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computing (ICOCO), IEEE International Conference on

作者： Zul-Azri Ibrahim Saiful Adli Ismail Fiza Abdul Rahim Muhammad Hazim Bin Abas Muhammad Idris Bin Khairul Anuar Aiman Harith Bin Azwan Institute of Informatics and Computing in Energy College of Computing and Informatics Universiti Tenaga Nasional Kajang Selangor Malaysia Faculty Artificial Intelligence Universiti Teknologi Malaysia Wilayah Persekutuan Kuala Lumpur Kuala Lumpur Malaysia Department of Computing College of Computing and Informatics Universiti Tenaga Nasional Kajang Selangor Malaysia

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

Domain Generation Algorithms (DGA) Botnets represent a significant threat to network security due to their ability to mask Command and Control (C&C) communications with botmaster. Traditional detection methods like domain blacklists have struggle to keep pace with the dynamic nature of these botnets. This research explores an alternative detection approach by investigating the use of CPU power consumption as an indicator for detecting DGA botnet in Internet of Things (IoT) devices. The power consumption data was collected and analyzed from a testbed comprising infected Raspberry Pi devices with DGA-based botnets. Statistical methods that include Analysis of Variance (ANOVA) and post-hoc tests, were used to determine if significant differences in CPU power consumption exist across the botnet phases. The results show that CPU power consumption is significantly higher during both the C&C and attack phases compared to normal operation. Additionally, the attack phase exhibits greater power consumption variance, suggesting unstable resource utilization. These findings highlight the potential of using power consumption data as a key metric for identifying botnet activities in IoT systems.

关键词： Command and control systems Measurement Power demand Botnet Heuristic algorithms Network security Blocklists Internet of Things Resource management Analysis of variance

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A real-time system for detecting personal protective equipment compliance using deep learning model YOLOv5 9th

A real-time system for detecting personal protective equipme...

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9th International Conference on Computer Science and Computational Intelligence, ICCSCI 2024

作者： Nazli, Nur Amielia Natasha Mohamad Sabri, Nurbaity Aminuddin, Raihah Ibrahim, Shafaf Yusof, Syafiq Nasir, Siti Diana Nabilah Mohd College of Computing Informatics & Mathematics MARA University of Technology Malacca Branch Jasin Campus Malacca77300 Malaysia Yus Industries Sdn Bhd Malaysia Faculty of Science and Engineering University of Hull Hull United Kingdom

The manufacturing and construction sectors report high rates of occupational accidents and diseases. In response, many countries, including Malaysia, have incorporated the use of Personal Protective Equipment (PPE) into their government laws and safety regulations. In Malaysia, this requirement is outlined in Standard Operating Procedure (SOP) manuals in the construction sector, which specify the mandatory use of seven types of PPE: hard hats, safety goggles, high-visibility vests, safety shoes, gloves, respirators, and earplugs. However, monitoring a large number of workers and ensuring consistent use of PPE on construction sites can be a challenging task if done manually by site supervisors. Therefore, this project aims to implement a real-time PPE detection system using a deep learning model to effectively monitor workers' safety compliance. This system can generate reports that companies in the appropriate sectors can use to monitor compliance with the Occupational Safety and Health Act (OSHA). The prototype system was developed as a web-based platform. The object detection deep learning model achieved a mean Average Precision (mAP) of 91.5% in the metric evaluation, while the accuracy testing yielded a 98.3% accuracy rate. For future work, it is suggested to enhance the detection model for other types of PPE and to make the system more reliable across all sectors by expanding the training dataset. © 2024 The Authors.

关键词： Occupational risks

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Feature Selection by Hybrid Artificial Bee Colony Algorithm for Intrusion Detection

Feature Selection by Hybrid Artificial Bee Colony Algorithm ...

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2022 International Conference on Edge computing and Applications, ICECAA 2022

作者： Stankovic, Milos Zivkovic, Miodrag Antonijevic, Milos Tanaskovic, Marko Bacanin, Nebojsa Jovanovic, Dijana Technical Faculty Singidunum University Belgrade Serbia Faculty of Informatics and Computing Singidunum University Belgrade Serbia College of academic studies"Dositej Belgrade Serbia

