Information (Switzerland)最新文献_第4页

Security Analysis and Enhancement of INTERBUS Protocol in ICS Based on Colored Petri Net 基于有色Petri网的ICS中INTERBUS协议的安全性分析与改进

Information (Switzerland) Pub Date : 2023-10-29 DOI: 10.3390/info14110589

Tao Feng, Chengfan Liu, Xiang Gong, Ye Lu

引用次数: 1

Large-Scale Group Decision-Making Method Using Hesitant Fuzzy Rule-Based Network for Asset Allocation 基于犹豫模糊规则网络的大规模群体资产配置决策方法

Information (Switzerland) Pub Date : 2023-10-26 DOI: 10.3390/info14110588

Abdul Malek Yaakob, Shahira Shafie, Alexander Gegov, Siti Fatimah Abdul Rahman, Ku Muhammad Naim Ku Khalif

引用次数: 0

Securing the Network: A Red and Blue Cybersecurity Competition Case Study 网络安全:红蓝网络安全竞争案例研究

Information (Switzerland) Pub Date : 2023-10-26 DOI: 10.3390/info14110587

Cristian Chindrus, Constantin-Florin Caruntu

{"title":"Securing the Network: A Red and Blue Cybersecurity Competition Case Study","authors":"Cristian Chindrus, Constantin-Florin Caruntu","doi":"10.3390/info14110587","DOIUrl":"https://doi.org/10.3390/info14110587","url":null,"abstract":"In today’s dynamic and evolving digital landscape, safeguarding network infrastructure against cyber threats has become a paramount concern for organizations worldwide. This paper presents a novel and practical approach to enhancing cybersecurity readiness. The competition, designed as a simulated cyber battleground, involves a Red Team emulating attackers and a Blue Team defending against their orchestrated assaults. Over two days, multiple teams engage in strategic maneuvers to breach and fortify digital defenses. The core objective of this study is to assess the efficacy of the Red and Blue cybersecurity competition in fostering real-world incident response capabilities and honing the skills of cybersecurity practitioners. This paper delves into the competition’s structural framework, including the intricate network architecture and the roles of the participating teams. This study gauges the competition’s impact on enhancing teamwork and incident response strategies by analyzing participant performance data and outcomes. The findings underscore the significance of immersive training experiences in cultivating proactive cybersecurity mindsets. Participants not only showcase heightened proficiency in countering cyber threats but also develop a profound understanding of attacker methodologies. Furthermore, the competition fosters an environment of continuous learning and knowledge exchange, propelling participants toward heightened cyber resilience.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"68 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134905830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning Approaches for Big Data-Driven Metadata Extraction in Online Job Postings 基于深度学习的大数据驱动元数据提取方法

Information (Switzerland) Pub Date : 2023-10-25 DOI: 10.3390/info14110585

Panagiotis Skondras, Nikos Zotos, Dimitris Lagios, Panagiotis Zervas, Konstantinos C. Giotopoulos, Giannis Tzimas

{"title":"Deep Learning Approaches for Big Data-Driven Metadata Extraction in Online Job Postings","authors":"Panagiotis Skondras, Nikos Zotos, Dimitris Lagios, Panagiotis Zervas, Konstantinos C. Giotopoulos, Giannis Tzimas","doi":"10.3390/info14110585","DOIUrl":"https://doi.org/10.3390/info14110585","url":null,"abstract":"This article presents a study on the multi-class classification of job postings using machine learning algorithms. With the growth of online job platforms, there has been an influx of labor market data. Machine learning, particularly NLP, is increasingly used to analyze and classify job postings. However, the effectiveness of these algorithms largely hinges on the quality and volume of the training data. In our study, we propose a multi-class classification methodology for job postings, drawing on AI models such as text-davinci-003 and the quantized versions of Falcon 7b (Falcon), Wizardlm 7B (Wizardlm), and Vicuna 7B (Vicuna) to generate synthetic datasets. These synthetic data are employed in two use-case scenarios: (a) exclusively as training datasets composed of synthetic job postings (situations where no real data is available) and (b) as an augmentation method to bolster underrepresented job title categories. To evaluate our proposed method, we relied on two well-established approaches: the feedforward neural network (FFNN) and the BERT model. Both the use cases and training methods were assessed against a genuine job posting dataset to gauge classification accuracy. Our experiments substantiated the benefits of using synthetic data to enhance job posting classification. In the first scenario, the models’ performance matched, and occasionally exceeded, that of the real data. In the second scenario, the augmented classes consistently outperformed in most instances. This research confirms that AI-generated datasets can enhance the efficacy of NLP algorithms, especially in the domain of multi-class classification job postings. While data augmentation can boost model generalization, its impact varies. It is especially beneficial for simpler models like FNN. BERT, due to its context-aware architecture, also benefits from augmentation but sees limited improvement. Selecting the right type and amount of augmentation is essential.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"61 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135113706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Boosting Holistic Cybersecurity Awareness with Outsourced Wide-Scope CyberSOC: A Generalization from a Spanish Public Organization Study 提升整体网络安全意识与外包范围广泛的网络soc:从西班牙公共组织研究的概括

