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Towards Cybersecurity Risk Assessment automation: an Ontological Approach

Towards Cybersecurity Risk Assessment Automation: an Ontolog...

引用

2023 IEEE international conference on Dependable, Autonomic and Secure Computing, 2023 international conference on Pervasive Intelligence and Computing, 2023 international conference on Cloud and Big Data Computing, 2023 international conference on Cyber science and technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Maunero, Nicolo De Rosa, Fabio Prinetto, Paolo Cini Cybersecurity National Lab Rome Italy Politecnico di Tornio Turin Italy Imt School for Advanced Studies Lucca Italy

ISBN: (纸本)9798350304602

In the past decade, cyber attacks have been escalating, affecting both large organizations and smaller businesses, as well as individuals and a proactive approach to cybersecurity risk assessment has become indispensable. The risk assessment process is composed of different activities, from vulnerability management to the identification of threats (threat modeling). The main challenge lies in the manual nature of these activities facing complexities due to evolving threats and the growth of ICT infrastructures. Hence, providing automation to the risk assessment process is becoming crucial. The work presented in this paper aims to automate risk assessment using an ontology-based approach;a structured and formal representation of data is provided for ICT infrastructures description and related security information leveraging a defined ontology. The proposed solution follows an asset-oriented approach in defining the ontology;this allows to tightly link together infrastructure components and security data, enhancing automation's effectiveness and precision. The ontology is automatically populated with the required security information of the infrastructure under analysis, threat modeling is performed resorting to defined inference rules and all this information items are used to assess the risk of identified threats. The risk assessment process automation, as well as the supporting models and knowledge-base, proposed allows to easily adapt to changes in both the ICT infrastructure under analysis and the threat land scape as new information are integrated seamlessly in the evaluation process and support analyst in gather, combine and analyse the information required for evaluating risk. © 2023 IEEE.

关键词： Risk assessment

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Combinatorial optimization algorithm for workshop scheduling

Combinatorial optimization algorithm for workshop scheduling

引用

2023 international conference on automation Control, Algorithm, and Intelligent Bionics, ACAIB 2023

作者： Cao, Wensheng Chen, Bin School of Computer Science and Technology Harbin Institute of Technology Harbin China

ISBN: (纸本)9781510667662

This paper studies the problem of production scheduling in a discrete production workshop, which involves the selection of equipment and the sequence of processes. Firstly, two vector coding structures are used to represent the solution to the problem. Secondly, to solve the problem of difficult search in the case of many devices, this paper improves the music chair algorithm, which has a fast convergence speed. Then, the algorithm is combined with Pareto optimality and an improved fuzzy decision-maker to solve the multi-objective production scheduling problem. Finally, it is compared with the genetic algorithm, the particle swarm optimization algorithm, and the simulated annealing algorithm to solve the single-objective scheduling problem on the actual production scheduling data. The results show that the algorithm can quickly converge on the optimal solution. Compared with the non-dominated genetic algorithm II and the multi-objective evolutionary algorithm based on decomposition to solve the multi-objective scheduling problem, the results show that this method can solve the multi-objective scheduling problem better. © 2023 SPIE.

关键词： Genetic algorithms

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Measurement automation Terminal Security Assessment Based on Bayes Network 13

Measurement Automation Terminal Security Assessment Based on...

引用

13th IEEE international conference on Electronics Information and Emergency Communication, ICEIEC 2023

作者： Li, Shuguang Fang, Jianliang Jiang, Wendong Yang, Dongmei University of Science and Technology Beijing Computer and Communication Engineering Beijing China State Grid Tongxiang Power Supply Company Zhejiang Province China

ISBN: (纸本)9798350331714

In the increasingly complex environment of the power network, the metering automation terminals of the power system face more and more security threats. The stability of the power system is based on the safety of the metering automation terminal, but the assessment of the safety risk of the metering automation terminal has the problems of difficulty in determining the indicators and low accuracy of the evaluation results. To improve the accuracy and effectiveness of the security assessment of the measurement automation terminal, this paper establishes a security assessment index system for the measurement automation terminal and effectively evaluates the terminal. Firstly, the terminal security evaluation index system is established from the three aspects of hardware, application, and network. Secondly, based on the weights of the evaluation indicators qualitatively analyzed by the AHP method, the judgment matrix is checked by the consistency analysis, and the subjective weights of the risk evaluation indicators are obtained. Then, the index weight is dynamically adjusted through the Bayesian prior probability property to obtain the objective weight of the evaluation index, and the comprehensive weight is calculated through the combined weighting to realize the monitoring and evaluation of the measurement automation terminal behavior. Finally, the simulation experiments are carried out. The results show that the security goal of the power metering automation system depends to a large extent on the hardware environment and application security. © 2023 IEEE.

