guardd

guardd Machine learning driven behavioral anomaly detection for Linux using eBPF + Isolation Forest Guardd collects low-level system events process execution, network activity, aggregates them into time-windowed feature vectors, and detects anomalous behavior using a machine learning model. Guard...

batea

Batea Una batea es una bandeja grande y poco profunda de madera o hierro utilizada tradicionalmente por los buscadores de oro para lavar arena y grava y recuperar pepitas de oro. Batea es un marco de clasificación de dispositivos de red basado en contexto que utiliza la familia de algoritmos de...

ThreatDetect

ThreatDetect ThreatDetect is a Streamlit-based insider threat detection prototype that analyzes employee activity data and flags potentially risky behavior. It uses a trained XGBoost classifier together with an Isolation Forest anomaly detector to produce organisation-level risk summaries and...

AegisUI: Behavioral Anomaly Detection for Structured User Interface Protocols in AI Agent Systems

AI agents that build user interfaces on the fly assembling buttons, forms, and data displays from structured protocol payloads are becoming common in production systems. The trouble is that a payload can pass every schema check and still trick a user: a button might say "View invoice" while its...

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...

Unsupervised Anomaly Detection for Smart IoT Devices: Performance and Resource Comparison

The rapid expansion of Internet of Things IoT deployments across diverse sectors has significantly enhanced operational efficiency, yet concurrently elevated cybersecurity vulnerabilities due to increased exposure to cyber threats. Given the limitations of traditional signature-based Anomaly...

NegBLEURT Forest: Leveraging Inconsistencies for Detecting Jailbreak Attacks

Jailbreak attacks designed to bypass safety mechanisms pose a serious threat by prompting LLMs to generate harmful or inappropriate content, despite alignment with ethical guidelines. Crafting universal filtering rules remains difficult due to their inherent dependence on specific contexts. To...

Semi-Supervised Supply Chain Fraud Detection with Unsupervised Pre-Filtering

Detecting fraud in modern supply chains is a growing challenge, driven by the complexity of global networks and the scarcity of labeled data. Traditional detection methods often struggle with class imbalance and limited supervision, reducing their effectiveness in real-world applications. This...

QUIC-Exfil: Exploiting QUIC'S Server Preferred Address Feature to Perform Data Exfiltration Attacks

The QUIC protocol is now widely adopted by major tech companies and accounts for a significant fraction of today's Internet traffic. QUIC's multiplexing capabilities, encrypted headers, dynamic IP address changes, and encrypted parameter negotiations make the protocol not only more efficient,...

Zero-Day Botnet Attack Detection in IoV: a Modular Approach Using Isolation Forests and Particle Swarm Optimization

The Internet of Vehicles IoV is transforming transportation by enhancing connectivity and enabling autonomous driving. However, this increased interconnectivity introduces new security vulnerabilities. Bot malware and cyberattacks pose significant risks to Connected and Autonomous Vehicles CAVs, ...