BERTector: Intrusion Detection Based on Joint-Dataset Learning

Intrusion detection systems IDS are facing challenges in generalization and robustness due to the heterogeneity of network traffic and the diversity of attack patterns. To address this issue, we propose a new joint-dataset training paradigm for IDS and propose a scalable BERTector framework based...

MirGuard: Towards a Robust Provenance-Based Intrusion Detection System against Graph Manipulation Attacks

Learning-based Provenance-based Intrusion Detection Systems PIDSes have become essential tools for anomaly detection in host systems due to their ability to capture rich contextual and structural information, as well as their potential to detect unknown attacks. However, recent studies have shown...

AI SOC 101: Key Capabilities Security Leaders Need to Know

Security operations have never been a 9-to-5 job. For SOC analysts, the day often starts and ends deep in a queue of alerts, chasing down what turns out to be false positives, or switching between half a dozen tools to piece together context. The work is repetitive, time-consuming, and high-stake...

IAG: Input-Aware Backdoor Attack on VLMs for Visual Grounding

Vision-language models VLMs have shown significant advancements in tasks such as visual grounding, where they localize specific objects in images based on natural language queries and images. However, security issues in visual grounding tasks for VLMs remain underexplored, especially in the conte...

MalFlows: Context-Aware Fusion of Heterogeneous Flow Semantics for Android Malware Detection

Static analysis, a fundamental technique in Android app examination, enables the extraction of control flows, data flows, and inter-component communications ICCs, all of which are essential for malware detection. However, existing methods struggle to leverage the semantic complementarity across...

Breaking Obfuscation: Cluster-Aware Graph with LLM-Aided Recovery for Malicious JavaScript Detection

With the rapid expansion of web-based applications and cloud services, malicious JavaScript code continues to pose significant threats to user privacy, system integrity, and enterprise security. But, detecting such threats remains challenging due to sophisticated code obfuscation techniques and...

ZIUM: Zero-Shot Intent-Aware Adversarial Attack on Unlearned Models

Machine unlearning MU removes specific data points or concepts from deep learning models to enhance privacy and prevent sensitive content generation. Adversarial prompts can exploit unlearned models to generate content containing removed concepts, posing a significant security risk. However,...

UBUNTU-CVE-2025-38417

In the Linux kernel, the following vulnerability has been resolved: ice: fix eswitch code memory leak in reset scenario Add simple eswitch mode checker in attaching VF procedure and allocate required port representor memory structures only in switchdev mode. The reset flows triggers VF if present...

Explainable Vulnerability Detection in C/C++ Using Edge-Aware Graph Attention Networks

Detecting security vulnerabilities in source code remains challenging, particularly due to class imbalance in real-world datasets where vulnerable functions are under-represented. Existing learning-based methods often optimise for recall, leading to high false positive rates and reduced usability...

Game Theory Meets LLM and Agentic AI: Reimagining Cybersecurity for the Age of Intelligent Threats

Protecting cyberspace requires not only advanced tools but also a shift in how we reason about threats, trust, and autonomy. Traditional cybersecurity methods rely on manual responses and brittle heuristics. To build proactive and intelligent defense systems, we need integrated theoretical...

SoK: a Systematic Review of Context- and Behavior-Aware Adaptive Authentication in Mobile Environments

As mobile computing becomes central to digital interaction, researchers have turned their attention to adaptive authentication for its real-time, context- and behavior-aware verification capabilities. However, many implementations remain fragmented, inconsistently apply intelligent techniques, an...

WATCHDOG: an Ontology-AWare Risk AssessmenT ApproaCH Via Object-Oriented DisruptiOn Graphs

When considering risky events or actions, we must not downplay the role of involved objects: a charged battery in our phone averts the risk of being stranded in the desert after a flat tyre, and a functional firewall mitigates the risk of a hacker intruding the network. The Common Ontology of Val...

O2Former:Direction-Aware and Multi-Scale Query Enhancement for SAR Ship Instance Segmentation

Instance segmentation of ships in synthetic aperture radar SAR imagery is critical for applications such as maritime monitoring, environmental analysis, and national security. SAR ship images present challenges including scale variation, object density, and fuzzy target boundary, which are often...

Embedding Trust at Scale: Physics-Aware Neural Watermarking for Secure and Verifiable Data Pipelines

We present a robust neural watermarking framework for scientific data integrity, targeting high-dimensional fields common in climate modeling and fluid simulations. Using a convolutional autoencoder, binary messages are invisibly embedded into structured data such as temperature, vorticity, and...

DinoCompanion: an Attachment-Theory Informed Multimodal Robot for Emotionally Responsive Child-AI Interaction

Children's emotional development fundamentally relies on secure attachment relationships, yet current AI companions lack the theoretical foundation to provide developmentally appropriate emotional support. We introduce DinoCompanion, the first attachment-theory-grounded multimodal robot for...

FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks

Graph Convolutional Neural Networks GCNs have gained widespread popularity in various fields like personal healthcare and financial systems, due to their remarkable performance. Despite the growing demand for cloud-based GCN services, privacy concerns over sensitive graph data remain significant...

GeoClip: Geometry-Aware Clipping for Differentially Private SGD

Differentially private stochastic gradient descent DP-SGD is the most widely used method for training machine learning models with provable privacy guarantees. A key challenge in DP-SGD is setting the per-sample gradient clipping threshold, which significantly affects the trade-off between privac...

Video Signature: In-Generation Watermarking for Latent Video Diffusion Models

The rapid development of Artificial Intelligence Generated Content AIGC has led to significant progress in video generation but also raises serious concerns about intellectual property protection and reliable content tracing. Watermarking is a widely adopted solution to this issue, but existing...

Keeping an Eye on LLM Unlearning: the Hidden Risk and Remedy

Although Large Language Models LLMs have demonstrated impressive capabilities across a wide range of tasks, growing concerns have emerged over the misuse of sensitive, copyrighted, or harmful data during training. To address these concerns, unlearning techniques have been developed to remove the...

TensorShield: Safeguarding On-Device Inference by Shielding Critical DNN Tensors with TEE

To safeguard user data privacy, on-device inference has emerged as a prominent paradigm on mobile and Internet of Things IoT devices. This paradigm involves deploying a model provided by a third party on local devices to perform inference tasks. However, it exposes the private model to two primar...