FIRCE: A Framework for Intrusion Response and Conformal Evaluation

Machine learning-based intrusion detection systems deployed in real-world environments frequently suffer from model degradation due to concept drift, where changes in traffic patterns invalidate training assumptions. To address this, we present FIRCE, a Framework for Intrusion Response and...

Detecting Concept Drift in Evolving Malware Families Using Rule-Based Classifier Representations

This work proposes a structural approach to concept drift detection in malware classification using decision tree rulesets. Classifiers are trained across temporal windows on the EMBER2024 dataset, and drift is quantified by comparing extracted rule representations using feature importance,...

IaC Inventory: A Unified View Across Code, Deployments, and Cloud

As AI applications introduce a new class of infrastructure resources, visibility into what your IaC creates, where it runs, and whether it has drifted has never been more critical...

Terraform Tutorial: Drift Detection Strategies

A fundamental challenge of architecture built using tools like Terraform is configuration drift. Check out these actionable strategies and steps you can take to detect and mitigate Terraform drift and manage any drift issues you might face...

Detect Container Drift in Your Kubernetes Deployments

Discover how to maintain compliance and secure your Kubernetes containers with automated security policies and scanning...