Biblio

Problems such as the increase in the number of private vehicles with the population, the rise in environmental pollution, the emergence of unmet infrastructure and resource problems, and the decrease in time efficiency in cities have put local governments, cities, and countries in search of solutions. These problems faced by cities and countries are tried to be solved in the concept of smart cities and intelligent transportation by using information and communication technologies in line with the needs. While designing intelligent transportation systems (ITS), beyond traditional methods, big data should be designed in a state-of-the-art and appropriate way with the help of methods such as artificial intelligence, machine learning, and deep learning. In this study, a data-driven decision support system model was established to help the business make strategic decisions with the help of intelligent transportation data and to contribute to the elimination of public transportation problems in the city. Our study model has been established using big data technologies and business intelligence technologies: a decision support system including data sources layer, data ingestion/ collection layer, data storage and processing layer, data analytics layer, application/presentation layer, developer layer, and data management/ data security layer stages. In our study, the decision support system was modeled using ITS data supported by big data technologies, where the traditional structure could not find a solution. This paper aims to create a basis for future studies looking for solutions to the problems of integration, storage, processing, and analysis of big data and to add value to the literature that is missing within the framework of the model. We provide both the lack of literature, eliminate the lack of models before the application process of existing data sets to the business intelligence architecture and a model study before the application to be carried out by the authors.

The fourth industrial revolution has led to the rapid development of industrial control systems. While the large number of industrial system devices connected to the Internet provides convenience for production management, it also exposes industrial control systems to more attack surfaces. Under the influence of multiple attack surfaces, sensitive data leakage has a more serious and time-spanning negative impact on industrial production systems. How to quickly locate the source of information leakage plays a crucial role in reducing the loss from the attack, so there are new requirements for tracing sensitive data in industrial control information systems. In this paper, we propose a digital watermarking traceability scheme for sensitive data in industrial control systems to address the above problems. In this scheme, we enhance the granularity of traceability by classifying sensitive data types of industrial control systems into text, image and video data with differentiated processing, and achieve accurate positioning of data sources by combining technologies such as national secret asymmetric encryption and hash message authentication codes, and mitigate the impact of mainstream watermarking technologies such as obfuscation attacks and copy attacks on sensitive data. It also mitigates the attacks against the watermarking traceability such as obfuscation attacks and copy attacks. At the same time, this scheme designs a data flow watermark monitoring module on the post-node of the data source to monitor the unauthorized sensitive data access behavior caused by other attacks.

Social media has beneficial and detrimental impacts on social life. The vast distribution of false information on social media has become a worldwide threat. As a result, the Fake News Detection System in Social Networks has risen in popularity and is now considered an emerging research area. A centralized training technique makes it difficult to build a generalized model by adapting numerous data sources. In this study, we develop a decentralized Deep Learning model using Federated Learning (FL) for fake news detection. We utilize an ISOT fake news dataset gathered from "Reuters.com" (N = 44,898) to train the deep learning model. The performance of decentralized and centralized models is then assessed using accuracy, precision, recall, and F1-score measures. In addition, performance was measured by varying the number of FL clients. We identify the high accuracy of our proposed decentralized FL technique (accuracy, 99.6%) utilizing fewer communication rounds than in previous studies, even without employing pre-trained word embedding. The highest effects are obtained when we compare our model to three earlier research. Instead of a centralized method for false news detection, the FL technique may be used more efficiently. The use of Blockchain-like technologies can improve the integrity and validity of news sources.

