APT attacks have the characteristics of low frequency, stealth, and persistence. Achieving attack objectives and preventing trace-back often involve diverse tactics, various tools, and changing processes and patterns. Additionally, the goals of APT attacks are diverse. Apart from service disruptions or network outages, the main goals include remotely penetrating target hosts through the network to steal information, unauthorized encryption, and destructive wiping. Existing methods for characterizing attack features lack sufficient research on the communication methods and data transmission patterns used in attacks. In particular, due to the non-associated addresses, low frequency, fragmentation, and silent requirements of attacks, the features exhibited in a single session are increasingly minimal. Traditional approaches are no longer sufficient to address these challenges that relying solely on single-sample statistical features and "packet-sniffing" windowed traffic grouping detection methods. To tackle these issues, we propose a innovative approach to characterize network attack traffic based on Spatial Pyramid Pooling (SPP) by analyzing the attack communication methods and data transmission patters in the network session traffic of APT attacks with the remote information theft. Specifically, it employs derived feature attributes that integrate mean, total, and concentration characteristics to longitudinally extract multi-level spatiotemporal correlated behavioral features from aggregated multi-session sets. These features are then fused with single-session characteristics, ensuring that each session sample possesses both current traffic features and correlated properties of contextual session traffic. Additionally, this approach meets the requirements of fixed-length input for heterogeneous data in deep learning. Extensive experiments have been conducted to demonstrate that this method enhances the effective detection of APT attacks by deep learning models. Experiments results show that this approach exhibits superior timeliness, precision, and specificity when compared to Principal Component Analysis (PCA) artificial feature engineering methods and other methods based on fixed-length deep learning for raw data.

Strategic maintenance is essential for sustainable road infrastructure development. Accurate estimation of road maintenance effects can support the assessment of maintenance strategies and reasonable allocation of budgets and resources. Road deterioration is affected by sophisticated factors, but accurate investigation of the integrated deterioration factors is limited. This study developed a dynamic trade-off model (DTOM), a hybrid nonlinear and machine learning method, for quantifying temporally varied impacts of factors and examining maintenance effects at the network level. Pavement deterioration factors are classified into three categories: (i) historical observations of roughness, (ii) pavement age, and (iii) traffic, climate and environment factors. Their respective impacts on pavements are estimated using a non-linear least square regression, a joinpoint regression and a random forest model, respectively. Vehicle-based laser scanner monitored high-resolution deterioration data was collected for a large spatial scale road network in Western Australia from 2007 to 2018. Results show that the resurfacing and rehabilitation are essential for strategic reduction of deterioration. Twelve-year maintenance activities reduced the distress of roughness by 7.5% and increased road performance (the percentage of roads with roughness lower than 2.085 IRI) by 14.5% for the whole road network. The DTOM has great potentials in accurately assessing infrastructure maintenance effects and predicting deterioration scenarios.

Road network conditions and road quality are directly linked with the performance of an entire infrastructure system. As sensor monitoring of road deteriorations has rapidly increased, road infrastructure performance can now be assessed using multiple measures. However, more effective and accurate quantitative analysis methods are increasingly required. This research explores road infrastructure performance using road deterioration network data in the Mid West Gascoyne region, Australia. A spatial heterogeneity-based segmentation (SHS) model is developed for redefining road segments across the network in terms of sensor monitoring data, and for both project-level and network-level infrastructure systems management. To evaluate the model effectiveness and accuracy, an evaluation system is proposed from four aspects: segment number, homogeneity within segments, heterogeneity among segments, and segment morphology. The SHS model is compared with two widely used road network segmentation methods. The results show that the SHS model can use fewer segments to ensure higher homogeneity within segments and heterogeneity among segments across the network. Meanwhile, the segment lengths are more uniformly distributed as compared with results from other methods. The developed model and findings from this research can significantly improve the utilization of sensor monitoring network data and support multi-scale infrastructure systems management.