Comparative Evaluation of Static Embedding Models for HTTP Request Anomaly Detection

Web applications are increasingly targeted by cyberattacks that exploit HTTP requests to evade security mechanisms. Traditional web application firewalls WAFs rely on rule-based approaches that often exhibit high false positive rates and limited adaptability. Recent studies have explored machine...

Confidence-Guided Cross-Modal Knowledge Transfer for Multimodal Anomaly Detection in Microservice Systems

Accurate anomaly detection is essential for reliable and secure operations of microservice systems. While an increasing number of studies have shifted from unimodal modeling to multimodal interaction and fusion, effectively leveraging reliable cross-modal information remains challenging. The...

Self-Verifying Anomaly Detection Using Explainable AI for Cybersecurity of DER Networks

The rapid growth of Distributed Energy Resources DERs has significantly expanded the cyber attack surface of modern power grids. Furthermore, increasing sophistication in attack techniques demands anomaly detection systems ADS that are accurate, interpretable, and reliable to support DER...

Towards a Resilience-Theoretic Foundation for Adversarial Robustness in Industrial Control System Anomaly Detection

Anomaly-based intrusion detection systems in industrial control systems ICS and operational technology OT environments are increasingly required to meet formal resilience criteria: absorbed adversarial disturbances, graceful degradation under sustained attack, and certified system-level guarantee...

darts v0.47.0

Time Series Made Easy in Python Darts is a Python library for user-friendly forecasting and anomaly detection on time series. It contains a variety of models, from classics such as ARIMA to deep neural networks. The forecasting models can all be used in the same way, using fit and predict...

Skynet: Workflow-Level Anomaly Detection for Agentic AI Via Semantic and Structural Modeling

Agentic AI systems execute complex tasks through long-horizon workflows of planning, tool use, and multi-agent coordination. Task failures in these systems often originate from a single step, such as an injected prompt or a flawed plan, and are then amplified through downstream dependencies as th...

monitor v0.41.0

BetterDB Monitor The monitoring layer that Valkey deserves. BetterDB persists what Valkey throws away - slowlogs, command patterns, client activity, anomaly signals - so you can debug what happened at 3am, not just what's happening now. Built for Valkey 8.x with native support for COMMANDLOG,...

netsentryx — Updated!

NetSentryx PRO 🛡️ AI-Driven, High-Performance Real-Time Network Observability & Threat Detection Engine NetSentryx PRO is a next-generation, event-driven network monitoring and threat intelligence platform. Combining rule-based signatures with a PyTorch Deep Autoencoder, it provides sub-10ms packe...

HSMLog: Small Language Model-Assisted Hardware Security Module Log Anomaly Detection with Behavioral Analysis

Hardware Security Module HSM logs capture security-critical behavior, but anomalies emerge from relationships across event sequences, keys, object states, sessions, and temporal patterns rather than isolated events. Existing methods separate detection from HSM-specific evidence validation and...

X-WAD: EXplainable Web Anomaly Detection

The rapid growth of web-based services, particularly API-driven architectures, reflects an increasing reliance on distributed systems, exposing sensitive data to security risks and making the adoption of automated defensive mechanisms essential. In this context, where benign traffic predominates ...

When Relationships Break: Interpreting Network Traffic Anomalies Via Dependency Violations

Current research on security monitoring is increasingly focusing on machine-learning-based approaches, but caveats remain. In addition to huge computational overhead, one concern is the lack of insights into "why" alerts are raised. Existing interpretability approaches rely on feature attribution...

Cyber-Electromagnetic Anomaly Detection through Time-Series Analysis

Military operations benefit from the coordination between kinetic and non-kinetic domains. In particular, the coordination of cyber operations and electromagnetic warfare has become increasingly relevant for gaining operational advantage. This coordination is also relevant for Cyber Situational...

RAD: Rule-Augmented Relational Anomaly Detection

Anomaly detection is often applied to data stored in relational databases, yet most existing methods require flattening multiple tables into a single feature matrix. This flattening can obscure entity identity, schema structure, and multi-hop dependencies, limiting the detection of anomalies that...

The Surprising Effectiveness of LLMs in BGP Security: Mining an Unprecedented Amount of Incidents and Boosting Anomaly Detection

Border Gateway Protocol BGP security is critical to Internet infrastructure, yet progress in routing anomaly detection has been limited by the scarcity of publicly available incident datasets, which contain only 18 recorded cases. We observe that public operator mailing lists, e.g., NANOG and...

Robustness of Anomaly Detection Models for Industrial Control Systems under Training-Time Data Contamination

Machine-learning-based anomaly detection is increasingly used in industrial control systems ICS, yet most studies assume that detector training data is trustworthy. In practice, training data may be corrupted through compromised logs, labeling errors, manipulated historian records, or unsafe...

From Noise to Signal: Improving Security Log Anomaly Detection Using LLMs with Endpoint-Specific Logs

Existing approaches to anomalous behaviour log detection, such as Wazuh rely primarily on predefined detection rules, while statistical anomaly detection approaches such as OpenSearch identify deviations from previously observed behavioural patterns. Recent research has investigated LLMs for log...

Identifying Agentic Automation with Behavioral Telemetry

Learn how Akamai uses Masked Autoencoder Transformer models to detect sparse behavioral telemetry from autonomous AI browser agents, such as Comet...

BIT-ELK-2026-72671 Missing Authorization in Kibana Leading to Unauthorized Modification of Machine Learning Trained Model Space Assignments

A Kibana Machine Learning capability that removes a saved object from the current space accepts machine learning trained models as a target, but it verifies only the privileges that apply to anomaly detection jobs and data frame analytics jobs. A user whose role grants create anomaly detection jo...

CVE-2026-72671

A Kibana Machine Learning capability that removes a saved object from the current space accepts machine learning trained models as a target, but it verifies only the privileges that apply to anomaly detection jobs and data frame analytics jobs. A user whose role grants create anomaly detection jo...

EUVD-2026-58258

A Kibana Machine Learning capability that removes a saved object from the current space accepts machine learning trained models as a target, but it verifies only the privileges that apply to anomaly detection jobs and data frame analytics jobs. A user whose role grants create anomaly detection jo...