Concept Drift Detection and Adaptive Retraining of Malware Classification Models

Concept drift refers to changes over time in the statistical properties of data, as compared to the data that was used to train a learning model. Machine learning models for malware detection or classification are particularly susceptible to performance degradation caused by concept drift, as...

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...

IoT-Based Android Malware Detection Using Graph Neural Network with Adversarial Defense

Since the Internet of Things IoT is widely adopted using Android applications, detecting malicious Android apps is essential. In recent years, Android graph-based deep learning research has proposed many approaches to extract relationships from applications as graphs to generate graph embeddings...