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Abstract The distributed architecture of cloud computing necessitates robust defense mechanisms to secure network-accessible resources against a diverse and dynamic threat landscape. A Network Intrusion Detection System (NIDS) is pivotal in this context, with its efficacy in cloud environments hinging on its adaptability to evolving threat vectors while mitigating false positives. In this paper, we present a novel NIDS algorithm, anchored in the Transformer model and finely tailored for cloud environments. Our algorithm melds the fundamental aspects of network intrusion detection with the sophisticated attention mechanism inherent to the Transformer model, facilitating a more insightful examination of the relationships between input features and diverse intrusion types, thereby bolstering detection accuracy. We provide a detailed design of our approach and have conducted a thorough comparative evaluation. Our experimental results demonstrate that the accuracy of our model is over 93%, which is comparable to that of the CNN-LSTM model, underscoring the effectiveness and viability of our Transformer-based intrusion detection algorithm in bolstering cloud security.

Abstract Cloud computing has gained popularity in recent years, but with its rise comes concerns about data security. Unauthorized access and attacks on cloud-based data, applications, and infrastructure are major challenges that must be addressed. While machine learning algorithms have improved intrusion detection systems in cloud data security, they often fail to consider the entire life cycle of file processing, making it difficult to detect certain issues, especially insider attacks. To address these limitations, this paper proposes a novel approach to analyzing data file processing in multi-cloud environments using process mining. By generating a complete file processing event log from a multi-cloud environment, the proposed approach enables detection from both control flow and performance perspectives, providing a deeper understanding of the underlying file processing in its full life cycle. Through our case study, we demonstrate the power and capabilities of process mining for file security detection and showcase its ability to provide further insights into file security in multi-cloud environments.

Abstract Spin light manipulation based on chiral metasurfaces is a striking hotspot that has intrigued huge attention. Circular dichroism, a unique phenomenon of chiral atoms/molecules, has been regarded as another auxiliary dimension for guiding electromagnetic waves, which has been explored in the field of artificial material sciences yet a challenging issue. Here, a generic strategy based on dynamic chiral meta-atom for revealing strong circular dichroism as well as applicable electromagnetic functionality is proposed in microwave regime. We demonstrate a dynamic metasurface that enables the fully independent holograms reconstruction for one circular polarization or the other at the active operating state. On the other hand, the electromagnetic scattering is realized for lowering observable backward reflection at the passive state. Numerical simulation and experimental verification are conducted to manifest the feasibility. It is expected that the proposed strategy can be applied to broaden the horizon for dynamic chiral meta-devices and may find applications in information encryption, anti-counterfeiting, and other dynamic systems.