With the increasing complexity of the driving environment, more and more attention has been paid to the research on improving the intelligentization of traffic control. Among them, the digital twin-based internet of vehicle can establish a mirror system on the cloud to improve the efficiency of communication between vehicles, provide warning and safety instructions for drivers, avoid driving potential dangers. To ensure the security and effectiveness of data sharing in traffic control, this paper proposes a secure and privacy-preserving scheme for digital twin-based traffic control. Specifically, in the data uploading phase, we employ a group signature with a time-bound keys technique to realize data source authentication with efficient members revocation and privacy protection, which can ensure that data can be securely stored on cloud service providers after it synchronizes to its twin. In the data sharing stage, we employ the secure and efficient attribute-based access control technique to provide flexible and efficient data sharing, in which the parameters of a specific sub-policy can be stored during the first decryption and reused in subsequent data access containing the same sub-policy, thus reducing the computing complexity. Finally, we analyze the security and efficiency of the scheme theoretically.

The heterogeneity of network traffic features brings quantitative calculation problems to the matching between network data. In order to solve the above fuzzy matching problem between the heterogeneous network feature data, a quantitative matching method for network traffic features is proposed in this paper. By constructing the numerical expression method of network traffic features, the numerical expression of key features of network data is realized. By constructing the suitable section calculation methods for the similarity of different network traffic features, the personalized quantitative matching for heterogeneous network data features is realized according to the actual meaning of different features. By defining the weight of network traffic features, the quantitative importance value of different features is realized. The weighted sum mathematical method is used to accurately calculate the overall similarity value between network data. The effectiveness of the proposed method through experiments is verified. The experimental results show that the proposed matching method can be used to calculate the similarity value between network data, and the quantitative calculation purpose of network traffic feature matching with heterogeneous features is realized.

For multi-source heterogeneous complex data types of data cleaning and visual display, we proposed to build dynamic multimode visualization analysis tool, according to the different types of data designed by the user in accordance with the data model, and use visualization technology tools to build and use CQRS technology to design, external interface using a RESTFul architecture, The domain model and data query are completely separated, and the underlying data store adopts Hbase, ES and relational database. Drools is adopted in the data flow engine. According to the internal algorithm, three kinds of graphs can be output, namely, transaction relationship network analysis graph, capital flow analysis graph and transaction timing analysis graph, which can reduce the difficulty of analysis and help users to analyze data in a more friendly way

This paper conducts the development of the key technologies of the legal case management information system considering QoS optimization. The designed system administrator can carry out that the all-round management of the system, including account management, database management, security setting management, core data entry management, and data statistics management. With this help, the QoS optimization model is then integrated to improve the systematic performance of the system as the key technology. Similar to the layering in the data source, the data set is composed of the fields of the data set, and contains the relevant information of the attribute fields of various entity element categories. Furthermore, the designed system is analyzed and implemented on the public data sets to show the results.

Smart grid is the next generation for power generation, consumption and distribution. However, with the introduction of smart communication in such sensitive components, major risks from cybersecurity perspective quickly emerged. This survey reviews and reports on the state-of-the-art techniques for detecting cyber attacks in smart grids, mainly through machine learning techniques.

Open Source Software plays an important role in many software ecosystems. Whether in operating systems, network stacks, or as low-level system drivers, software we encounter daily is permeated with code contributions from open source projects. Decentralized development and open collaboration in open source projects introduce unique challenges: code submissions from unknown entities, limited personpower for commit or dependency reviews, and bringing new contributors up-to-date in projects’ best practices & processes.In 27 in-depth, semi-structured interviews with owners, maintainers, and contributors from a diverse set of open source projects, we investigate their security and trust practices. For this, we explore projects’ behind-the-scene processes, provided guidance & policies, as well as incident handling & encountered challenges. We find that our participants’ projects are highly diverse both in deployed security measures and trust processes, as well as their underlying motivations. Based on our findings, we discuss implications for the open source software ecosystem and how the research community can better support open source projects in trust and security considerations. Overall, we argue for supporting open source projects in ways that consider their individual strengths and limitations, especially in the case of smaller projects with low contributor numbers and limited access to resources.