Information (Switzerland) Pub Date : 2023-10-25 DOI: 10.3390/info14110586

Manuel Domínguez-Dorado, Francisco J. Rodríguez-Pérez, Javier Carmona-Murillo, David Cortés-Polo, Jesús Calle-Cancho

{"title":"Boosting Holistic Cybersecurity Awareness with Outsourced Wide-Scope CyberSOC: A Generalization from a Spanish Public Organization Study","authors":"Manuel Domínguez-Dorado, Francisco J. Rodríguez-Pérez, Javier Carmona-Murillo, David Cortés-Polo, Jesús Calle-Cancho","doi":"10.3390/info14110586","DOIUrl":"https://doi.org/10.3390/info14110586","url":null,"abstract":"Public sector organizations are facing an escalating challenge with the increasing volume and complexity of cyberattacks, which disrupt essential public services and jeopardize citizen data and privacy. Effective cybersecurity management has become an urgent necessity. To combat these threats comprehensively, the active involvement of all functional areas is crucial, necessitating a heightened holistic cybersecurity awareness among tactical and operational teams responsible for implementing security measures. Public entities face various challenges in maintaining this awareness, including difficulties in building a skilled cybersecurity workforce, coordinating mixed internal and external teams, and adapting to the outsourcing trend, which includes cybersecurity operations centers (CyberSOCs). Our research began with an extensive literature analysis to expand our insights derived from previous works, followed by a Spanish case study in collaboration with a digitization-focused public organization. The study revealed common features shared by public organizations globally. Collaborating with this public entity, we developed strategies tailored to its characteristics and transferrable to other public organizations. As a result, we propose the “Wide-Scope CyberSOC” as an innovative outsourced solution to enhance holistic awareness among the cross-functional cybersecurity team and facilitate comprehensive cybersecurity adoption within public organizations. We have also documented essential requirements for public entities when contracting Wide-Scope CyberSOC services to ensure alignment with their specific needs, accompanied by a management framework for seamless operation.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"48 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135168400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pervasive Real-Time Analytical Framework—A Case Study on Car Parking Monitoring 普适实时分析框架——以停车场监控为例

Information (Switzerland) Pub Date : 2023-10-25 DOI: 10.3390/info14110584

Francisca Barros, Beatriz Rodrigues, José Vieira, Filipe Portela

{"title":"Pervasive Real-Time Analytical Framework—A Case Study on Car Parking Monitoring","authors":"Francisca Barros, Beatriz Rodrigues, José Vieira, Filipe Portela","doi":"10.3390/info14110584","DOIUrl":"https://doi.org/10.3390/info14110584","url":null,"abstract":"Due to the amount of data emerging, it is necessary to use an online analytical processing (OLAP) framework capable of responding to the needs of industries. Processes such as drill-down, roll-up, three-dimensional analysis, and data filtering are fundamental for the perception of information. This article demonstrates the OLAP framework developed as a valuable and effective solution in decision making. To develop an OLAP framework, it was necessary to create the extract, transform and load the (ETL) process, build a data warehouse, and develop the OLAP via cube.js. Finally, it was essential to design a solution that adds more value to the organizations and presents several characteristics to support the entire data analysis process. A backend API (application programming interface) to route the data via MySQL was required, as well as a frontend and a data visualization layer. The OLAP framework was developed for the ioCity project. However, its great advantage is its versatility, which allows any industry to use it in its system. One ETL process, one data warehouse, one OLAP model, six indicators, and one OLAP framework were developed (with one frontend and one API backend). In conclusion, this article demonstrates the importance of a modular, adaptable, and scalable tool in the data analysis process and in supporting decision making.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135169438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Integrated GIS-Based Reinforcement Learning Approach for Efficient Prediction of Disease Transmission in Aquaculture 基于gis的水产养殖疾病传播有效预测强化学习方法

