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Research pertaining to threat modeling is significant. However, the existing threat modeling methods suffer from ambiguity, heterogeneity and incompleteness; furthermore, the threat models at different abstraction levels are separated from each other, and the model elements are fragmented. In the knowledge engineering community, an ontology is an explicit specification of a conceptualization. Introducing the ontology method into the study of threat models is an effective way to solve the above problems. This paper creates a multiontology framework for the threat model of information systems (IS) based on domain knowledge (attack and defense knowledge), engineering experience, and industry standards (ISO/IEC 27032). The multiontology framework includes a generalized ontology (GO), a domain ontology (DO), and an application ontology (AO). This paper builds the ontology of each layer and ultimately presents case studies. The results show that the multiontology threat model based on adversarial attack and defense effectively solves the above problems of the existing threat modeling methods. In addition, systematic threat modeling using the multiontology method can be used not only for attack path-based threat analysis but also for adversarial attack and defense-based threat analysis. This method can help detect security issues and effectively guide security personnel.

Matrix completion has been widely used in image recovery and recommendation. The conventional matrix completion models based on multi-layer perceptron (MLP) only has local constraints on the observation data so that the completed matrix contains a lot of noise. Therefore, this paper proposes a novel matrix completion model based on the MLP integrating kernel regularization (MCKR). The proposed model uses the MLP to extract feature, which can automatically extract interaction feature of missing data and observational data. Moreover, through a non-linear mapping of low-dimensional latent subspace and schatten p-norm, a novel kernel regularization is derived to provide a global constraint for reducing the noise of the completed matrix. Finally, the validity and practicability of the proposed model is verified on three publicly available datasets. Compared with baselines including matrix factorization (MF), Logistic matrix factorization (LMF), expertise matrix factorization (EMF) and multi-layer perceptron (MLP), the proposed model achieves state-of-the-art result.

The quality of unmanned aerial vehicle flight control and management system (UAV FCMS) software is crucial to guaranteeing the quality of UAVs. Software requirement elicitation (SRE) is an important part of the UAV FCMS software development process. However, this activity suffers from ambiguity, heterogeneity and incompleteness; furthermore, because the use of UAVs is closely related to their geographic environment, geographic environment factors must be fully considered when conducting UAV FCMS SRE activity. In the knowledge engineering community, an ontology is an explicit specification of a conceptualization. Introducing the ontology method into the SRE process is an effective way to solve the above problems. This paper creates a UAV FCMS SRE ontology (SREO) based on domain knowledge and empirical data, as well as a geo-ontology based on geographic information metadata. Then, the paper integrates the above two ontologies into a new ontology. The goal of ontology integration is to analyze ontology concepts by adopting a hybrid ontology mapping method. The specific process analyzes the semantic similarities between the concepts of two ontologies and then decides whether to use a description logic (DL) strategy based on the analysis results. When the corresponding conditions are satisfied, the DL strategy is used to perform both direct and transitive reasoning for the relationships to achieve the ontology mapping, and the ontology integration is eventually implemented. Finally, this paper uses a criteria-based ontology evaluation approach to evaluate the quality of the newly integrated ontology. The evaluation results show that the UAV FCMS SREO considering geographic environment factors exhibits high quality. Further engineering practices also show that the SRE activities and the generated software requirement specifications (SRSs) exhibit a large increase in quality. Through the above activities, improvements to both the quality and reliability of UAV FCMS software can be achieved.

In the event of a fault in the AC/DC distribution network with modular multilevel converter (MMC), the current will surge and have a large amplitude, which seriously threatens the secure and reliable operation of the distribution network. In order to handle the fault, this paper presents an integrated control and protection strategy for AC/DC distribution network, which is based on the active current limiting control (ACLC) and single-terminal fault identification and protection principle. Firstly, an ACLC technique utilizing virtual reactance is introduced to reduce the rising rate of fault current by real-time monitoring of fault current slope. Then, taking into account the impact of ACLC, the characteristics and dynamic deviation value of single terminal current are analyzed, and a fast fault area identification method is designed. Through the cooperation of circuit breakers, this method achieves the goal of fast fault isolation. Finally, the simulation model of AC/DC distribution network is built in PSCAD/EMTDC, where the ACLC described in this paper can reduce the average rising rate of fault current by 38.4%. Moreover, the efficacy of the suggested plan is verified under different fault situations.

Currently, the continuous expansion of the credit card business and the increasingly fierce competition have made all kinds of fraud risks in the overall credit card business the biggest threat. How to detect credit card fraud accurately and effectively through machine learning algorithms has become a research hotspot and challenge in this field. This project addresses the problems of existing credit card fraud detection methods and, based on the domain knowledge of credit card fraud and machine learning theory, proposes a method based on Mahalanobis distance SMOTE-ENN hybrid sampling and Random Forest for credit card fraud detection. First, fraud detection experiments were conducted by selecting credit card fraud datasets published on the Kaggle platform. Then, to further confirm the effectiveness of the method, the method was used for the credit card customer default dataset in Taiwan for further experimental proof. Finally, the experimental results in this paper were compared with the best experimental results in other articles, reflecting that the method based on Mahalanobis distance SMOTE-ENN hybrid sampling and Random Forest can be more effective for credit card fraud detection compared with other methods. The proposed method demonstrates a strong application potential and practical effect in credit card fraud detection, which not only improves the accuracy and efficiency of fraud detection but also provides a wide range of insights and methodological references for the solution of similar problems.

Recently, the control plane of the core network (CN) has introduced a flat service-based architecture to improve flexibility, openness, and intelligence. And a novel radio access network (RAN)-CN converged network architecture was proposed to leverage the advantages of virtualization and cloudification, reduce network delay and overhead, and increase the network processing efficiency. In this paper, in order to fully utilize the advantages of the RAN-CN converged network and move services closer to user equipment, a multi-layer network architecture is proposed, where some CN network functions (NFs) are moved from the CN to the edge node (EN) and integrate with corresponding RAN functions to avoid unnecessary signaling and data forwarding. And all NFs in the EN except converged data storage management function (cDSM) are designed to be stateless to decouple the storage resources from these NFs. Based on the proposed multi-layer network architecture, a cDSM placement approach based on improved non-dominated sorting genetic algorithm II (iNSGA-II) is proposed to balance the data forwarding cost and the maximum load on cDSMs. Simulation results demonstrate that compared with the conventional NSGA-II, the convergence speed and distribution of the proposed iNSGA-II are improved. And according to different network requirements, the proposed scheme can achieve a trade-off between the data forwarding cost and the maximum load on cDSMs.

In this paper, a novel modulation scheme called enhanced asymmetrically clipped DC-biased optical OFDM (eADO-OFDM) is proposed to accommodate different demands of multi-users in downlink multiple access for visible light communication systems. Meanwhile, to satisfy various dimming levels, enhanced negative ADO-OFDM (eNADO-OFDM) is investigated and combined with eADO-OFDM for multiplexing transmission. By adjusting the proportion of these two signals, the dimming control scheme can be achieved, while the entire dynamic range of light-emitting diodes is exploited. In order to achieve superior transmission performance, the optimal power allocation under the desired dimming level is studied to minimize the overall bit error rate (BER). Moreover, to obtain more accurate estimation, an iterative receiver with interference cancellation, in which the pairwise averaging and pairwise clipping are exploited, is proposed. The simulation results show that the proposed scheme can effectively carry out the dimming control with wide dimming range for illumination and achieve superior BER performance at the same signal-to-noise ratio compared to the conventional counterparts, while only a few iterations are required.