Flight safety remains a central concern in civil aviation. Recently, increasing attention has been given to leveraging high-dimensional temporal flight data, typically collected by Quick Access Recorders (QAR), to predict safety events. However, most existing studies focus on individual events, overlooking the latent correlations among multiple events. For instance, during landing, a maneuver that reduces the risk of one event may inadvertently increase the risk of another. Predicting multiple events introduces three main challenges: 1)handling parameters recorded at inconsistent sampling rates, 2) learning task-specific parameter importance for interpretability, and 3) modeling complex temporal dependencies for accuracy. To address these, we propose MultiSafe, a deep multi-task learning model for predicting and interpreting multiple flight safety events. First, to process parameters with heterogeneous frequencies, we introduce a Multi-Scale Shared Encoder with adaptive convolutional kernels to unify representations across tasks. Next, a gating-based Parameter Selector learns task-specific parameter importance, enabling interpretable predictions. Finally, a Temporal Decoder with fine-grained attention captures intricate temporal dependencies among parameters. Experiments on a dataset of 37,518 A320 flight records demonstrate that MultiSafe outperforms state-of-the-art baselines in prediction accuracy, while its interpretability offers actionable insights to assist pilots in enhancing flight safety.

System intrusions, particularly Advanced Persistent Threats (APTs), pose significant threats to enterprises and organizations. Provenance graph-based attack detection and investigation methods are crucial for defending against these intrusions. To detect various attacks, security systems collect comprehensive operating system event data, resulting in massive provenance graphs that increase storage costs and complicate analysis and querying. Efficiently optimizing these provenance graphs has thus become a core issue. However, existing data reduction methods often mistakenly delete critical security information, significantly impacting attack detection and investigation. This paper introduces TeRed, a novel method for reducing provenance graphs based on normal behavior patterns. Our approach employs unit tests to learn the system’s normal behavior patterns, which are then used to streamline the provenance graph. Experiments on five datasets show that our method reduces the provenance graph while preserving all attack-related information. Importantly, it does not compromise attack detection and investigation, showcasing significant advantages over other data reduction techniques.

Hard landing, as one of the most frequent flight safety incidents during the landing stage, is highly concerned by the aviation industry. Recently, the popularization of Quick Access Recorder (QAR), a modern flight data recording system, has made it possible to collect large volume of flight parameters and incorporate state-of-the-art AI technologies to improve flight safety. However, due to the complex, multivariate, and highly specialized nature of QAR data, most existing studies either suffer from information loss caused by rough feature extraction methods, or rely solely on black-box models with no interpretations, making themselves difficult to achieve satisfactory performance in terms of prediction and explainability. To address this issue, we propose a novel attention-driven model named SDTAN (Scalable Deep Time-Aware Attention Network), which can accurately predict hard landing events and provide interpretable insights to help reveal the possible reasons leading to the events. Specifically, SDTAN fully captures information to learn the local representations of parameters, and leverages the time-interval attention mechanism to focus on the entire temporal pattern of flight over the relevant time intervals. It further re-encodes the representations of parameters in a global view and learns the global effect of parameters on the predicted output to uncover the ones which strongly indicate the flight safety status, enabling both high prediction accuracy and qualitative interpretability. We conduct experiments on real-world QAR datasets of 37,920 Airbus A320 flight samples. Experimental results demonstrate that SDTAN outperforms other state-of-the-art baselines and provides effective interpretability by visualizing the importance of parameters.

The traditional vector-based classifiers, such as support vector machine (SVM) and twin support vector machine (TSVM), cannot handle tensor data directly and may not utilize the data informations effectively. In this paper, we propose a novel classifier based on tensor data, called twin bounded support tensor machine (TBSTM) which is an extension of twin bounded support vector machine (TBSVM). Similar to TBSVM, TBSTM gets two hyperplanes and obtains the solution by solving two quadratic programming problems (QPPs). The computational complexity of each QPPs is smaller than that of support tensor machine (STM). TBSTM not only retains the advantage of TBSVM, but also has its unique superior characteristics: (1) it makes full use of the structure information of data; (2) it has acceptable or better classification accuracy compared to STM, TBSVM and SVM; (3) the computational cost is basically less than STM; (4) it can deal with large data that TBSVM is not easy to achieve, especially for small-sample-size (S3) problems; (5) it adopts alternating successive over relaxation iteration (ASOR) method to solve optimization problems which accelerates the pace of training. Finally, we demonstrate the effectiveness and superiority by the experiments based on vector and tensor data.