Information (Switzerland) Pub Date : 2023-10-24 DOI: 10.3390/info14110583

Aristeidis Karras, Christos Karras, Spyros Sioutas, Christos Makris, George Katselis, Ioannis Hatzilygeroudis, John A. Theodorou, Dimitrios Tsolis

{"title":"An Integrated GIS-Based Reinforcement Learning Approach for Efficient Prediction of Disease Transmission in Aquaculture","authors":"Aristeidis Karras, Christos Karras, Spyros Sioutas, Christos Makris, George Katselis, Ioannis Hatzilygeroudis, John A. Theodorou, Dimitrios Tsolis","doi":"10.3390/info14110583","DOIUrl":"https://doi.org/10.3390/info14110583","url":null,"abstract":"This study explores the design and capabilities of a Geographic Information System (GIS) incorporated with an expert knowledge system, tailored for tracking and monitoring the spread of dangerous diseases across a collection of fish farms. Specifically targeting the aquacultural regions of Greece, the system captures geographical and climatic data pertinent to these farms. A feature of this system is its ability to calculate disease transmission intervals between individual cages and broader fish farm entities, providing crucial insights into the spread dynamics. These data then act as an entry point to our expert system. To enhance the predictive precision, we employed various machine learning strategies, ultimately focusing on a reinforcement learning (RL) environment. This RL framework, enhanced by the Multi-Armed Bandit (MAB) technique, stands out as a powerful mechanism for effectively managing the flow of virus transmissions within farms. Empirical tests highlight the efficiency of the MAB approach, which, in direct comparisons, consistently outperformed other algorithmic options, achieving an impressive accuracy rate of 96%. Looking ahead to future work, we plan to integrate buffer techniques and delve deeper into advanced RL models to enhance our current system. The results set the stage for future research in predictive modeling within aquaculture health management, and we aim to extend our research even further.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135315628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep-Learning-Based Multitask Ultrasound Beamforming 基于深度学习的多任务超声波束形成

Information (Switzerland) Pub Date : 2023-10-23 DOI: 10.3390/info14100582

Elay Dahan, Israel Cohen

引用次数: 0

Interoperability and Targeted Attacks on Terrorist Organizations Using Intelligent Tools From Network Science 使用网络科学智能工具对恐怖组织的互操作性和针对性攻击

Information (Switzerland) Pub Date : 2023-10-21 DOI: 10.3390/info14100580

Alexandros Z. Spyropoulos, Evangelos Ioannidis, Ioannis Antoniou

{"title":"Interoperability and Targeted Attacks on Terrorist Organizations Using Intelligent Tools From Network Science","authors":"Alexandros Z. Spyropoulos, Evangelos Ioannidis, Ioannis Antoniou","doi":"10.3390/info14100580","DOIUrl":"https://doi.org/10.3390/info14100580","url":null,"abstract":"The early intervention of law enforcement authorities to prevent an impending terrorist attack is of utmost importance to ensuring economic, financial, and social stability. From our previously published research, the key individuals who play a vital role in terrorist organizations can be timely revealed. The problem now is to identify which attack strategy (node removal) is the most damaging to terrorist networks, making them fragmented and therefore, unable to operate under real-world conditions. We examine several attack strategies on 4 real terrorist networks. Each node removal strategy is based on: (i) randomness (random node removal), (ii) high strength centrality, (iii) high betweenness centrality, (iv) high clustering coefficient centrality, (v) high recalculated strength centrality, (vi) high recalculated betweenness centrality, (vii) high recalculated clustering coefficient centrality. The damage of each attack strategy is evaluated in terms of Interoperability, which is defined based on the size of the giant component. We also examine a greedy algorithm, which removes the node corresponding to the maximal decrease of Interoperability at each step. Our analysis revealed that removing nodes based on high recalculated betweenness centrality is the most harmful. In this way, the Interoperability of the communication network drops dramatically, even if only two nodes are removed. This valuable insight can help law enforcement authorities in developing more effective intervention strategies for the early prevention of impending terrorist attacks. Results were obtained based on real data on social ties between terrorists (physical face-to-face social interactions).","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"119 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135511101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Mobility Control Centre and Artificial Intelligence for Sustainable Urban Districts 可持续城区交通控制中心与人工智能

Information (Switzerland) Pub Date : 2023-10-21 DOI: 10.3390/info14100581

Francis Marco Maria Cirianni, Antonio Comi, Agata Quattrone

引用次数: 1