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

The fast increase in number of different hardware being attached to the internet of things (Io T) mesh also leverages the importance of solving the security issues for establishing the secure environment data transfer over the network. As novel vulnerabilities are being discovered daily, the scientists and network security specialists must devise new models to detect and prevent the intrusions and keep the network safe. One way to address this challenge is to apply the artificial intelligence to develop modern intrusion detection systems (IDS). These cutting-edge systems are able to adjust and counter the novel threats as they come to light, allowing reaction in the real time and altering their bearing with respect to the previous experience. However, the challenge to classify the ongoing traffic is getting more complex due to the immense data volume that is produced by the network systems and leveraged computation requirements. Consequently, the feature selection must be employed as a mean to decrease data complexity through the process of removal of the least relevant information for the current classifying job, increasing the accuracy of the model on the way. This research suggests using a hybrid variant of the commonly used artificial bee colony (ABC) metaheuristics to tackle the feature selection, aiming to increase the accuracy of the extreme learning machine classifier. The novel model has been validated on two famous network security datasets (UNSW-NB15 and CICIDS-2017) to demonstrate the improvement in the performances, and the simulation outcomes were compared to the results obtained by other contemporary methods that have been employed for the same task and under similar circumstances. © 2022 IEEE.

关键词： Internet of things

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Feature Selection by Improved Sand Cat Swarm Optimizer for Intrusion Detection 2

Feature Selection by Improved Sand Cat Swarm Optimizer for I...

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2nd IEEE International Conference on Artificial Intelligence in Everything, AIE 2022

作者： Jovanovic, Dijana Marjanovic, Marina Antonijevic, Milos Zivkovic, Miodrag Budimirovic, Nebojsa Bacanin, Nebojsa College of Academic Studies "dositej Belgrade Serbia Singidunum University Technical Faculty Belgrade Serbia Singidunum University Faculty of Informatics and Computing Belgrade Serbia

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

The rapid growth of number of devices that are connected to internet of things (IoT) networks, increases the severity of security problems that need to be solved in order to provide safe environment for network data exchange. The discovery of new vulnerabilities is everyday challenge for security experts and many novel methods for detection and prevention of intrusions are being developed for dealing with this issue. To overcome these shortcomings, artificial intelligence (AI) can be used in development of advanced intrusion detection systems (IDS). This allows such system to adapt to emerging threats, react in real-time and adjust its behavior based on previous experiences. On the other hand, the traffic classification task becomes more difficult because of the large amount of data generated by network systems and high processing demands. For this reason, feature selection (FS) process is applied to reduce data complexity by removing less relevant data for the active classification task and therefore improving algorithm's accuracy. In this work, hybrid version of recently proposed sand cat swarm optimizer algorithm is proposed for feature selection with the goal of increasing performance of extreme learning machine classifier. The performance improvements are demonstrated by validating the proposed method on two well-known datasets-UNSW-NB15 and CICIDS-2017, and comparing the results with those reported for other cutting-edge algorithms that are dealing with the same problems and work in a similar configuration. © 2022 IEEE.

关键词： Internet of things

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Natural Language Processing Studies in HumanComputer Interaction: A Bibliometric Analysis and Future Research Framework 22

Natural Language Processing Studies in HumanComputer Interac...

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22nd IEEE Student Conference on Research and Development, SCOReD 2024

作者： Rashid, Farah Syazwani Binti Mohd Kamsin, Amirrudin Bin Noor, Nurul Fazmidar Binti Mohd Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System Technology Kuala Lumpur50603 Malaysia Universiti Teknologi MARA College of Computing Informatics and Mathematics Selangor Shah Alam40450 Malaysia

ISBN: (纸本)9798331510077

This study conducts a bibliometric analysis of Natural Language Processing (NLP) within Human-Computer Interaction (HCI) to identify trends, challenges, and future directions. Analyzing 1,710 SCOPUS-indexed documents (1983-2024) using a PRISMA flowchart, the results show that the United States and China are leading contributors. Key developments include emotion recognition, chatbot interfaces, and speech processing, highlighting NLP's role in user-centered technologies. Despite growing applications, challenges such as reliability and ethical concerns persist. This analysis emphasizes the need for ethical frameworks and technological advancements to address deployment issues and align NLP innovations with the United Nations Sustainable Development Goals (SDGs). By mapping global research trends, this study provides insights into the transformative potential of NLP in HCI for developing inclusive and responsive systems. © 2024 IEEE.

关键词： Emotion Recognition

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Behind the Scenes: An Explainable Artificial Intelligence (XAI) on the Service Classification of the 5G/B5G Network 3

Behind the Scenes: An Explainable Artificial Intelligence (X...

