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Intelligent Forensic Investigation Using Optimal Stacked Autoencoder for Critical Industrial Infrastructures

computers, Materials & Continua 2022年第8期72卷 2275-2289页

作者： Abdullah S.AL-Malaise AL-Ghamdi Mahmoud Ragab F.J.Alsolami Hani Choudhry Ibrahim Rizqallah Alzahrani Information Systems Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Computer Science Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Biochemistry Department Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science and Engineering Department College of Computer Science and EngineeringUniversity of Hafr Al BatinAl JamiahHafar Al Batin39524Saudi Arabia

Industrial Control systems(ICS)can be employed on the industrial processes in order to reduce the manual labor and handle the complicated industrial system processes as well as communicate *** of Things(IoT)integrates numerous sets of sensors and devices via a data network enabling independent *** incorporation of the IoT in the industrial sector leads to the design of Industrial Internet of Things(IIoT),which find use in water distribution system,power plants,*** the IIoT is susceptible to different kinds of attacks due to the utilization of Internet connection,an effective forensic investigation process becomes *** study offers the design of an intelligent forensic investigation using optimal stacked autoencoder for critical industrial *** proposed strategy involves the design of manta ray foraging optimization(MRFO)based feature selection with optimal stacked autoencoder(OSAE)model,named MFROFS-OSAE *** primary objective of the MFROFS-OSAE technique is to determine the presence of abnormal events in critical industrial ***-OSAE approach involves several subprocesses namely data gathering,data handling,feature selection,classification,and parameter ***,the MRFO based feature selection approach is designed for the optimal selection of feature ***,the OSAE based classifier is derived to detect abnormal events and the parameter tuning process is carried out via the coyote optimization algorithm(COA).The performance validation of the MFROFS-OSAE technique takes place using the benchmark dataset and the experimental results reported the betterment of the MFROFS-OSAE technique over the recent approaches interms of different measures.

关键词： Industrial control systems internet of things artificial intelligence feature selection deep learning

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Ensemble Learning for Fetal Health Classification

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computer systems science & engineering 2023年第10期47卷 823-842页

作者： Mesfer Al Duhayyim Sidra Abbas Abdullah Al Hejaili Natalia Kryvinska Ahmad Almadhor Huma Mughal Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz University Al-Kharj16273Saudi Arabia Department of Computer Science COMSATS UniversityIslamabad53000Pakistan Faculty of Computers&Information Technology Computer Science DepartmentUniversity of TabukTabuk71491Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Information Systems Department Faculty of Management Comenius University in BratislavaOdbojárov 1082005 Bratislava 25Slovakia Department of Computer Science Kinnaird College for WomenLahore54000Pakistan

Cardiotocography(CTG)represents the fetus’s health inside the womb during ***,assessment of its readings can be a highly subjective process depending on the expertise of the *** signals from fetal monitors acquire parameters(i.e.,fetal heart rate,contractions,acceleration).Objective:This paper aims to classify the CTG readings containing imbalanced healthy,suspected,and pathological fetus ***:We perform two sets of ***,we employ five classifiers:Random Forest(RF),Adaptive Boosting(AdaBoost),Categorical Boosting(CatBoost),Extreme Gradient Boosting(XGBoost),and Light Gradient Boosting Machine(LGBM)without over-sampling to classify CTG readings into three categories:healthy,suspected,and ***,we employ an ensemble of the above-described classifiers with the *** use a random over-sampling technique to balance CTG records to train the ensemble *** use 3602 CTG readings to train the ensemble classifiers and 1201 records to evaluate *** outcomes of these classifiers are then fed into the soft voting classifier to obtain the most accurate ***:Each classifier evaluates accuracy,Precision,Recall,F1-scores,and Area Under the Receiver Operating Curve(AUROC)*** reveal that the XGBoost,LGBM,and CatBoost classifiers yielded 99%***:Using ensemble classifiers over a balanced CTG dataset improves the detection accuracy compared to the previous studies and our first experiment.A soft voting classifier then eliminates the weakness of one individual classifier to yield superior performance of the overall model.

关键词： Fetal health cardiotocography(CTG) ensemble learning adaptive boosting(AdaBoost) voting classifier

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A Mixed-Integer Linear Programming Approach to Maintenance Budgeting in Electrical Distribution Networks Considering Repair Time Uncertainty

A Mixed-Integer Linear Programming Approach to Maintenance B...

