Efficient online liveness fault detection is crucial to cloud systems. Most current online liveness fault detection techniques, such as system layer heartbeating, use a single unreliable detector to detect cloud system liveness. A single unreliable detector requires a certain amount of time to detect faults to avoid misjudgment, regardless of the type of fault detected. However, many faults can be detected by other detectors more quickly. Therefore, this article proposes an efficient online liveness fault detection mechanism for cloud systems that integrates existing detectors to quickly detect faults. We compared the fault detection efficiency of the proposed mechanism with those of counterpart mechanisms. According to the results, relative to system layer heartbeating without any auxiliary detection mechanism, our proposed mechanism had a 70.3 percent shorter fault detection time.

The web shell attacks (WebShells) have long been a source of persistent annoyance for administrators. They have become a major security concern in cloud computing environments since the scalability and distributed nature of the cloud services could intensify the potential risks and impacts of such attacks. In response, researchers have proposed numerous strategies to shield assets from WebShell intrusions. Consequently, this study proposes a method that utilizes the BPE (Byte Pair Encoding) for tokenization, the CodeBERT for extracting the word embedding vector of a given source code piece, and a deep model (GRU or Bidirectional GRU) for determining whether the code contains a WebShell. This architecture is designed to detect the presence of WebShells in PHP code through analysis of the source code. Our experimental results indicate that, the proposed method with GRU achieves the best performance, with an accuracy of 99.72%, a precision of 99.36%, and an F1-score of 99.36%. Furthermore, it outperforms the methods proposed in the prior related studies as presented in the paper.

Modern computing platforms usually use multiple sensors to report system information. In order to achieve high availability (HA) for the platform, the sensors can be used to efficiently detect system faults that make a cloud service not live. However, a sensor may fail and disable HA protection. In this case, human intervention is needed, either to change the original fault model or to fix the sensor fault. Therefore, this study proposes an HA mechanism that can continuously provide HA to a cloud system based on dynamic fault model reconstruction. We have implemented the proposed HA mechanism on a four-layer OpenStack cloud system and tested the performance of the proposed mechanism for all possible sets of sensor faults. For each fault model, we inject possible system faults and measure the average fault detection time. The experimental result shows that the proposed mechanism can accurately detect and recover an injected system fault with disabled sensors. In addition, the system fault detection time increases as the number of sensor faults increases, until the HA mechanism is degraded to a one-system-fault model, which is the worst case as the system layer heartbeating.