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3rd International Conference on Digital Transformation and Applications, ICDXA 2024

作者： Allias, Noormadinah Kadir, Diyana Ab Abdullahi, Akibu Mahmoud Ismail, Shahrinaz Tunku Abdul Rahman University of Management and Technology Faculty of Computing and Information Technology Kuala Lumpur Malaysia Computer Engineering Technology Universiti Kuala Lumpur- Malaysia Institute of Information Technology Kuala Lumpur Malaysia School of Computing and Informatics Albukhary International University Kedah Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9798350373424

Fifth generation and beyond (5G/B5G) is part of the next generation of mobile technology, which is expected to offer more capacity and faster speeds than the previous generation Long-Term Evolution (LTE) network. These features enable 5G/B5G to offer users a wide range of services, including smart cities, entertainment and multimedia, healthcare and mission-critical applications that benefit the entire economy and communities. However, due to growing demand, telecom service providers need to provide better Quality of service (QoS) on their networks to meet user expectations. Therefore, it is necessary to implement service classification that enables service providers to select the right network slices for each service to improve network QoS. Previous studies have predicted the classification of 5G/B5G services using traditional machine learning algorithms such as random forest and decision tree, achieving good accuracy. However, prediction based on black-box machine learning models was not transparent enough, so mobile service providers could not understand the reliability and interpretation of the results. Therefore, this article aims to model the system performance of 5G Key Performance Indicators (KPI) and extend the experiment to Explainable Artificial Intelligence (XAI) by using the Explainable AI to look behind the scenes of the chosen black-box models for service classification of 5G/B5G networks, as this is crucial for telecom service providers to make critical investment decisions around customer and employee experience. The results from the experiments show that the random forest has the highest confidence towards class Label 3 with the probability of 35 percent. © 2024 IEEE.

关键词： Decision trees

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Operational Level System: Transaction Processing System and Decision Making 1

Operational Level System: Transaction Processing System and ...

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1st International Conference on Computer Science and Emerging Technologies, CSET 2023

作者： Wahsheh, Firas Rashed Marie, Ziyad Farea, Mazen Mohammed Al-Haithami, Wafa Abdulkarem Mohamad, Fatma Susilawati Bisht, Neeraj Ajloun National University Faculty of Business Department of Management Information Systems Ajloun Jordan Al-Istiqlal University Faculty of Administrative Sciences and Informatics Public Administration Department Jericho10 Palestine Al-Madinah International University Faculty of Finance & Administrative Sciences Malaysia Mediu Faculty of Finance and Administrative Sciences Malaysia UniSZA Faculty of Informatics and Computing Malaysia Birla Institute of Applied Sciences Bhimtal India

ISBN: (纸本)9798350341737

This research paper investigates the critical role of Operational Level Systems (OLS) in facilitating transaction processing and supporting decision-making within modern organizations. In today's fast-paced and data-driven business environment, OLS plays a pivotal role in capturing, processing, and disseminating real-time data to aid in operational efficiency and effective decision-making. This study aims to explore the functionalities and interconnections of Transaction Processing Systems (TPS) as integral components of OLS, and their contribution to informed decision-making at the operational level. To achieve this, an extensive literature review is conducted to comprehend the theoretical underpinnings and practical implications of OLS and TPS. Additionally, empirical data are collected through surveys, interviews, and case studies from various industries to gain insights into the real-world implementation and benefits of OLS and its impact on decision-making processes. The findings reveal that OLS, with its robust TPS, provides organizations with streamlined data processing capabilities, enabling seamless handling of day-to-day transactions, such as sales, inventory management, and financial transactions. Moreover, it facilitates the efficient storage and retrieval of vast amounts of transactional data, laying the foundation for data-driven decision-making. Furthermore, the research highlights the significance of the integration between TPS and decision-making systems, emphasizing the pivotal role of OLS in supporting operational managers in making well-informed decisions. The ability to access real-time transactional data empowers decision-makers to identify trends, patterns, and potential bottlenecks, leading to timely adjustments and optimized performance. This research sheds light on the vital role of Operational Level Systems, particularly Transaction Processing Systems, in today's business landscape. By efficiently processing transactions and providing time

关键词： Decision making

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E-Commerce Supply Chains with Considerations of Cyber-Security 1

E-Commerce Supply Chains with Considerations of Cyber-Securi...

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1st International Conference on Computer Science and Emerging Technologies, CSET 2023

作者： Zawaideh, Firas H. Abu-Ulbeh, Waheeb Majdalawi, Yousef I. Zakaria, Muhammad D. Jusoh, Julaily Aida Das, Sunanda Irbid National University Faculty of Science and Informaiton Technology Department of Cyber Security Irbid Jordan Al-Istiqlal University Faculty of Administrative Sciences and Informatics Cybersecurity Department Palestine Petra University Faculty of Management and Financial Sciences Business & E-commerce Department Amman Jordan Faculty of Informatics and Computing UniSZA Malaysia School of Computer Science and Engineering Jain University Bangalore India

