitoring by Characterizing Security-Critical Code Fragments.- MITRE-Based APT Attack Generation and Prediction.- Assuring Privacy of AI-Powered Community Driven Android Code Vulnerability Detection.- Formalizing Federated Learning and Differential Privacy for GIS systems in IIIf.- AI-Assisted Assurance Profile Creation for System Security Assurance.- Attack to Defend: Gamifying the MITRE ATT&CK for Cyber Security Training using the COFELET Framework.- Canary in the Coal Mine: Identifying Cyber Threat Trends through Topic Mining -- Stack Overflow Case Study.