Log anomaly detection is crucial for computer systems. By analyzing and processing the logs generated by a system, abnormal events or potential problems in the system can be identified, which is helpful for its stability and reliability. At present, due to the expansion of the scale and complexity of software systems, the amount of log data grows enormously, and traditional detection methods have been unable to detect system anomalies in time. Therefore, it is important to design log anomaly detection methods with high accuracy and strong generalization. In this paper, we propose the log anomaly detection method LogADSBERT, which is based on Sentence-BERT. This method adopts the Sentence-BERT model to extract the semantic behavior characteristics of log events and implements anomaly detection through the bidirectional recurrent neural network, Bi-LSTM. Experiments on the open log data set show that the accuracy of LogADSBERT is better than that of the existing log anomaly detection methods. Moreover, LogADSBERT is robust even under the scenario of new log event injections.

Unknown tag identification plays a pivotal role in radio frequency identification (RFID) systems, but it has not been fully investigated. This paper proposes a filter-based and parallel unknown tag identification protocol (FPUI) for open RFID systems. The FPUI adopts an RSQF-based fingerprint filter to reconcile the collision slots and discriminate the known tags from unknown tags. Meanwhile, it collects the IDs of unknown tags in parallel. FPUI achieves high performance through the following three steps: (1) adopting the RSQF-based filter to build an indicator vector, thus improving the space efficiency; (2) building a fingerprint filter to discriminate known tags from unknown tags, thus reducing the false positive rate; (3) employing a parallel identification scheme to collect the IDs of unknown tags, thus improving identification efficiency. The identification time of our protocol was minimized by conducting a theoretical analysis of the relevant parameters. Furthermore, the performance of our protocol was evaluated by conducting a wide range of simulation experiments. The theoretical analysis and simulation results indicated that our protocol significantly outperformed the current advanced protocols.

Compressed sensing (CS) is an innovative signal acquisition and reconstruction technology that has broken through the limit of the Nyquist sampling theory. It is widely employed to optimize the measurement processes in various applications. One of the core challenges of CS is the construction of a measurement matrix. However, traditional random measurement matrices are often impractical. Additionally, many existing deterministic binary measurement matrices fail to provide the required flexibility for practical applications. In this study, inspired by the observation that pseudo-random sequences share similar properties with random sequences, we constructed a deterministic sparse measurement matrix with a flexible measurement number based on an pseudo-random sequence—the Legendre sequence. Empirical analysis of the phase transition and an assessment of the practical features of the proposed measurement matrix were conducted. We validated the effectiveness of the proposed measurement matrix on randomly synthesized signals and images. The results of our simulations reveal that our proposed measurement matrix performs better than several other measurement matrices, particularly in terms of accuracy and efficiency.