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Related Articles from SNS
MEC-Cox: Machine-Learning-Assisted Generalized Entropy Calibration for ATT Marginal Hazard-Ratio Estimation
arXiv:2606.08305v1 Announce Type: cross Abstract: Externally controlled survival trials are increasingly used when concurrent randomized controls are infeasible, particularly in oncology and rare-disease settings with time-to-event endpoints. We target an average-treatment-effect-on-the-treated (ATT)-type marginal hazard-ratio estimand, comparing treatment with counterfactual control in the treated trial population, and estimate it using inverse-probability-weighted (IPW) Cox regression....
ATT-CR: Adaptive Triangular Transformer for Cloud Removal
arXiv:2606.05999v1 Announce Type: new Abstract: Cloud removal aims to accurately reconstruct the ground objects obscured by clouds in remote sensing images. Existing Transformer-based methods utilizing self-attention have shown impressive results by effectively modeling long-range dependencies in cloudy images. However, they suffer from the following issues: 1) the high computational complexity of self-attention limits scalability; 2) treating both cloudy and clean pixels as valid within the...
AutoSUT: The Environment Semantics Gap in Structured CTI for Adversary Emulation
Announce Type: new Abstract: Structured Cyber Threat Intelligence (CTI) is increasingly used for adversary emulation, detection evaluation, and cyber range design. However, these workflows still require a target System Under Test (SUT) whose environment is not fully described by public CTI. We measure how much of that environment can be derived from MITRE ATT&CK Structured Threat Information Expression (STIX) bundles.
CTIConnect: A Benchmark for Retrieval-Augmented LLMs over Heterogeneous Cyber Threat Intelligence
arXiv:2510.11974v2 Announce Type: replace Abstract: Cyber Threat Intelligence (CTI) is foundational to modern cybersecurity, enabling organizations to proactively defend against evolving threats. However, the sheer volume and heterogeneity of CTI data, spanning structured knowledge bases (CVE, CWE, CAPEC, MITRE ATT&CK) and unstructured threat reports, far exceed the capacity of manual analysis. The strong contextual understanding and reasoning of Large Language Models (LLMs) have driven...
Common sign of high cholesterol can be found in your legs — what to look out for
Common sign of high cholesterol can be found in your legs — what to look out for Having high cholesterol levels can increase your risk of heart disease High cholesterol typically shows no symptoms, yet telltale signs can occasionally emerge. One potential warning sign is cholesterol deposits on the Achilles tendon – but what do they actually look like? Cholesterol, a waxy substance found in your blood, is essential for producing healthy cells.
Attention-guided Fine-tuning of Multimodal Large Language Models Improves Chain-of-Thought Reasoning
arXiv:2606.01558v1 Announce Type: new Abstract: The effectiveness of Chain-of-Thought (CoT) prompting in Multimodal Large Language Models (MLLMs) remains uncertain: across several visual reasoning benchmarks, CoT prompting often degrades performance compared to direct prompting. In this paper, we provide a systematic analysis of CoT behavior in three modern MLLM families across model scales on datasets requiring step-wise visual evidence. Our analysis identifies two recurring failure modes:...
AI-Native Closed-Loop Security for 6G-Enabled Cyber-Physical Systems: From Edge Detection to Network-Wide Mitigation
arXiv:2606.08173v1 Announce Type: new Abstract: In sixth-generation (6G) networks, billions of cyber-physical systems (CPSs) - autonomous vehicles, smart grids, industrial robots, and remote-surgical equipment - will run over ultra-reliable low-latency slices, collapsing the gap between a remote breach and physical harm to milliseconds, a budget perimeter firewalls and centralised security operations centres cannot meet. This survey reframes 6G CPS security as a closed-loop, AI-native...
MAECO-Lite: Modular Ontology for Dynamic Malware Analysis
arXiv:2605.31199v1 Announce Type: new Abstract: Capturing dynamic malware behavior in a practical but still semantically precise manner remains a significant challenge in cyber threat intelligence. While standards such as MAEC and STIX provide widely adopted vocabularies for describing malware artifacts and observations, they represent data with considerable complexity in structures that often obscure important ontological distinctions. In particular, they tend to conflate enduring malware...
Self-Supervised Learning for Android Malware Detection on a Time-Stamped Dataset
Announce Type: replace Abstract: Android malware detectors built with machine learning often suffer from temporal bias: models are trained and evaluated without respecting apps' actual release times, inflating accuracy and weakening real-world robustness. We address this by constructing a time-stamped dataset of benign and malicious Android apps and introducing a timestamp-verification procedure to ensure temporal accuracy. We then propose a detection framework that uses Bootstrap Your Own...
From Attack Simulation to SIEM Rule: Deterministic Detection-as-Code Synthesis with Probe-Level Traceability
Announce Type: new Abstract: Security teams routinely simulate attacks against their own systems to check whether their monitoring would catch a real intruder. These Breach-and-Attack-Simulation (BAS) tools surface findings, but the security information and event management (SIEM) systems that watch production need detection rules -- and today a human bridges that gap by hand, reading each finding and writing the corresponding Sigma rule (a vendor-neutral detection format). We show this...