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2nd International Conference on computer Science and Engineering, IC2SE 2021

作者： Khan, Moahmmed Mutiullah Ghani, Norjihan Abdul Hamid, Suraya Abbas, Mazhar University of Malaya Faculty of Computer Science and Technology Department of Information System Kuala Lumpur Malaysia University of ha'Il College of Business Adminstration Department of Management and MIS Ha'il Saudi Arabia

ISBN: (纸本)9781665400459

The Pandemic caused due to COVID-19 has overweighed the current healthcare system and made us realize that how unaware we were of our health. Although lots of rational and harsh measured were taken to curb the spread of COVID-19 but still we lost millions of lives. During this pandemic technology played a vital role ranging from the invention of vaccine to remotely monitoring the usage with the help of IoT. Among several emerging technologies wearable smart devices were burgeoning as they are also powered by IoT now. Wearable devices are being used in different scenarios ranging from tracking and monitory infected patients to utilize the data for policy making. Proposed is a framework of an ecosystem "AWARE"which comprises of a smart band with advanced PPG and EEG sensors to detect Heart rate, Heart rate Variability, Respiration rate, SpO2, Step and Sleep data. The sensor data will be transferred to the AWARE application on host mobile through BLE and from mobile the user data it will be transferred to AWARE cloud for pre and post processing using Machine Learning algorithms. AWARE can used for monitoring and detection of health anomalies and diseases such as COVID-19 or chronic lifestyle diseases. AWARE works on a multi-Tenancy cardinality framework were the group (i.e., kids, elders, domestic worker) users can share the cloud storage and receive customized notifications. Also, the group manager (i.e., father, employer) will be notified in case of emergencies. Cloud data can be accessed by the users through dashboards. Government authorities can also access user data through APIs. Wearables are widely accepted these days due to its less intrusiveness. Although some professionals are skeptical of these devices, but the advantages are far beyond the minor pitfalls. In fact, several countries have already implemented wearables devices as the primary medium of COVID-19 detection, monitoring. © 2021 IEEE.

关键词： COVID-19

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Hotel Information system Management Audit Using COBIT 2019 - APO12

Hotel Information System Management Audit Using COBIT 2019 -...

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Innovation in technology (INOCON), IEEE International Conference for

作者： Joy Nashar Utamajaya Suhono Harso Supangat Ford Lumban Gaol Benny Ranti BINUS Graduate Program School of Information System BINUS University Jakarta Indonesia School of Electrical Engineering and Informatic Bandung Institute of Technology Bandung Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Faculty of Computer Science University of Indonesia Depok Indonesia

In general, public or private organizations or companies have used information-based technology as a support to improve business performance to be more effective and efficient in order to achieve a company’s business goals. Given the large contribution of information technology in the application of hotel applications as a supporting information system, it is a vital system that must avoid risks that can hinder and cause harm to the hotel management business processes. Therefore, it is necessary to carry out risk management using the COBIT 5 framework to manage possible risks that may occur based on the APO12 (Manage Risk) domain. From the evaluation results of the data obtained through observation and interviews as well as the calculation of the results of the questionnaire based on 6 APO12 subdomains, the results of the risk management level capability assessment in hotel applications are still at level 3 or have reached the level of established process with the target to be achieved at level 4 resulting in a gap of 1 level. Based on the results obtained, it is necessary to propose recommendations that can be used by hotels in improving the application of information technology risk management so that in the future it can achieve the expected target level so that the APO12 level of capability can increase and become more optimal.

关键词： Technological innovation Companies Risk management Information technology Interviews Information systems

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Feature Selection with Stacked Autoencoder Based Intrusion Detection in Drones Environment

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computers, Materials & Continua 2022年第12期73卷 5441-5458页

作者： Heba G.Mohamed Saud S.Alotaibi Majdy M.Eltahir Heba Mohsen Manar Ahmed Hamza Abu Sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

The Internet of Drones(IoD)offers synchronized access to organized airspace for Unmanned Aerial Vehicles(known as drones).The availability of inexpensive sensors,processors,and wireless communication makes it possible in real time *** several applications comprise IoD in real time environment,significant interest has been received by research *** IoD operates in wireless environment,it is needed to design effective intrusion detection system(IDS)to resolve security issues in the IoD *** article introduces ametaheuristics feature selection with optimal stacked autoencoder based intrusion detection(MFSOSAEID)in the IoD *** major intention of the MFSOSAE-ID technique is to identify the occurrence of intrusions in the IoD *** do so,the proposed MFSOSAE-ID technique firstly pre-processes the input data into a compatible *** addition,the presented MFSOSAEID technique designs a moth flame optimization based feature selection(MFOFS)technique to elect appropriate ***,firefly algorithm(FFA)with stacked autoencoder(SAE)model is employed for the recognition and classification of intrusions in which the SAE parameters are optimally tuned with utilize of *** performance validation of the MFSOSAE-ID model was tested utilizing benchmark dataset and the outcomes implied the promising performance of the MFSOSAE-ID model over other techniques with maximum accuracy of 99.72%.

