CVE-2025-68982

Missing Authorization vulnerability in designthemes DesignThemes LMS Addon designthemes-lms-addon allows Exploiting Incorrectly Configured Access Control Security Levels.This issue affects DesignThemes LMS Addon: from n/a through = 2.6...

VULNEXPO

🔥 VULNEXPO — Vulnerability Detection & Exploitation Framework...

Towards Eco Friendly Cybersecurity: Machine Learning Based Anomaly Detection with Carbon and Energy Metrics

The rising energy footprint of artificial intelligence has become a measurable component of US data center emissions, yet cybersecurity research seldom considers its environmental cost. This study introduces an eco aware anomaly detection framework that unifies machine learning based network...

EUVD-2025-205753

Missing Authorization vulnerability in designthemes DesignThemes LMS Addon designthemes-lms-addon allows Exploiting Incorrectly Configured Access Control Security Levels.This issue affects DesignThemes LMS Addon: from n/a through = 2.6...

PT-2025-53872

Name of the Vulnerable Software and Affected Versions DesignThemes LMS Addon versions prior to and including 2.6 Description An authorization issue exists in the DesignThemes LMS Addon due to incorrectly configured access control security levels. This allows for potential exploitation of the...

FedLiTeCAN : A Federated Lightweight Transformer for Fast and Robust CAN Bus Intrusion Detection

This work implements a lightweight Transformer model for IDS in the domain of Connected and Autonomous Vehicles...

Quantum Machine Learning Approaches for Coordinated Stealth Attack Detection in Distributed Generation Systems

Coordinated stealth attacks are a serious cybersecurity threat to distributed generation systems because they modify control and measurement signals while remaining close to normal behavior, making them difficult to detect using standard intrusion detection methods. This study investigates quantu...

cyber-attack-detection-main

🔥 Smart Firewall with Machine Learning WAF + ML Đồ án d...

Agentic AI for Autonomous Defense in Software Supply Chain Security: Beyond Provenance to Vulnerability Mitigation

The software supply chain attacks are becoming more and more focused on trusted development and delivery procedures, so the conventional post-build integrity mechanisms cannot be used anymore. The available frameworks like SLSA, SBOM and in toto are majorly used to offer provenance and traceabili...

Application-Specific Power Side-Channel Attacks and Countermeasures: A Survey

Side-channel attacks try to extract secret information from a system by analyzing different side-channel signatures, such as power consumption, electromagnetic emanation, thermal dissipation, acoustics, time, etc. Power-based side-channel attack is one of the most prominent side-channel attacks i...

Zero-Trust Agentic Federated Learning for Secure IIoT Defense Systems

Recent attacks on critical infrastructure, including the 2021 Oldsmar water treatment breach and 2023 Danish energy sector compromises, highlight urgent security gaps in Industrial IoT IIoT deployments. While Federated Learning FL enables privacy-preserving collaborative intrusion detection,...

MeLeMaD: Adaptive Malware Detection Via Chunk-Wise Feature Selection and Meta-Learning

Confronting the substantial challenges of malware detection in cybersecurity necessitates solutions that are both robust and adaptable to the ever-evolving threat environment. The paper introduces Meta Learning Malware Detection MeLeMaD, a novel framework leveraging the adaptability and...

binary-exploitation-learning

No d...

Machine Learning Power Side-Channel Attack on SNOW-V

This paper demonstrates a power analysis-based Side-Channel Analysis SCA attack on the SNOW-V encryption algorithm, which is a 5G mobile communication security standard candidate. Implemented on an STM32 microcontroller, power traces captured with a ChipWhisperer board were analyzed, with Test...

Evasion-Resilient Detection of DNS-Over-HTTPS Data Exfiltration: A Practical Evaluation and Toolkit

The purpose of this project is to assess how well defenders can detect DNS-over-HTTPS DoH file exfiltration, and which evasion strategies can be used by attackers. While providing a reproducible toolkit to generate, intercept and analyze DoH exfiltration, and comparing Machine Learning vs...

How an LMS Cloud Model Supports Scalable Learning

There's a new era for training and development programs, making the LMS Learning Management System cloud model the…...

Elevating Intrusion Detection and Security Fortification in Intelligent Networks through Cutting-Edge Machine Learning Paradigms

The proliferation of IoT devices and their reliance on Wi-Fi networks have introduced significant security vulnerabilities, particularly the KRACK and Kr00k attacks, which exploit weaknesses in WPA2 encryption to intercept and manipulate sensitive data. Traditional IDS using classifiers face...

Enhancing Decision-Making in Windows PE Malware Classification during Dataset Shifts with Uncertainty Estimation

Artificial intelligence techniques have achieved strong performance in classifying Windows Portable Executable PE malware, but their reliability often degrades under dataset shifts, leading to misclassifications with severe security consequences. To address this, we enhance an existing LightGBM...

CVE-2025-67848

A flaw was found in Moodle. This authentication bypass vulnerability allows suspended users to authenticate through the Learning Tools Interoperability LTI Provider. The issue arises from the LTI authentication handlers failing to enforce the user's suspension status, enabling unauthorized access...

MAD-OOD: A Deep Learning Cluster-Driven Framework for an Out-Of-Distribution Malware Detection and Classification

Out of distribution OOD detection remains a critical challenge in malware classification due to the substantial intra family variability introduced by polymorphic and metamorphic malware variants. Most existing deep learning based malware detectors rely on closed world assumptions and fail to...