How to effectively diagnose and mitigate database performance anomalies remains a significant concern for modern database systems. Manually identifying the root causes of the anomalies is a labor-intensive process and significantly relies on professional experience. Meanwhile, existing work on automatic database diagnosis mainly focuses on detecting anomalous performance metrics or system log. These solutions lack the power to pinpoint detailed issues such as bad queries or problematic operators, which are indispensable for most database troubleshooting processes. In this paper, we propose OpDiag, a diagnosis framework that attributes database performance anomalies to query operators. In this framework, we first construct models offline to represent the relationship between query operators, performance metrics, and anomalies. These models can capture query plan features and support ad-hoc queries and schemas. Then, through feature attribution on these models during online diagnosis, OpDiag can effectively identify critical anomalous metrics and further trace back to suspicious queries and operators. This can provide concrete guidance for subsequent steps in anomaly mitigation. We applied OpDiag to both synthetic benchmark and real industry cases from ZTE Corporation. Empirical studies prove that OpDiag can accurately localize anomalous queries and operators, thus reducing human efforts in diagnosing and mitigating database performance anomalies.

Data leakage is one of the most severe security threats that can compromise sensitive data through breaches or unauthorized access. Existing techniques usually adopt encryption or access control protection methods, but inevitably affects the data usability and introduce significant overhead. In this paper, we propose a novel technique for data leakage prevention in collaborative systems by dynamically broadening the deceptive attack surface. Our proposed technique offers an adaptive deception strategy that leverages historical user behaviors and current operations to generate deceptive data, and we developed an amplifying-based calculation method to enhance the accuracy of user trust degree evaluation. Furthermore, we introduce three deceptive indicators to evaluate our technique. Experimental results show that our technique can effectively prevent data leakage while preserving data usability and imposing minimal overhead to the system.

As an extension of cloud computing, edge computing provides storage and computing services at the network edge. Due to resource limitation of edge nodes, collaborative data offloading is usually utilized to offload overloaded data in the current node to adjacent nodes to ensure quality of service. In this case, the reputation mechanism is a crucial tool to select a reliable one from adjacent nodes to provide data storage service. However, existing works usually adopt the single source-based evaluation method, which is vulnerable to malicious manipulation and lacks objectivity. Meanwhile, the reputation of malicious nodes can be rapidly recovered, leading to a reduction in evaluation accuracy. To address these issues, we propose a secure blockchain-based edge nodes reputation scheme, combined with subjective evaluation result and objective monitoring values to provide a credible reputation. Specifically, to increase the malicious attack cost and limit reputation recovery speed, we design an Amplifying-based Reputation Calculation (ARC) mechanism to amplify the impact of malicious behaviors on reputation. In addition, we propose a Dynamic Miner Selection (DMS) algorithm to resist bookkeeping right attack from malicious infrastructure providers. Detailed analysis proves the security of our scheme and experiment results testify the effectiveness and efficiency.

Mobile Edge Computing (MEC) has emerged as a distributed computing paradigm offering low-latency services to users. However, the distributed and intricate deployment inevitably makes it challenge to ensure the reliability of MEC services. Anomaly detection using the streaming data of MEC services is an essential way to address the challenge. In this paper, to improve the detection accuracy and efficiency, we propose an MTDF-Detection framework for MEC services, jointly extracting Multi-Timescale and Dimensional Feature (MTDF), including the long-term trend, periodic, short-term fluctuation and auxiliary parameters (e.g., system maintenance and offloading task). In this framework, to reduce the computing costs, we adopt a Bidirectional Simple Recurrent Unit (Bi-SRU) to obtain contextual feature; and we design an adaptive m-Sigma algorithm to determine the dynamic threshold with real-time streaming data. Extensive experiments are conducted on a real-world dataset, and the results demonstrate that the MTDF-Detection framework outperforms the state-of-the-art schemes in terms of accuracy and efficiency.

Telemetry data is an important resource to detect anomaly of satellites in orbit. In recent years, the telemetry data-driven satellite anomaly detection has drawn great attention from academia and industries. However, in prior arts, the time-or frequency-domain data feature is usually separately utilized; and, to reduce the compute complexity, a small portion of data dimensions are usually selected from the telemetry data manually, leading to reduction in detection accuracy. Motivated by this, we propose a novel satellite telemetry data anomaly detection approach using high-dimension telemetry data, and the Multiple Factors and Co-Attention based LSTM (MFCA-LSTM) model. Specifically, to achieve an accurate prediction of telemetry sequence, we first propose the MFCA-LSTM model, jointly considering the time- and frequency-domain data feature, and the auxiliary information, e.g., telecommand and mission planning. Then, by using the MFCA-LSTM model, we construct a two-level anomaly detection framework to efficiently detect full-dimension data; and, to improve the accuracy rate and reduce the false alarm rate, we further propose an adaptive Tukey test algorithm to determine the dynamic thresholds. Finally, we perform extensive experiments based on two real-world datasets, and the results testify the effectiveness of our proposed framework.

Summary: Large-scale phosphoproteomics studies have improved our understanding of dynamic cellular signaling, but the downstream analysis of phosphoproteomics data is still a bottleneck. We develop DynaPho, a useful web-based tool providing comprehensive and in-depth analyses of time-course phosphoproteomics data, making analysis intuitive and accessible to non-bioinformatics experts. The tool currently implements five analytic modules, which reveal the transition of biological pathways, kinase activity, dynamics of interaction networks and the predicted kinase-substrate associations. These features can assist users in translating their larger-scale time-course phosphoproteomics data into valuable biological discoveries. Availability and implementation: DynaPho is freely available at http://dynapho.jhlab.tw/ . Contact: [email protected] or [email protected] . Supplementary information: Supplementary data are available at Bioinformatics online.

This paper proposes a vehicle reidentification (VRI) system with self-adaptive time windows to estimate the mean travel time for each time period on the freeway under traffic demand and supply uncertainty. To capture the traffic dynamics in real-time application, interperiod adjusting based on the exponential smoothing technique is introduced to define an appropriate time-window constraint for the VRI system. In addition, an intraperiod adjusting technique is also employed to handle the nonpredictable traffic congestion. To further reduce the negative effect caused by the mismatches, a postprocessing technique, including thresholding and stratified sampling, is performed on the travel time data derived from the VRI system. Several representative tests are carried out to evaluate the performance of the proposed VRI against potential changes in traffic conditions, e.g., recurrent traffic congestion, freeway bottlenecks, and traffic incidents. The results show that this method can perform well under traffic demand and supply uncertainty.