关键词： automation

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Robert: An Automated Tool to Perform Mobile Application Test 24

Robert: An Automated Tool to Perform Mobile Application Test

引用

10th international conference on computer technology Applications, ICCTA 2024

作者： Chaves, Lennon Correa Oliveira, Flavia Camila Morais Tiago, Leonardo Albuquerque Castro, Renata Gabriella Vieira Sidia Institute of Science and Technology Manaus Brazil

ISBN: (纸本)9798400716386

Software tests are fundamental to ensuring that the end user does not face any issues, and there are two primary approaches to application testing: manual (testing without scripts) and automated (using programming language scripts). Thus, automated testing is promising because of its ability to reduce manual effort during testing. Thus, the goal of this study is to present a tool called Robert, which was implemented to perform a suite of automated test cases on mobile applications in the Institute of Research and Development, which develops software for smartphones and tablets with the Android operating system. To achieve this goal, a study was carried out in 2023, collecting data from all tests performed with the Robert Tool. In addition, the testing team answered a survey regarding what they gained from the tool and the lessons they learned as they used it. Between the main results, it was observed that the strategy of applying the Robert Tool, which automated 15 test cases out of a total of 35 in a specific scope, in the application validation reduced the effort of manual execution by 33%. Furthermore, another observed result is that there will be a reduction of 714h of testing through automation in 2023. The survey participants found the tool easy to use and appreciated the time savings. Overall, this study presents an industry experience report and outlines the lessons learned and the success factors for implementing automation in mobile application testing. © 2024 ACM.

关键词： Mobile applications

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Ultrasonic and VCSEL sensor fusion for distance measurement in parking assistance 24

Ultrasonic and VCSEL sensor fusion for distance measurement ...

引用

24th international conference on Control Systems and computer science, CSCS 2023

作者： Sandru, Florin-Daniel Ungureanu, Vlad-Ilie Silea, Ioan Politehnica University Timisoara Automation and Applied Informatics Timisoara Romania

ISBN: (纸本)9798350313390

Parking assistance is a desired and common feature of vehicles manufactured in the present years. For a vehicle to offer valuable information for such a system, in most of the cases, ultrasonic sensors are used to measure distance. Being dependent on a single type of sensor can result in an unreliable measurement, especially if that sensor is in an environment that affects its measurement capabilities. This paper proposes a combination of an ultrasonic sensor and a light-based measurement of distance sensor. The focus is on overcoming the technological weak points of each individual solution and obtaining more accurate measurements through sensor fusion. The fusion is based on the usage of an individual Kalman filter per sensor and an external fusion block that uses the Kalman filter outputs. Apart from being presented in graphical form, the results are analyzed using mean absolute percentage error as a relevant metric. © 2023 IEEE.

关键词： Ultrasonic sensors

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Big Mart Sales using Hybrid Learning Framework with Data Analysis 2

Big Mart Sales using Hybrid Learning Framework with Data Ana...

引用

2nd international conference on automation, Computing and Renewable Systems, ICACRS 2023

作者： Phani Praveen, S. Chaitanya, Polavarapu Mohan, A. Shariff, Vahiduddin Ramesh, Janjhyam Venkata Naga Sunkavalli, Jayaprakash Computer Science and Engineering Prasad V Potluri Siddhartha Institute of Technology Kanuru Vijayawada India Computer Science and Engineering Sir C R Reddy College of Engineering Eluru India Computer Science and Engineering Chaitanya Bharathi Institute of Technology Hyderabad India Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur India Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (纸本)9798350340235

The retail industry has undergone a rapid transformation in recent years, owing to technological advancements and the availability of massive amounts of data. In this context, predictive analytics is critical in assisting retailers in making informed decisions, optimising operations, and increasing profitability. This study employs a hybrid learning framework combining machine learning and traditional statistical methods to predict sales in the Big Mart retail chain. The first stage of the proposed approach entails a thorough data analysis in which this study examines historical sales data, product attributes, store demographics, and other relevant variables to gain insights into sales patterns and correlations. Exploratory data analysis and feature engineering are used to prepare the data for model development. The new algorithm analyses customer data and provides insights to help businesses make better business decisions. Using this technology, Big Mart hopes to increase sales and improve customer satisfaction. Big Mart used the Ensemble algorithm in this paper, a type of artificial intelligence that can learn from data and make predictions based on that data. The ensemble algorithm is a hybrid algorithm that combines the two algorithms used to find Big Mart sales, such as Random Forest (RF) and S upport Vector Machine (S VM). The algorithm is trained using historical customer data, such as purchase history, demographic information, and other variables that may influence purchasing behavior. The dataset is obtained from Kaggle to evaluate the model's performance. The algorithm can use this data to identify patterns and trends in customer behaviour, which can be used to forecast future sales. © 2023 IEEE.

关键词： Machine Learning Random Forest (RF) Support Vector Machine (SVM)

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Analysis of Electroencephalographic Signals to Study the Behavior of Brain Frequencies for the Study of Academic Stress 2

Analysis of Electroencephalographic Signals to Study the Beh...

