Reversing the Lens: Using Explainable AI to Understand Human Expertise

The introduction of ProtoPFormer, a novel approach that integrates prototypical part networks with vision transformers, aims to enhance interpretable image recognition by addressing the distraction problem where prototypes are overly activated by background elements. This development seeks to improve the focus on relevant features in images, thereby enhancing the model's interpretability.

A new study proposes a hybrid transfer framework utilizing contrastive transfer learning with Siamese networks to enhance the detection of Advanced Persistent Threats (APTs). This approach addresses challenges such as class imbalance and feature drift, which have hindered traditional machine learning methods in cybersecurity. The framework integrates Explainable AI (XAI) to improve feature selection and anomaly detection across different attack domains.

A new anomaly detection method called GiBy has been proposed for Industrial Control Systems (ICS), focusing on the continuous monitoring of cyber-physical interactions to ensure safe automation and operation. This method emphasizes accurate linearization of non-linear sensor-actuator relationships, which is crucial for timely detection of anomalies such as attacks and faults.