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2023 International Conference on Future Energy Solutions, FES 2023

作者： Mirsaeedi, Hamed Hassankashi, Alireza Sajjadi, Mahsa Siano, Pierluigi University of Tehran Tehran Iran Iran University of Science and Technology Tehran Iran University of Tennessee Department of Electrical and Computer Engineering KnoxvilleTN United States University of Salerno Department of Management & Innovation Systems Salerno84084 Italy

ISBN: (纸本)9798350332308

As a component of asset management strategies, maintenance management is essential for improving the reliability of the Electrical Distribution Networks (EDNs). To increase system efficiency, electricity companies invest a significant effort to assign their financial sources to important system components. In this paper, a mixed-integer linear programming approach to maintenance budgeting considering restoration time uncertainty is suggested. The suggested technique finds the optimal budget for doing maintenance tasks in distribution network feeders. The objective is to reduce overall cost, which includes total interruption cost of customer (TCCIC), total energy not supplied cost TCENS), total cost of materials and labor for repair (TCREP), and total cost of maintenance (TCPM). Three cases are also taken into account to observe the effectiveness of the suggested maintenance method: Case I assumes that no maintenance actions are performed in the system, in Case II, maintenance budget for each distribution feeder is taken into consideration equally, in Case III, according to constraints, the suggested formulation allocates the optimal maintenance budget. The Roy Billinton test system's bus number two (RBTS2) is subjected to the proposed reliability improvement approaches, and the results are discussed. © 2023 IEEE.

关键词： Budget control

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PrivBox: Privacy-Preserving Deep Packet Inspection with Dual Double-masking Obfuscated Rule Generation

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wu, Pengfei Ning, Jianting Huang, Xinyi Chen, Rongmao Zhang, Kai Liang, Kaitai Wuhan University School of Cyber Science and Engineering Wuhan China Singapore Management University School of Computing and Information Systems Singapore City University of Macau Faculty of Data Science Macau China Fujian Normal University College of Computer and Cyber Security Fuzhou China Jinan University College of Cyber Security Guangzhou China National University of Defense Technology College of Computer Changsha China Shanghai University of Electric Power College of Computer Science and Technology Shanghai China Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Netherlands

Many network middleboxes have been deployed to perform deep packet inspection (DPI) over packet payloads. However, such middleboxes cannot accomplish their tasks when the traffic is encrypted. BlindBox (SIGCOMM 2015) provided the first solution for performing DPI over encrypted traffic. To improve its efficiency, a later proposal PrivDPI (CCS 2019) introduced a practical technique to generate encrypted rules. However, a recent proposal P2DPI (ASIACCS 2021) showed that the rule generator in PrivDPI can comprise the user's privacy. In this paper, we present a new attack on P2DPI and show that the privacy of its endpoints can still be compromised by the rule generator. We comprehensively analyze the vulnerability of prior studies and present PrivBox, a new DPI system that achieves the same privacy guarantee as BlindBox while maintaining practical efficiency. This is based on a new technique called dual double-masking obfuscated rule generation. For a ruleset of 3,000, PrivBox achieves connection establishment time on the endpoint side comparable to PrivDPI and supports up to 4,672 token encryptions per second, which is sufficient for a number of real-world applications. Overall, our experiment demonstrates that PrivBox is practical and well-suited for short, frequently established sessions, especially when token repeating is common. ©2004-2012 IEEE.

关键词： Differential privacy

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Developing a Smart Drip Infusion System to Elevate Precision in Healthcare

Developing a Smart Drip Infusion System to Elevate Precision...

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Ubiquitous Computing and Intelligent Information systems (ICUIS), International Conference on

作者： Sefemo Molefinyane Abid Yahya V. Hamsadhwani R. Rohini Ravi Samikannu K.Maruliya Begam Department of Electrical and Communications Systems Engineering Botswana International University of Science and Technology Palapye Botswana Department of Electrical and Electronics Engineering Periyar Maniammai Institute of Science and Technology Thanjavur India Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Tiruchengode India Department of Computer Science and Communications Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technology Science Tiruchengode India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Intravenous (IV) therapy is a critical component of modern healthcare, yet traditional IV systems are prone to human errors, manual adjustments, and limited monitoring capabilities, which can jeopardize patient safety and hinder healthcare efficiency. This paper presents the development of the Smartdrip IV Infusion System, a sophisticated and automated solution that integrates advanced sensors, control algorithms, and intuitive user interfaces to address these limitations. The Smartdrip system minimizes human intervention, provides real-time monitoring, and automates fluid administration with enhanced precision and safety features. The system demonstrates high reliability through simulation and prototype testing in detecting anomalies, issuing alerts, and ensuring accurate infusion rates, thereby improving patient outcomes and reducing healthcare provider workload. The results validate the Smartdrip system as a transformative approach to optimizing IV therapy in diverse clinical settings.