As the effects of climate change are becoming more and more evident, the importance of improved situation awareness is also gaining more attention, both in the context of preventive environmental monitoring and in the context of acute crisis response. One important aspect of situation awareness is the correct and thorough monitoring of air pollutants. The monitoring is threatened by sensor faults, power or network failures, or other hazards leading to missing or incorrect data transmission. For this reason, in this work we propose two complementary approaches for predicting missing sensor data and a combined technique for detecting outliers. The proposed solution can enhance the performance of low-cost sensor systems, closing the gap of missing measurements due to network unavailability, detecting drift and outliers thus paving the way to its use as an alert system for reportable events. The techniques have been deployed and tested also in a low power microcontroller environment, verifying the suitability of such a computing power to perform the inference locally, leading the way to an edge implementation of a virtual sensor digital twin.

In the recent years, we have witnessed quite notable cyber-attacks targeting industrial automation control systems. Upgrading their cyber security is a challenge, not only due to long equipment lifetimes and legacy protocols originally designed to run in air-gapped networks. Even where multiple data sources are available and collection established, data interpretation usable across the different data sources remains a challenge. A modern hydro power plant contains the data sources that range from the classical distributed control systems to newer IoT- based data sources, embedded directly within the plant equipment and deeply integrated in the process. Even abundant collected data does not solve the security problems by itself. The interpretation of data semantics is limited as the data is effectively siloed. In this paper, the relevance of semantic integration of diverse data sources is presented in the context of a hydro power plant. The proposed semantic integration would increase the data interoperability, unlocking the data siloes and thus allowing ingestion of complementary data sources. The principal target of the data interoperability is to support the data-enhanced cyber security in an operational hydro power plant context. Furthermore, the opening of the data siloes would enable additional usage of the existing data sources in a structured semantically enriched form.

The number of publications related to Explainable Artificial Intelligence (XAI) has increased rapidly this last decade. However, the subjective nature of explainability has led to a lack of consensus regarding commonly used definitions for explainability and with differing problem statements falling under the XAI label resulting in a lack of comparisons. This paper proposes in broad terms a simple comparison framework for XAI methods based on the output and what we call the practical attributes. The aim of the framework is to ensure that everything that can be held constant for the purpose of comparison, is held constant and to ignore many of the subjective elements present in the area of XAI. An example utilizing such a comparison along the lines of the proposed framework is performed on local, post-hoc, model-agnostic XAI algorithms which are designed to measure the feature importance/contribution for a queried instance. These algorithms are assessed on two criteria using synthetic datasets across a range of classifiers. The first is based on selecting features which contribute to the underlying data structure and the second is how accurately the algorithms select the features used in a decision tree path. The results from the first comparison showed that when the classifier was able to pick up the underlying pattern in the model, the LIME algorithm was the most accurate at selecting the underlying ground truth features. The second test returned mixed results with some instances in which the XAI algorithms were able to accurately return the features used to produce predictions, however this result was not consistent.

In recent years, with the rise of the Internet of things industry, a variety of user location-based applications came into being. While users enjoy these convenient services, their location information privacy is also facing a great threat. Therefore, the research on location privacy protection in the Internet of things has become a hot spot for scholars. Privacy protection microdata publishing is a hot spot in data privacy protection research. Data interference is an effective solution for privacy protection microdata publishing. Aiming at privacy protection clustering problem, a privacy protection data interference method is proposed. In this paper, the location privacy protection algorithm is studied, with the purpose of providing location services and protecting the data interference of users' location privacy. In this paper, the source location privacy protection protocol (PR \_ CECRP) algorithm with controllable energy consumption is proposed to control the energy consumption of phantom routing strategy. In the routing process from the source node to the phantom node, the source data packet forwarding mechanism based on sector area division is adopted, so that the random routing path is generated and the routing energy consumption and transmission delay are effectively controlled.