关键词： Internet of drones unmanned aerial vehicles security intrusion detection machine learning

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Advanced IOT Traffic Light Control system

Advanced IOT Traffic Light Control System

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International Conference on Advances in Computational Tools for Engineering Applications, ACTEA

作者： Martin Abou Hamad Antonio El Hajj Roy Abi Zeid Daou Ali Hayek Gaby Abou Haidar Biomedical Technologies Department Faculty of Public Health Lebanese German University Lebanon MART Learning Education and Research Center Lebanon Department of Computer Architecture and System Programming University of Kassel Kassel Germany Engineering Faculty American University of Science and Technology Zahle Lebanon

For several years, traffic congestion has been a major problem in big cities where the number of cars and different means of transportation has been increasing significantly. The problem of congestion is becoming more and more critical, and if not treated smartly this issue will negatively affect drivers by wasting time and fuel gas while waiting for hours in lanes. This paper presents a new and smart way to mitigate this issue in an affordable cost, minimum processing power, and low power consumption. This concept takes into consideration the majority of the cases that may cause congestion and presents a smart and accurate outputs to ease traffic flow leading to the prediction of the peak hours of traffic congestion for smarter control. A model is designed to study the case of a four lanes crossroad with two traffic lights and two LCD monitors. The strategy in reading data is divided into two parts: real data from sensors and pre collected data from google maps to create a kind of a predicted pattern over a certain time interval. The responsiveness of the system is analyzed thoroughly, and the accuracy of all possible cases is carefully considered and evaluated. Each part of the system was tested alone, and the overall system is still in an ongoing testing phase. The results have shown minimum faulty errors and accepted outputs that can lead to safe traffic control decisions. Finally, integrating more IoT devices and sensors between V2V, V2P, V2I with the help of artificial intelligence will definitely optimize this system with higher accuracy.

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Sensor Array system Based on Electronic Nose to Detect Borax in Meatballs with Artificial Neural Network

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Journal of Electrical and computer Engineering 2023年第1期2023卷

作者： Pradhana, Anak Agung Surya Astuti, Suryani Dyah Fauziah Permatasari, Perwira Annissa Dyah Agustina, Riskia Yaqubi, Ahmad Khalil Setyawati, Harsasi Winarno Putra, Cendra Devayana Department of Computer System Faculty of Technology and Informatics Indonesian Institute of Business and Technology Denpasar80225 Indonesia Department of Physics Faculty of Science and Technology Airlangga University Surabaya60115 Indonesia Department of Mathematics Faculty of Science and Technology Airlangga University Surabaya60115 Indonesia Magister of Forensic Science Post Graduate School Airlangga University Surabaya60115 Indonesia Faculty of Science and Technology Airlangga University Surabaya60115 Indonesia Department of Chemistry Faculty of Science and Technology Airlangga University Surabaya60115 Indonesia Institute of Information Management National Cheng Kung University Tainan Taiwan

The categorization of odors utilizing gas sensor arrays with various meatball borax concentrations has been studied. The samples included meatballs with a borax content of 0.05%, 0.10%, 0.15%, 0.20%, and 0.25% (%mm) and meatballs without any borax. Six TGS gas sensors with a baseline of 10 seconds, a detecting period of 120 seconds, and a purging period of 250 seconds make up the gas sensor array used in this work. Artificial neural networks (ANNs) and principal component analysis (PCA), which are beneficial for feature extraction and classification, are used to handle the collected data based on machine learning approaches. Two models were produced by the data analysis: model 1, which only used the PCA approach, and model 2, which only used the ANN methodology. 90.33% is the total variance value of PC from model 1. In addition, the multilayer perceptron artificial neural network (ANN-MLP) technique for model 2 yielded accuracy values of 95%. © 2023 Anak Agung Surya Pradhana et al.

关键词： Principal component analysis

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qing Liu Qian Gao Jun Fan Zhiqiang Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China China Telecom Digital Intelligence Techonology Co Ltd Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN— a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model’s effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets.

关键词： Measurement Bridges Contrastive learning Signal processing Data processing Vectors Space exploration Resource management Speech processing Mutual information

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On Big Data Forensic and Forensic Cloud Environment

On Big Data Forensic and Forensic Cloud Environment

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International Conference on Smart Communications and Networking (SmartNets)

作者： Oteng Tabona Kopo M. Ramokapane Thabiso M. Maupong Faculty of Computer and Information Systems Higher Colleges of Technology Abu Dhabi United Arab Emirates Department of Computer Science University of Bristol Bristol United Kingdom Department of Computer Science and Information System Botswana International University of Science and Technology Palapye Botswana

ISBN: (数字)9781665487580

ISBN: (纸本)9781665487597

The number of electronic devices involved in cyber related crimes has increased significantly in the last few years. This is primarily due to advancements in technology and increased accessibility to devices. This poses significant challenges with respect to forensic investigations as investigators are faced with processing huge volumes of data from various sources within a limited time. Due to the urgency, frequency, and magnitude of some cases, investigators may miss deadlines, and crucial leads. In this paper, we consider these above stated challenges and propose a new definition for big data forensics, which recognises the time aspect and variety of sources when carrying our digital forensics. This definition will help digital forensic tool designers consider these critical aspects and design comprehensive tools to combat the limitations of the current state-of-the-art. We review an existing tool, the Forensic Cloud Environment (FCE), against the proposed definition. We show how its design leverages the power of Hadoop Distributed File system (HDFS), HBase, and MapReduce to provide a comprehensive solution for big data forensics. In conclusion, we provide an argument for FCE as a solution for resolving big data forensic challenges based on general Scientific Working Group on Digital Evidence (SWGDE) forensic principles.