关键词： Fluids Medical treatment Prototypes User interfaces Sensor phenomena and characterization Ubiquitous computing Sensor systems Real-time systems Safety Testing

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Toward Scene Understanding with Depth and Object-Aware Clustering in Contested Environment 22

Toward Scene Understanding with Depth and Object-Aware Clust...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Bhurtel, Manish Siwakoti, Yuba R. Rawat, Danda B. Sadler, Brian M. Fossaceca, John M. Rice, Daniel O. Howard University Department of Electrical Engineering and Computer Science WashingtonDC United States U.S. Army Research Laboratory AdelphiMD United States Bae Systems Fast Labs BurlingtonMA United States

ISBN: (纸本)9798350345346

Scene understanding in a contested battlefield is one of the very difficult tasks for detecting and identifying threats. In a complex battlefield, multiple autonomous robots for multi-domain operations are likely to track the activities of the same threat/objects leading to inefficient and redundant tasks. To address this problem, we propose a novel and effective object clustering framework that takes into account the position and depth of objects scattered in the scene. This framework enables the robot to focus solely on the objects of interest. Our system model incorporates YOLOv5 object detection model (ODM) and a pre-trained depth estimation model (DEM), whose outputs are fed into the KMeans Clustering (KM C) model. To train YOLOv5, we create our own battlefield dataset entitled 'Common Objects in Battlefield (COBA)' which is split into a training dataset and a validation dataset using our customized object-aware stratified 7-fold cross-validation technique. We present the creation of minimum optimal clusters assuming the minimum number of available battlefield robots. These clusters exhibit accurate relative distances among objects, further validating the effectiveness of our approach for depth and object-aware clustering for scene understanding. Overall, our proposed approach facilitates efficient work division among robots, enhances decision-making capabilities, and helps to improve situational awareness in battlefield scenarios. © 2023 IEEE.

关键词： clustering depth estimation object detection scene understanding situational awareness task allocation

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Enhancing Wireless Sensor Network Data Collection through Aerial Unmanned Vehicles

Enhancing Wireless Sensor Network Data Collection through Ae...

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Decision Aid sciences and Application (DASA), International Conference on

作者： Amjed Abbas Ahmed Khattab M. Ali Alheeti Ali Hamza Najim Nurhizam Safie Mohd Satar Salah A. Aliesawi Aisha Hassan Abdallah Hashim Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering mam AlKadhum College (IKC) Baghdad Iraq Computer Networking Systems Department College of Computer and Information Technology University of Anbar Anbar Iraq Department of Computer Technical Engineering Imam Al-Kadhum College (IKC) Al-Diwaniyah Iraq Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Malaysia Department of Computer Networking Systems College of Computer and Information Technology University of Anbar Anbar Iraq Department of Electrical and Computer Engineering Faculty of Engineering International Islamic University Malaysia Selangor Malaysia

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

This study addresses the critical aspect of data collection within Wireless Sensor Networks (WSNs), which consist of autonomous, compact sensor devices deployed to monitor environmental conditions. These networks have limited processing capacity, storage, short-range communication capabilities, and constrained battery power. A primary challenge in WSN s revolves around data gathering, particularly when employing static sinks, leading to premature energy depletion in nodes near these sinks. Integrating mobile collectors introduces the potential for energy conservation by shortening data routes. This article introduces three distinct algorithms for optimizing data collection in WSN s. Our approach involves an in-depth analysis of diverse strategies to mitigate packet collisions and facilitate the retransmission of lost packets. Our findings underscore the considerable impact that packet collisions can exert on the overall network performance during data gathering within wireless sensor networks. Through empirical exploration and analysis, we highlight the significance of these algorithms in enhancing the efficiency, energy conservation, and reliability of data collection processes within WSNs. This research sheds light on these algorithms' vital role in addressing the pressing challenges of data gathering and network performance optimization in wireless sensor networks.

关键词： Wireless communication Wireless sensor networks Protocols Energy conservation Pressing Data collection Reliability Optimization Monitoring Drones

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Evaluation of Extended Reality Authoring Tool for General Aviation Weather Training 43

Evaluation of Extended Reality Authoring Tool for General Av...

