secops-ai-threat-analyzer

🛡️ SecOpsAI: Threat Analysis & Adaptive Security Engine An e...

Risk Models As Mediating Artifacts: A Postphenomenological Analysis of the CIIM Framework in Cybersecurity Practice

This article applies postphenomenological theory to the field of cybersecurity risk management, arguing that formal risk models function as mediating artifacts that shape how security practitioners or analysts perceive, interpret, and act on threats. Based on Don Ihde's taxonomy on human-technolo...

PT-2026-34832

Critical vulnerability in Anthropic Mythos and reported NSA adoption CVE-2026-21841 https://t.co/ZwHNBc0RF8 machinelearning ai...

MLDAS: Machine Learning Dynamic Algorithm Selection for Software-Defined Networking Security

Network security is a critical concern in the digital landscape of today, with users demanding secure browsing experiences and protection of their personal data. This study explores the dynamic integration of Machine Learning ML algorithms with Software-Defined Networking SDN controllers to enhan...

A Synthetic Conversational Smishing Dataset for Social Engineering Detection

Smishing SMS phishing has become a serious cybersecurity threat, especially for elderly and cyber-unaware individuals, causing financial loss and undermining user trust. Although prior work has focused on detecting smishing at the level of individual messages, real-world attackers often rely on...

Machine Learning-Based Detection of MCP Attacks

The Model Context Protocol MCP is a new and emerging technology that extends the functionality of large language models, improving workflows but also exposing users to a new attack surface. Several studies have highlighted related security flaws, but MCP attack detection remains underexplored. To...

CVE-2026-5194

A flaw was found in wolfSSL. Missing hash/digest size and Object Identifier OID checks allow the acceptance of smaller, less secure digests during the verification of Elliptic Curve Digital Signature Algorithm ECDSA certificates. This could enable a remote attacker, with knowledge of the public...

CVE-2026-5194

Missing hash/digest size and OID checks allow digests smaller than allowed when verifying ECDSA certificates, or smaller than is appropriate for the relevant key type, to be accepted by signature verification functions. This could lead to reduced security of ECDSA certificate-based authentication...

The agentic SOC—Rethinking SecOps for the next decade

Every major shift in cyberattacker behavior over the past decade has followed a meaningful shift in how defenders operate. When security operation centers SOCs deployed endpoint detection and response EDR—and later extended detection and response XDR—security teams raised the bar, pushing...

zantetsu-trainer is unmaintained

The zantetsu-trainer crate is no longer maintained. The ML training infrastructure it contained was removed as part of the zantetsu 0.2 release, which replaced the neural parser with a pure heuristic engine. A tombstone version 0.2.0 has been published and 0.1.4 has been yanked. There is no...

Towards Resilient Intrusion Detection in CubeSats: Challenges, TinyML Solutions, and Future Directions

CubeSats have revolutionized access to space by providing affordable and accessible platforms for research and education. However, their reliance on Commercial Off-The-Shelf COTS components and open-source software has introduced significant cybersecurity vulnerabilities. Ensuring the cybersecuri...

chromium -- security fixes

Chrome Releases reports: This update includes multiple security fixes: Critical: CVE-2026-5858: Heap buffer overflow in WebML. CVE-2026-5859: Integer overflow in WebML. High: CVE-2026-5860: Use after free in WebRTC. CVE-2026-5861: Use after free in V8. CVE-2026-5862: Inappropriate implementation ...

Improving ML Attacks on LWE with Data Repetition and Stepwise Regression

The Learning with Errors LWE problem is a hard math problem in lattice-based cryptography. In the simplest case of binary secrets, it is the subset sum problem, with error. Effective ML attacks on LWE were demonstrated in the case of binary, ternary, and small secrets, succeeding on fairly sparse...

Explainable PQC: A Layered Interpretive Framework for Post-Quantum Cryptographic Security Assumptions

This paper studies how post-quantum cryptographic PQC security assumptions can be represented and communicated through a structured, layered framework that is useful for technical interpretation but does not replace formal cryptographic proofs. We propose "Explainable PQC,'' an interdisciplinary...

ML Defender (ARGus NDR): An Open-Source Embedded ML NIDS for Botnet and Anomalous Traffic Detection in Resource-Constrained Organizations

Ransomware and DDoS attacks disproportionately impact hospitals, schools, and small organizations that cannot afford enterprise security solutions. We present ML Defender aRGus NDR, an open-source network intrusion detection system built in C++20, deployable on commodity hardware at approximately...

CVE-2026-34445

Open Neural Network Exchange ONNX is an open standard for machine learning interoperability. Prior to version 1.21.0, the ExternalDataInfo class in ONNX was using Python’s setattr function to load metadata like file paths or data lengths directly from an ONNX model file. It didn’t check if the...

Quantum Bit Error Rate Analysis in BB84 Quantum Key Distribution: Measurement, Statistical Estimation, and Eavesdropping Detection

Quantum Key Distribution QKD provides information-theoretic security by exploiting the principles of quantum mechanics. Among QKD protocols, the BB84 scheme remains the most widely adopted for both theoretical research and practical implementation. A critical parameter determining the reliability...

Context-Aware Phishing Email Detection Using Machine Learning and NLP

Phishing attacks remain among the most prevalent cybersecurity threats, causing significant financial losses for individuals and organizations worldwide. This paper presents a machine learning-based phishing email detection system that analyzes email body content using natural language processing...

CVE-2026-3962

A vulnerability was identified in Jcharis Machine-Learning-Web-Apps up to a6996b634d98ccec4701ac8934016e8175b60eb5. The impacted element is the function rendertemplate of the file Machine-Learning-Web-Apps-master/Build-n-Deploy-Flask-App-with-Waypoint/app/app.py of the component Jinja2 Template...

Machine Learning Operations: Yesterday, Today, and Tomorrow

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