ISBN: (纸本)9798350341737

As the global e-commerce landscape continues to expand rapidly, the efficiency and security of supply chains have become critical areas of concern. With the increasing reliance on digital platforms and the interconnected nature of supply chains, the vulnerability to cyber threats has escalated significantly. This research paper delves into the multifaceted domain of e-commerce supply chains with a primary focus on cyber-security considerations. The objective of this study is to assess the existing cyber-security measures and vulnerabilities within e-commerce supply chains while proposing effective strategies to mitigate potential risks. Through an extensive literature review, the paper highlights various cyber-security challenges faced by e-commerce enterprises, including data breaches, ransomware attacks, and supply chain disruptions caused by cyber-attacks on logistics partners. By analyzing real-world case studies and industry reports, the research provides valuable insights into the consequences of cyber-security lapses and their impact on supply chain resilience and consumer trust. The research also explores the technological advancements and best practices that can fortify e-commerce supply chains against cyber threats. It examines the role of encryption, multi-factor authentication, secure payment gateways, and cloud-based security solutions in safeguarding sensitive data and maintaining the integrity of supply chain operations. Additionally, the study investigates the importance of employee training and awareness programs to cultivate a cyber-security-oriented organizational culture. Furthermore, this paper considers the legal and regulatory aspects of cyber-security in the context of e-commerce supply chains. By examining international laws, industry standards, and compliance requirements, it highlights the responsibilities and obligations of e-commerce businesses to protect customer data and secure the flow of goods throughout the supply chain. The efficie

关键词： Cybersecurity

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Modelling and Performance Analysis of Visible Light Communication System in Industrial Implementations

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Computers, Materials & Continua 2023年第11期77卷 2189-2204页

作者： Mohammed S.M.Gismalla Asrul I.Azmi Mohd R.Salim Farabi Iqbal Mohammad F.L.Abdullah Mosab Hamdan Muzaffar Hamzah Abu Sahmah M.Supa’at Faculty of Electrical Engineering Faculty of EngineeringUniversiti TeknologiMalaysiaSkudai81310Malaysia Lightwave Communications Research Group(LCRG) Faculty of Electrical EngineeringUniversiti TeknologiSkudaiJohorMalaysia Department of Communication Engineering Faculty of Electrical and Electronic EngineeringUniversiti Tun Hussein Onn Malaysia(UTHM)Parit RajaBatu PahatJohor86400Malaysia Faculty of Computing and Informatics Universiti Malaysia SabahKota Kinabalu88400Malaysia Department of Computer Science University of São PauloSão PauloBrazil

Visible light communication(VLC)has a paramount role in industrial implementations,especially for better energy efficiency,high speed-data rates,and low susceptibility to ***,since studies on VLC for industrial implementations are in scarcity,areas concerning illumination optimisation and communication performances demand further *** such,this paper presents a new modelling of light fixture distribution for a warehouse model to provide acceptable illumination and communication *** proposed model was evaluated based on various semi-angles at half power(SAAHP)and different height levels for several parameters,including received power,signal to noise ratio(SNR),and bit error rate(BER).The results revealed improvement in terms of received power and SNR with 30 Mbps data *** modulations were studied to improve the link quality,whereby better average BER values of 5.55×10^(−15) and 1.06×10^(−10) had been achieved with 4 PAM and 8 PPM,*** simulation outcomes are indeed viable for the practical warehouse model.

关键词： Visible light communication(VLC) industrial applications warehouse model light fixtures bit error rate(BER)

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The Effects of Thermal Radiation and Viscous Dissipation on the Stagnation Point Flow of a Micropolar Fluid over a Permeable Stretching Sheet in the Presence of Porous Dissipation

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Fluid Dynamics & Materials Processing 2023年第1期19卷 61-81页

作者： Muhammad Salman Kausar H.A.M.Al-Sharifi Abid Hussanan Mustafa Mamat Faculty of Informatics and Computing University Sultan Zainal Abidin(Kampus Gong Badak)Kuala TerengganuTerengganuMalaysia Department of Mathematics College of Education for Pure SciencesUniversity of KerbalaKerbalaIraq Department of Mathematics Division of Science and TechnologyUniversity of EducationLahorePakistan

In this paper,the effects of thermal radiation and viscous dissipation on the stagnation–point flow of a micropolar fluid over a permeable stretching sheet with suction and injection are analyzed and discussed.A suitable similarity transformation is used to convert the governing nonlinear partial differential equations into a system of nonlinear ordinary differential equations,which are then solved numerically by a fourth–order Runge–Kutta *** is found that the linear fluid velocity decreases with the enhancement of the porosity,boundary,and suction ***,it increases with the micropolar and injection *** angular velocity grows with the boundary,porosity,and suction parameters,whereas it is reduced if the micropolar and injection parameters become *** is concluded that the thermal boundary layer extension increases with the injection parameter and decreases with the suction parameter.

关键词： Micropolar fluid viscous dissipation stagnation point stretching sheet porous media thermal radiation

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