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43rd AIAA DATC/IEEE Digital Avionics systems Conference, DASC 2024

作者： Kim, Jiwon Wang, Kexin Dorneich, Michael Winer, Eliot Brown, Lori Whitehurst, Geoffrey Iowa State University Industrial and Manufacturing Systems Engineering AmesIA United States Iowa State University Human Computer Interaction AmesIA United States Iowa State University Mechanical Engineering AmesIA United States Western Michigan University Aviation Flight Science KalamazooMI United States

ISBN: (纸本)9798350349610

This paper presents the preliminary evaluation results of an extended reality (XR) authoring tool designed for aviation weather training. XR has the potential to improve educational outcomes in flight training programs. However, the complexity and time demands of existing XR authoring tools have slowed wider adoption. This work strives to address these gaps by developing and continuously refining an instructor-centered, user-friendly XR authoring tool. Seventeen flight instructors participated in developing two XR lessons and evaluated the tool's effectiveness. Results indicated that the introduction of the XR authoring tool significantly boosted the instructors' confidence in creating XR content and alleviated concerns related to time consumption. Additionally, the findings showed that the XR authoring tool is highly usable and demands a low workload, with the XR lessons produced being highly satisfactory. By employing an instructor-centered approach, the XR authoring tool enabled instructors who are not XR experts to improve students' learning experiences, focusing on aviation weather content without the burden of technical complexities. © 2024 IEEE.

关键词： Educational technology

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H2 Performance Analysis of Tractors Traveling on Rough Terrain

ASME Letters in Dynamic Systems and Control

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ASME Letters in Dynamic systems and Control 2022年第4期2卷

作者： Izumi, Shinsaku Hayashida, Riku Xin, Xin Faculty of Computer Science and Systems Engineering Okayama Prefectural University Okayama719-1197 Japan Graduate School of Computer Science and Systems Engineering Okayama Prefectural University Okayama719-1197 Japan

This article considers tractors traveling on rough terrain and studies the effects of the road surfaces on their behavior. Clarifying these effects is useful for their stable traveling on rough terrain, which contributes to the automation of tractors. We select the H2 norm as a performance index to quantify the behavior of tractors and attempt to analyze the norm of a system corresponding to a tractor traveling on rough terrain. To improve the accuracy of the analysis, we modify the system by focusing on the relationship between the H2 norm, white noise, and the characteristics of the road surface. Subsequently, the H2 norm of the resulting system is derived as a function of the parameters, including the road roughness. Numerical examples demonstrate that the analysis result based on the modified system accurately captures the behavior of the tractor compared with the result based on the original system. Copyright © 2022 by ASME.

关键词： Linear systems

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Modified Garden Balsan Optimization Based Machine Learning for Intrusion Detection

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computer systems science & engineering 2023年第8期46卷 1471-1485页

作者： Mesfer Al Duhayyim Jaber S.Alzahrani Hanan Abdullah Mengash Mrim M.Alnfiai Radwa Marzouk Gouse Pasha Mohammed Mohammed Rizwanullah Amgad Atta Abdelmageed Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityAl QunfidhahSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The Internet of Things(IoT)environment plays a crucial role in the design of smart *** and privacy are the major challenging problems that exist in the design of IoT-enabled real-time *** susceptibilities in IoT-based systems pose security threats which affect smart environment *** detection systems(IDS)can be used for IoT environments to mitigate IoT-related security attacks which use few security *** paper introduces a modified garden balsan optimizationbased machine learning model for intrusion detection(MGBO-MLID)in the IoT cloud *** presented MGBO-MLID technique focuses on the identification and classification of intrusions in the IoT cloud ***,the presented MGBO-MLID model applies min-max normalization that can be utilized for scaling the features in a uniform *** addition,the MGBO-MLID model exploits the MGBO algorithm to choose the optimal subset of ***,the attention-based bidirectional long short-term(ABiLSTM)method can be utilized for the detection and classification of *** the final level,the Aquila optimization(AO)algorithm is applied as a hyperparameter optimizer to fine-tune the ABiLSTM *** experimental validation of the MGBO-MLID method is tested using a benchmark *** extensive comparative study reported the betterment of the MGBO-MLID algorithm over recent approaches.

关键词： Deep learning internet of things cloud computing feature selection intrusion detection

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