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A serious security incident has recently emerged involving Feiniu (fnOS) Network Attached Storage devices. These systems are being actively targeted and infected by the Netdragon botnet, a malware strain that first appeared in October 2024. The attackers are leveraging undisclosed security vulnerabilities within the fnOS platform to implant malicious code. This campaign represents a focused […]

The post Feiniu NAS Devices Infected in Large-Scale Netdragon Botnet Attack Exploiting Unpatched Vulnerabilities appeared first on Cyber Security News.

Digital sovereignty is now a strategic imperative for many European organizations. According to a new IDC Market Note¹, “Sovereignty is not viewed just as a contractual consideration, but as an architectural one, and one of technical feasibility.”

The post IDC Market Note: Surging Demand for EU Data Sovereignty Drives New Cybersecurity-Cloud Partnership appeared first on Security Boulevard.

Session 12B: Malware

Authors, Creators & Presenters: Adrian Shuai Li (Purdue University), Arun Iyengar (Intelligent Data Management and Analytics, LLC), Ashish Kundu (Cisco Research), Elisa Bertino (Purdue University)

PAPER
Revisiting Concept Drift in Windows Malware Detection: Adaptation to Real Drifted Malware with Minimal Samples

In applying deep learning for malware classification, it is crucial to account for the prevalence of malware evolution, which can cause trained classifiers to fail on drifted malware. Existing solutions to address concept drift use active learning. They select new samples for analysts to label and then retrain the classifier with the new labels. Our key finding is that the current retraining techniques do not achieve optimal results. These techniques overlook that updating the model with scarce drifted samples requires learning features that remain consistent across pre-drift and post-drift data. The model should thus be able to disregard specific features that, while beneficial for the classification of pre-drift data, are absent in post-drift data, thereby preventing prediction degradation. In this paper, we propose a new technique for detecting and classifying drifted malware that learns drift-invariant features in malware control flow graphs by leveraging graph neural networks with adversarial domain adaptation. We compare it with existing model retraining methods in active learning-based malware detection systems and other domain adaptation techniques from the vision domain. Our approach significantly improves drifted malware detection on publicly available benchmarks and real-world malware databases reported daily by security companies in 2024. We also tested our approach in predicting multiple malware families drifted over time. A thorough evaluation shows that our approach outperforms the state-of-the-art approaches.

ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

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The post NDSS 2025 – Revisiting Concept Drift In Windows Malware Detection appeared first on Security Boulevard.

In a September 2025 incident response case, investigators found a rogue virtual machine inside a VMware vSphere environment and tied it with high confidence to Muddled Libra, also tracked as Scattered Spider and UNC3944. The VM acted like a quiet staging host, giving the intruders a place to recon the network, pull down tools, and […]

The post Rogue VM Linked to Muddled Libra in VMware vSphere Attack, Revealing Key TTPs appeared first on Cyber Security News.

New research reveals a 1-trillion-attribute threat landscape driven by machine speed and scale, and high-density credential consolidation. LOS ALTOS, CA — February 12, 2026 — Constella, the leader in Identity Risk Intelligence, today announced the release of its flagship 2026 Identity Breach Report. The report details a fundamental shift in the cyber threat landscape, moving from the …

The post Constella Intelligence Unveils 2026 Identity Breach Report: The Industrialization of Identity appeared first on Security Boulevard.