Anomaly detection in streaming data is a crucial task for many real-world applications, such as network security, fraud detection, and system monitoring. However, streaming data often exhibit concept drift, which means that the data distribution changes over time. This poses a significant challenge for anomaly detection algorithms, as they need to adapt to the evolving data to maintain high detection accuracy. Existing streaming anomaly detection algorithms lack a unified evaluation framework that can assess their performance and robustness under different types of concept drift and anomaly. In this paper, we conduct a systematic technical review of the state-of-the-art methods for anomaly detection in streaming data. We propose a new data generator, called SCAR (Streaming data generator with Customizable Anomalies and concept dRifts), that can synthesize streaming data based on synthetic and real-world datasets from different domains. Furthermore, we adapt four static anomaly detection models to the streaming setting using a generic reconstruction strategy as baselines, and then compare them systematically with 9 existing streaming anomaly detection algorithms on 76 synthesized datasets that exhibit various types of anomalies and concepts. The challenges and future research directions for anomaly detection in streaming data are also presented. All the codes and datasets are publicly available at https://github.com/yixiaoma666/ SCAR.

Zero Trust Architecture (ZTA) is a paradigm shift in how we protect data, stay connected, and access resources. ZTA is  non-perimeter based defense, which has been emerging as a promising revolution in the cybersecurity field.  It can be used to continuously maintain security by safeguarding against attacks both from inside and outside of the network system. However, automation and orchestration ZTA, an essential direction towards its seamless deployments over real-word networks, have been poorly understood in literature. In this paper, we first identify the bottlenecks, discuss the background of ZTA and compare it with traditional perimeter-based security architectures. More importantly, we present a comprehensive direction towards the automation and orchestration of ZTA  by employing AI techniques. In other words, the key potential and roles of celebrates AI techniques for the automation and orchestration of ZTA are demonstrated for further exploration. Overall, in this review paper, we develop a foundational view on the challenges and potential enablers for the automation and orchestration of ZTA.