GNN-Enhanced Traffic Anomaly Detection for Next-Generation SDN-Enabled Consumer Electronics

Consumer electronics CE connected to the Internet of Things are susceptible to various attacks, including DDoS and web-based threats, which can compromise their functionality and facilitate remote hijacking. These vulnerabilities allow attackers to exploit CE for broader system attacks while...

EUVD-2021-0247

Malware in sbrugna...

EUVD-2019-0092

Malware in sbrugna...

EUVD-2020-21905

Malware in sbrugna...

EUVD-2021-17755

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EUVD-2020-21906

Malware in sbrugna...

Adversarial-Resilient RF Fingerprinting: A CNN-GAN Framework for Rogue Transmitter Detection

Radio Frequency Fingerprinting RFF has evolved as an effective solution for authenticating devices by leveraging the unique imperfections in hardware components involved in the signal generation process. In this work, we propose a Convolutional Neural Network CNN based framework for detecting rog...

NatGVD: Natural Adversarial Example Attack Towards Graph-Based Vulnerability Detection

Graph-based models learn rich code graph structural information and present superior performance on various code analysis tasks. However, the robustness of these models against adversarial example attacks in the context of vulnerability detection remains an open question. This paper proposes...

EUVD-2024-34426

Malicious code in bioql PyPI...

EUVD-2024-37921

Malicious code in bioql PyPI...

EUVD-2023-0183

Malicious code in bioql PyPI...

EUVD-2025-30894

Malicious code in bioql PyPI...

EUVD-2024-36591

Malicious code in bioql PyPI...

EUVD-2022-30644

Malicious code in bioql PyPI...

EUVD-2024-36016

Malicious code in bioql PyPI...

EUVD-2024-19406

Malicious code in bioql PyPI...

EUVD-2024-38230

Malicious code in bioql PyPI...

Adaptive Deception Framework with Behavioral Analysis for Enhanced Cybersecurity Defense

This paper presents CADL Cognitive-Adaptive Deception Layer, an adaptive deception framework achieving 99.88% detection rate with 0.13% false positive rate on the CICIDS2017 dataset. The framework employs ensemble machine learning Random Forest, XGBoost, Neural Networks combined with behavioral...

Vision Transformers: the Threat of Realistic Adversarial Patches

The increasing reliance on machine learning systems has made their security a critical concern. Evasion attacks enable adversaries to manipulate the decision-making processes of AI systems, potentially causing security breaches or misclassification of targets. Vision Transformers ViTs have gained...

EvoMail: Self-Evolving Cognitive Agents for Adaptive Spam and Phishing Email Defense

Modern email spam and phishing attacks have evolved far beyond keyword blacklists or simple heuristics. Adversaries now craft multi-modal campaigns that combine natural-language text with obfuscated URLs, forged headers, and malicious attachments, adapting their strategies within days to bypass...