• Author(s): Tal Reiss, George Kour, Naama Zwerdling, Ateret Anaby-Tavor, Yedid Hoshen

This paper introduces a novel approach to cold-start anomaly detection, addressing the challenge of identifying anomalies in scenarios where historical data is scarce or unavailable. Traditional anomaly detection methods often rely on extensive historical data to model normal behavior, making them ineffective in cold-start situations. The proposed method, termed “From Zero to Hero,” leverages advanced machine learning techniques to detect anomalies without the need for prior data.

The approach utilizes a combination of unsupervised learning and transfer learning to build a robust anomaly detection model. Initially, the model is trained on a large, diverse dataset to learn general patterns of normal behavior. This pre-trained model is then fine-tuned using a small amount of data from the target domain, allowing it to adapt to the specific characteristics of the new environment. The fine-tuning process is designed to be efficient, requiring minimal computational resources and time.

Extensive experiments are conducted to evaluate the performance of the proposed method. The results demonstrate that “From Zero to Hero” significantly outperforms traditional anomaly detection techniques in cold-start scenarios. The method achieves high detection accuracy and low false-positive rates, even with limited target domain data. Additionally, the approach shows robustness across various application domains, including network security, industrial monitoring, and healthcare.

The paper also explores the potential applications of the proposed method in real-world settings. By providing an effective solution for cold-start anomaly detection, “From Zero to Hero” enables organizations to quickly identify and respond to anomalies, enhancing their ability to maintain operational efficiency and security. The authors discuss the implications of their findings and suggest directions for future research to further improve the capabilities of anomaly detection systems. “From Zero to Hero” represents a significant advancement in the field of anomaly detection, offering a practical and efficient solution for cold-start scenarios. The proposed method addresses the limitations of traditional techniques and opens up new possibilities for detecting anomalies in environments with limited historical data.