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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 ↗659 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.
- OpenAlex · Published · First archived
Intelligent security policy generation algorithm based on graph neural network and reinforcement learning for SD-WAN ↗
With the evolution of enterprise networks to multi-site and cloud architectures, Software-Defined Wide Area Network (SD-WAN) is prone to the problems of insufficient global policy regulation and frequent policy adjustment under dynamic security threats, which in turn leads to increased delay, aggravated packet loss and unstable network operation. Existing research mostly focuses on traffic optimization or local security detection, lacking global topology security situation modeling for SD-WAN and intelligent policy generation mechanisms that balance security benefits and policy stability. To a…
- OpenAlex · Published · First archived
Cloud security frameworks: a comprehensive analysis of security controls, governance models, and implementation challenges in modern cloud infrastructures ↗
Abstract Cloud computing has become the preferred technological foundation for modern businesses and governments, offering users unparalleled flexibility, scalability, and cost savings. The complexity of securing distributed cloud environments, which are characterized by shared responsibility, multitenancy models, and expanded attack surfaces, has increased exponentially. This study evaluates and categorizes existing cloud security frameworks into two primary classifications: Industry-Wide Frameworks (vendor-neutral), comprising General Cybersecurity Frameworks (NIST CSF v2.0, ISO/IEC 27017:20…
- OpenAlex · Published · First archived
SecureFresh-WSN: An Energy-Aware and Trust-Based Clustering and Routing Protocol for Cyberattack-Resilient Wireless Sensor Networks ↗
Wireless sensor networks (WSNs) in critical environments need to conserve energy and deliver fresh information despite possible cyberattacks. Meeting these requirements remains a challenge. Classical protocols such as LEACH optimise energy but neglect security and freshness, while recent reinforcement-learning and fuzzy-trust methods address security at the expense of energy balance or data freshness. This paper proposes SecureFresh-WSN, a unified clustering and routing protocol that jointly balances the optimisation of energy consumption, cyberattack resilience, and Age-of-Information (AoI) f…
- OpenAlex · Published · First archived
The Dual Use of Generative Artificial Intelligence in Cybersecurity: A Survey from Deepfake Attacks to Intelligent Defense ↗
The rapid iteration of generative Artificial Intelligence (AI) is reshaping the cybersecurity offense-defense landscape. This paper systematically reviews research progress in this field from 2021 to 2026, focusing on the dual use of generative AI in cybersecurity. On the one hand, malicious actors exploit generative AI to create deepfake content, automate phishing emails, and conduct social engineering attacks, significantly improving the personalization and scalability of attacks. On the other hand, the same technological system demonstrates significant effectiveness in defense scenarios suc…
- OpenAlex · Published · First archived
Online privacy protection: How the varieties of democracy matter ↗
Abstract Digital technologies have enabled the gathering, storing, and processing of large amounts of data in increasing and unprecedented ways. This has triggered concerns regarding governments’ ability to harvest vast amounts of information on citizens and the consequences of this surveillance on democracy. The reliance of many democracies on digital (surveillance) technologies in response to the 2019 Coronavirus pandemic has amplified these concerns. While most studies focus on how privacy violations affect democracy, this study analyzes how variations in democracy affect the legal protecti…
- OpenAlex · Published · First archived
Mitochondrial Localization of Cx26 Promotes Colorectal Cancer Malignancy and Exposes a Fatal Vulnerability to Drug Targeting ↗
ABSTRACT The malignant progression of colorectal cancer (CRC) is closely linked to metastasis and therapy resistance, but underlying mechanisms remain unclear. Here, we identify mitochondrial translocation of connexin 26 (Cx26) as a hallmark of highly malignant metastatic CRC and reveal its critical role in promoting ER‐mitochondria crosstalk, calcium signaling, and epithelial‐mesenchymal transition (EMT). Mechanistically, we demonstrate that protein L‐isoaspartyl/D‐aspartyl O‐methyltransferase (PIMT) methylates Cx26 at asparagine 176, enhancing its mitochondrial localization and stability. Th…
- OpenAlex · Published · First archived
Hiding Identity, Preserving Respiration: Semantic-Decoupled Privacy Protection for mmWave Radar Sensing ↗
Millimeter-wave (mmWave) radar-based respiratory sensing has attracted increasing attention due to its contactless, unobtrusive nature and high sensitivity to micro-motions. However, mmWave radar signals inevitably encode identity-sensitive information beyond respiration, raising serious privacy concerns. Existing privacy protection strategies are often coarsegrained or indiscriminately applied, leading to substantial degradation in respiratory sensing performance. To address these issues, we propose P 2 CRM , an identity-preserving mmWave radar-based contactless respiratory sensing system tha…
- OpenAlex · Published · First archived
Deep Learning Approaches for Intrusion Detection System Using Multi-Model Architectures for Network Security ↗
It has become necessary to design efficient, robust, and effective Intrusion Detection Systems (IDS) amid the rising complexity of cyberattacks and the exponential growth in network traffic. In this work, we present an advanced IDS framework using comparative deep learning (in short, DL) models for precise classification and detection of all types of network intrusions. The proposed system consists of five DL models: Recurrent Neural Network (RNN), One-Dimensional Convolutional Neural Network (1DCNN), Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), and a Transformer-based model, wh…
- OpenAlex · Published · First archived
DG-VDT: Dynamic Graph-Guided Reinforcement Learning for Low-Latency Vulnerability Detection and Attack Traceability in Ethereum Smart Contracts ↗
Ethereum hosts more than 50 million deployed contracts, and cumulative losses from contract exploits exceed three billion US dollars, making automated detection and attacker attribution a practical priority. Existing tool-augmented language models and reward-driven reinforcement learning reason over transaction text or a single graph view; they infer maliciousness from superficial topology and degrade when applied to attacks whose traces lack it. This article presents DG-VDT, a reinforcement-learning framework in which tool-centric rewards are replaced by graph-structured feedback derived dire…
- OpenAlex · Published · First archived
Anomaly detection of internet of medical things cyberattacks using federated learning ↗
Abstract Devices connected to the Internet of Medical Things (IoMT) handle sensitive patient data under strict privacy and resource constraints. Centralized intrusion detection introduces privacy risks and communication bottlenecks, while existing Federated Learning (FL) solutions struggle with class imbalance and data heterogeneity. This paper proposes One-Shot Federated Anomaly Detection (OneShot-FedAD), which completes feature selection and model training in a single communication round, eliminating iterative weight exchange. It integrates Federated Differential Privacy Mutual Information (…
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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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