Traffic flow prediction is vital in urban traffic management, planning, and development. With the continuous advancement of urbanization, there is an increasing demand for traffic flow prediction models to achieve higher accuracy and long-range forecasting capabilities. Against this backdrop, traditional methods that rely on local feature extraction and static spatial graph construction often fall short of expectations. This highlights the urgent need for advanced approaches to dynamically model spatio-temporal features while capturing global dependencies, effectively meeting the demands of complex traffic flow prediction tasks. To achieve this, we propose the Transformer-Enhanced Adaptive Graph Convolutional Network (T-AGCN), a novel model designed to capture global temporal relationships and dynamically extract rich spatial information. T-AGCN incorporates an Adaptive Graph Learner module to model dynamic relationships among traffic nodes and a Transformer-Based Spatio-Temporal graph convolutional module to capture long-range temporal dependencies in historical traffic data effectively. These innovations enable T-AGCN to jointly learn dynamic spatial interactions and complex temporal patterns, offering a comprehensive representation of traffic network dynamics. We evaluate T-AGCN on three real-world datasets, PeMSD7(M), PeMS08, and METR-LA. The experimental results demonstrate that T-AGCN, inspired by the baseline model Spatial-Temporal Graph Convolutional Network (STGCN), significantly enhances its design. Moreover, T-AGCN consistently outperforms state-of-the-art models, including the Transformer-Based Interactive Temporal and Adaptive Network (TITAN) and the Spatial-Temporal Decoupled Masked Autoencoder (STD-MAE). The implementation is available on GitHub at https://github.com/time1722/T-AGCN .

Abstract Insider threats pose a critical risk to organisations, impacting their data, processes, resources, and overall security. Such significant risks arise from individuals with authorised access and familiarity with internal systems, emphasising the potential for insider threats to compromise the integrity of organisations. Previous research has addressed the challenge by pinpointing malicious actions that have already occurred but provided limited assistance in preventing those risks. In this research, we introduce a novel approach based on bidirectional long short-term memory (BiLSTM) networks that effectively captures and analyses the patterns of individual actions and their sequential dependencies. The focus is on predicting whether an individual would be a malicious insider in a future day based on their daily behavioural records over the previous several days. We analyse the performance of the four supervised learning algorithms on manual features, sequential features, and the ground truth of the day with different combinations. In addition, we investigate the performance of different RNN models, such as RNN, LSTM, and BiLSTM, in incorporating these features. Moreover, we explore the performance of different predictive lengths on the ground truth of the day and different embedded lengths for the sequential features. All the experiments are conducted on the CERT r4.2 dataset. Experiment results show that BiLSTM has the highest performance in combining these features.

Software vulnerabilities, also known as flaws, bugs or weaknesses, are common in modern information systems, putting critical data of organizations and individuals at cyber risk. Due to the scarcity of resources, initial risk assessment is becoming a necessary step to prioritize vulnerabilities and make better decisions on remediation, mitigation, and patching. Datasets containing historical vulnerability information are crucial digital assets to enable AI-based risk assessments. However, existing datasets focus on collecting information on individual vulnerabilities while simply storing them in relational databases, disregarding their structural connections. This article constructs a compact vulnerability knowledge graph, VulKG, containing over 276 K nodes and 1 M relationships to represent the connections between vulnerabilities, exploits, affected products, vendors, referred domain names, and more. We provide a detailed analysis of VulKG modeling and construction, demonstrating VulKG-based query and reasoning, and providing a use case of applying VulKG to a vulnerability risk assessment task, i.e., co-exploitation behavior discovery. Experimental results demonstrate the value of graph connections in vulnerability risk assessment tasks. VulKG offers exciting opportunities for more novel and significant research in areas related to vulnerability risk assessment. The data and codes of this article are available at https://github.com/happyResearcher/VulKG.git .