引用

2nd international conference on automation, Computing and Renewable Systems, ICACRS 2023

作者： Moyeenudin, H.M. Hannah, S. Anuradha, T. Muthalagu, R. Seedha Devi, V. Jose Anand, A. Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai600117 India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu 6000262 India Department of Electrical and Electronics Engineering KCG College of Technology Tamil Nadu Chennai600097 India Department of Biomedical Engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu 603308 India Department of IT Jaya Engineering College Tamil Nadu Chennai602024 India Department of ECE KCG College of Technology Tamil Nadu Chennai600 097 India

ISBN: (纸本)9798350340235

The current study perform an analysis of electroencephalographic signals to study the behavior of brain frequencies in subjects who are under academic stress generated by a cognitive task, while listening to music or being silent. Creation of a corpus of more than 10 subjects under different sound stimuli is created. Characterization of brain signals are characterized for the identification of academic stress. Protocol is designed and brain signals are collected to observe the relationship between music listening and academic stress. EEG signal classifiers are used to identify differences between different sessions. Analysis of brain frequencies are analysed obtained in the sessions for each participant. © 2023 IEEE.

关键词： Academic Stress Brain Frequencies Brainwave Patterns EEG (Electroencephalography) Stress Response

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Object Detection Method for Autonomous Vehicles in Adverse Weather

Object Detection Method for Autonomous Vehicles in Adverse W...

引用

2023 IEEE international conference on Unmanned Systems, ICUS 2023

作者： Su, Huiping Wang, Xinmiao Gao, Hongbo Zheng, Xiao University of Science and Technology of China Department of Automation Hefei China School of Computer Science and Technology Anhui University of Technology Ma'anshan China

ISBN: (纸本)9798350316308

Object detection in adverse weather has become a major constraint to the development of autonomous driving. This manuscript presents an object detection model for autonomous driving in adverse weather, which combines the advantages of sensor fusion and domain incremental learning. Filter concatenation and convolution are used to perform data-level radar and camera sensor fusion. The fused information is successively segmented and stored through domain incremental learning. The proposed method provides high accuracy and robustness object detection, even in adverse weather, to provide a basis for downstream tasks and ensure driving safety. © 2023 IEEE.

关键词： Autonomous vehicles

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A Physics-Informed Integrated Modeling Method for Lithium-ion Batteries 21

A Physics-Informed Integrated Modeling Method for Lithium-io...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Fan, Yunsheng Huang, Zhiwu Guan, Kaifu Shu, Boyu Liu, Yongjie Zhu, Zeyu He, Peinan Li, Shuo Central South University School of Automation Changsha China Central South University School of Computer Science and Engineering Changsha China Changsha University of Science and Technology School of Electronic Information and Electrical Engineering Changsha China

ISBN: (纸本)9798350329223

Battery models play a crucial role in battery management systems, describing the inner workings of batteries. However, offline models cannot adapt to the battery parameter degradation. To address this issue, a physics-informed integrated battery model combines equivalent electrical circuits and a neural network regression model is proposed. Electrical circuits capture essential battery electricity behaviors while a regression model with the recurrent neural network is employed to simulate the nonlinear open circuit voltage (OCV). The regression model is trained using unlabeled learning and the parameters of the electrical circuit are identified online. Finally, the proposed battery model is validated with a cubature Kalman filter. Extensive experiments confirmed that the proposed modeling method outperforms other methods with Less than 1.66% RMSE in SOC estimation. © 2023 IEEE.

关键词： Kalman filters

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Machine Learning and Data Privacy in Human Resource Management 4

Machine Learning and Data Privacy in Human Resource Manageme...

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4th international conference on Computation, automation and Knowledge Management, ICCAKM 2023

作者： Manoharan, Geetha Jalaja, V. Sathe, Manoj A. Neetika Lourens, Melanie Suresh, K. School of Business Sr University Telangana Warangal India Reva University Bengaluru560064 India Pes Modern Institute of Business Studies Computer Science Pune India School of Management Ims Ghaziabad India Durban University of Technology South Africa Sri Venkateswara College of Engineering TamilNadu Chennai India

ISBN: (纸本)9798350393248

The incorporation of machine learning (ML) for recruiting has been one of the primary subjects in human resources because ML software investigated the first recruitment software and discovered that utilising technology enhances their productivity at work, speed, and makes the method simpler. In addition to developing and testing a prototype of the system for the activities it is intended to accomplish, this study intends to highlight the significance of employing Al in the recruitment process for the Indian Police Academy. This article has three goals: to evaluate the necessity for ML in the organization's hiring procedures, to evaluate the degrees of adoption of this technology, and, ultimately, to look at the incidence of complaints during such important organisational activities. Additionally, in order to increase the validity and trustworthiness of the study's findings, a survey research design is used, as well as a triangulation of qualitative and quantitative approaches. © 2023 IEEE.

关键词： Human resource management

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