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Earlier sources: Security
Digital security protects systems, software, accounts, and information from misuse. Explore cybersecurity, vulnerabilities, threat intelligence, cryptography, privacy, and identity, with links to the underlying advisories and research.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗639 classified signals in the fourteen-day window. Recent momentum: steady. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- arXiv · Published · First archived
Online AutoML: Evaluating Poisoning Attacks on Adversarial Training Defense Strategy in IoT Networks ↗
Machine learning (ML)-powered poisoning attack vectors are adversarial maneuvers whereby an attacker intentionally inserts, corrupts, or alters training data to distort an ML model's learning process. The objective is to diminish model efficacy, instill biases, induce misclassifications, or include concealed backdoors that may be attacked during implementation. In streaming contexts, poisoning attacks pose significant risks since models perpetually update based on incoming streams of data. An assailant may incrementally introduce harmful samples into this data stream, leading the model to assi…
- Hacker News · Posted on Hacker News · First archived
Show HN: Tip Tool – dodge spam and phishing ↗
Hacker News discussion.
- arXiv · Published · First archived
Agentic-ZTA: A Multi-Agent Architecture for Autonomous Zero Trust Enforcement ↗
Agentic AI is emerging as a promising paradigm for automating complex cybersecurity decisions, yet its use in enforcing zero trust introduces significant challenges in safety, reliability, and policy compliance. This paper presents Agentic AI based zero trust architecture (Agentic-ZTA) that operationalizes the NIST SP 800-207 ZTA architecture control loop through coordinated multi- agent decision pipeline. In the proposed framework, policy knowledge is embedded into a retrieval-augmented generation pipeline and retrieved at inference time as top-k relevant policies. Access requests are interce…
- GitHub · Repository created · First archived
MediaPublishing/ainauten-voice ↗
AInauten Voice: lokale Diktier-App für macOS. Quellcode, Screenshots, Video und Installation mit einem Agent.
- Hacker News · Posted on Hacker News · First archived
Topscan.me – a new security platform with a new approach to scanning ↗
Topscan.me is a security scanning platform for companies where security is somebody's second job, usually a CTO or a DevOps lead. It covers what is normally bought from three vendors: code analysis, scanning of running web applications and monitoring of the external perimeter, all in one workspace with a deadline on every finding. What distinguishes it is a handful of decisions about how scanning should behave, described below. What it covers - C…
- OpenAlex · Published · First archived
AI-Based Real-Time Person Counting and Security Monitoring System Using YOLO Object Detection and Multi-Object Tracking ↗
AbstractManual counting of people at entrances, exhibitions and access-controlled premises is slow, labour-intensive anderror-prone, motivating automated, vision-based alternatives. This paper presents the design, implementation and fieldtesting of an AI-based counter and security monitoring system that couples a deep-learning object detector (YOLO)with a multi-object tracker to count and monitor people crossing a virtual reference line in live video. Frames capturedfrom a CCTV/webcam feed are processed with OpenCV; a pretrained YOLO model localises every person in theframe, and a tracking sta…
- OpenAlex · Published · First archived
Risk-Adaptive Security Authorization for Critical Information Infrastructure: A Remediation-First Framework for Dynamic Target Security Profiles ↗
Security authorization relies on a target security profile that is relatively stable, whereas threats, exposure, control health, and assessment evidence change continuously. We present a two-stage decision-support model that separates operational remediation from structural insufficiency of an already authorized profile. The first stage tests whether an assurance-adjusted authorization-risk index can be restored to tolerance using admissible actions while the profile remains fixed. Only when this problem is infeasible does the second stage generate a minimum-burden candidate profile for assess…
- OpenAlex · Published · First archived
Information-Theoretic Limits and One-Bit Estimation of Sparse Relation Matrices Under Personalized Local Differential Privacy and Byzantine Contamination ↗
We study estimation of globally sparse relation probability matrices from capped binary relation sets under personalized user-level local differential privacy and Byzantine response replacement. A cardinality-calibrated joint personalized relation sketch (JPRS) releases one bit per user. The decoder combines public channel-amplitude weighting with exact sparse feasible projection. We distinguish trimmed honest information, adversarial leverage, and a response-contamination indistinguishability radius. An explicit capped binary block construction yields a sampling lower bound with an explicit l…
- OpenAlex · Published · First archived
Benchmarking Generative AI Models for Cybersecurity Vulnerability Identification Using 50 Curated Vulnerability Cases: An Exploratory Single-Pass Comparative Study of ChatGPT, Gemini, and Claude ↗
LLMs are being rapidly examined for cybersecurity applications, including identifying software vulnerabilities, conducting secure code reviews, and performing security analysis. Despite recent developments in Generative Artificial Intelligence (GenAI), there is a lack of empirical evidence on the relative performance of state-of-the-art commercial LLMs on discovering and categorizing software vulnerabilities. In this paper, we benchmarked the cybersecurity vulnerability-identification capabilities of ChatGPT, Gemini, and Claude on a dataset of fifty curated vulnerability cases from various Com…
- OpenAlex · Published · First archived
Malware Detection by Machine Learning based on the LightGBM Model ↗
Wannacry has grown to be considered one of the biggest threats affecting computer security over the past ten years, which has prompted continuous research on detection and mitigation techniques. The EMBER 2018 dataset, which contains sample WPE files categorized as harmless and harmful, is used to synthesize research findings and data in order to build an efficient machine learning model for malware identification. The pre-processed data is sent into a deep learning system for training. The machine learning model was built using dense and conventional dropout layers to reduce constraints on re…
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
How to assess evidence about Security
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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