Smart city technologies promise reduced congestion by optimizing transportation movements. Increased connectivity, however, may increase the attack surface of a municipality's critical functions. Increased supply chain attacks (up nearly 80 % in 2019) and municipal ransomware attacks (up 60 % in 2019) motivate the need for holistic approaches to risk assessment. Therefore, we present a methodology to quantify the degree to which supply-chain movements may be observed or disrupted via compromised smart-city devices. Our data-processing pipeline uses publicly available datasets to model intermodal commodity flows within and surrounding a municipality. Using a hierarchy tree to adaptively sample spatial networks within geographic regions of interest, we bridge the gap between grid- and network-based risk assessment frameworks. Results based on fieldwork for the Jack Voltaic exercises sponsored by the Army Cyber Institute demonstrate our approach on intermodal movements through Charleston, SC and San Diego, CA.

Recently, Mobile Edge Cloud computing (MEC) has attracted attention both from academia and industry. The idea of moving a part of cloud resources closer to users and data sources can bring many advantages in terms of speed, data traffic, security and context-aware services. The MEC infrastructure does not only host and serves applications next to the end-users, but services can be dynamically migrated and reallocated as mobile users move in order to guarantee latency and performance constraints. This specific requirement calls for the involvement and collaboration of multiple MEC providers, which raises a major issue related to trustworthiness. Two main challenges need to be addressed: i) trustworthiness needs to be handled in a manner that does not affect latency or performance, ii) trustworthiness is considered in different dimensions - not only security metrics but also performance and quality metrics in general. In this paper, we propose a trust layer for public MEC infrastructure that handles establishing and updating trust relations among all MEC entities, making the interaction withing a MEC network transparent. First, we define trust attributes affecting the trusted quality of the entire infrastructure and then a methodology with a computation model that combines these trust attribute values. Our experiments showed that the trust model allows us to reduce latency by removing the burden from a single MEC node, while at the same time increase the network trustworthiness.

This paper proposes a logic-based machine learning approach called Acuity which is designed to facilitate user-guided elucidation of novel phenomena from evidence sparsely distributed across large volumes of linked relational data. The work builds on systems from the field of Inductive Logic Programming (ILP) by introducing a suite of new techniques for interacting with domain experts and data sources in a way that allows complex logical reasoning to be strategically exploited on large real-world databases through intuitive hypothesis-shaping and data-caching functionality. We propose two methods for rebutting or shaping candidate hypotheses and two methods for querying or importing relevant data from multiple sources. The benefits of Acuity are illustrated in a proof-of-principle case study involving a retrospective analysis of the CryptoWall ransomware attack using data from a cyber security testbed comprising a small business network and an infected laptop.

With the booming of Internet technology, the continuous emergence of new technologies and new algorithms greatly expands the application boundaries of cyberspace. While enjoying the convenience brought by informatization, the society is also facing increasingly severe threats to the security of cyberspace. In cyber security defense, cyberspace operators rely on the discovered vulnerabilities, attack patterns, TTPs, and other knowledge to observe, analyze and determine the current threats to the network and security situation in cyberspace, and then make corresponding decisions. However, most of such open-source knowledge is distributed in different data sources in the form of text or web pages, which is not conducive to the understanding, query and correlation analysis of cyberspace operators. In this paper, a knowledge graph for cyber security is constructed to solve this problem. At first, in the process of obtaining security data from multi-source heterogeneous cyberspaces, we adopt efficient crawler to crawl the required data, paving the way for knowledge graph building. In order to establish the ontology required by the knowledge graph, we abstract the overall framework of security data sources in cyberspace, and depict in detail the correlations among various data sources. Then, based on the \$$\backslash$mathbfOWL +$\backslash$mathbfSWRL\$ language, we construct the cyber security knowledge graph. On this basis, we design an analysis system for situation in cyberspace based on knowledge graph and the Snort intrusion detection system (IDS), and study the rules in Snort. The system integrates and links various public resources from the Internet, including key information such as general platforms, vulnerabilities, weaknesses, attack patterns, tactics, techniques, etc. in real cyberspace, enabling the provision of comprehensive, systematic and rich cyber security knowledge to security researchers and professionals, with the expectation to provide a useful reference for cyber security defense.