关键词： File systems Digital forensics Big Data Best practices

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Strengthening IT Governance in the Crypto Marketplace: Leveraging Penetration Testing and Standards Alignment

Strengthening IT Governance in the Crypto Marketplace: Lever...

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International Conference on Informatics, Multimedia, Cyber and Information system (ICIMCIS)

作者： Semi Yulianto Erly Krisnanik Muhammad Syarif Hartawan Computer Science Department Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information System Department University of Pembangunan Nasional Veteran Jakarta Indonesia Faculty of Technology University of Krisnadwipayana Jakarta Indonesia ICT Department Asia e University Malaysia

This research explores the vital role of penetration testing in cybersecurity, specifically its alignment with ISO 27001:2022, COBIT 2019, and NIST CSF standards in the context of crypto asset exchange management. These sectors grapple with rapidly evolving threat landscapes, necessitating robust cybersecurity strategies. However, the integration of penetration testing with these standards remains underexamined. Through a comprehensive approach, including literature reviews, surveys, case studies, and interviews, this study investigates the incorporation of penetration testing into an organization's cybersecurity. It unveils the current state of penetration testing practices, interprets ISO 27001:2022, COBIT 2019, and NIST CSF perspectives, and pinpoints integration challenges and opportunities. This study offers practical insights and best practices for integrating penetration testing into cybersecurity frameworks by emphasizing alignment. It underscores the importance of aligning penetration testing with information security standards in the crypto asset exchange management sector, enhancing security, proactively identifying vulnerabilities, and effectively mitigating risks in the context of growing crypto asset importance. This research provides invaluable guidance for safeguarding digital assets in this evolving domain.

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Water Wave Optimization with Deep Learning Driven Smart Grid Stability Prediction

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computers, Materials & Continua 2022年第12期73卷 6019-6035页

作者： Anwer Mustafa Hilal Aisha Hassan Abdalla Hashim Heba G.Mohamed Mohammad Alamgeer Mohamed K.Nour Anas Abdelrahman Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science and Bioinformatics Singhania UniversityPacheri BariJhnujhunuRajasthanIndia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Mechanical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt

Smart Grid(SG)technologies enable the acquisition of huge volumes of high dimension and multi-class data related to electric power grid operations through the integration of advanced metering infrastructures,control systems,and communication *** SGs,user demand data is gathered and examined over the present supply criteria whereas the expenses are then informed to the clients so that they can decide about electricity *** the entire procedure is valued on the basis of time,it is essential to perform adaptive estimation of the SG’s *** advancements inMachine Learning(ML)andDeep Learning(DL)models enable the designing of effective stability prediction models in *** this background,the current study introduces a novel Water Wave Optimization with Optimal Deep Learning Driven Smart Grid Stability Prediction(WWOODL-SGSP)*** aim of the presented WWOODL-SGSP model is to predict the stability level of SGs in a proficient *** attain this,the proposed WWOODL-SGSP model initially applies normalization process to scale the data to a uniform ***,WWO algorithm is applied to choose an optimal subset of features from the pre-processed ***,Deep Belief Network(DBN)model is followed to predict the stability level of ***,Slime Mold Algorithm(SMA)is exploited to fine tune the hyperparameters involved in DBN *** order to validate the enhanced performance of the proposedWWOODL-SGSP model,a wide range of experimental analyses was *** simulation results confirmthe enhanced predictive results of WWOODL-SGSP model over other recent approaches.

关键词： Smart grid stability prediction deep learning energy systems machine learning metaheursitics

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EventTrojan: Manipulating Non-Intrusive Speech Quality Assessment via Imperceptible Events

EventTrojan: Manipulating Non-Intrusive Speech Quality Asses...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Ying Ren Kailai Shen Zhe Ye Diqun Yan Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Juphoon System Software Co. Ltd Ningbo China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Non-Intrusive speech quality assessment (NISQA) has gained significant attention for predicting speech’s mean opinion score (MOS) without requiring the reference speech. Researchers have gradually started to apply NISQA to various practical scenarios. However, little attention has been paid to the security of NISQA models. Backdoor attacks represent the most serious threat to deep neural networks (DNNs) due to the fact that backdoors possess a very high attack success rate once embedded. However, existing backdoor attacks assume that the attacker actively feeds samples containing triggers into the model during the inference phase. This is not adapted to the specific scenario of NISQA. And current backdoor attacks on regression tasks lack an objective metric to measure the attack performance. To address these issues, we propose a novel backdoor triggering approach (EventTrojan) that utilizes an event during the usage of the NISQA model as a trigger. Moreover, we innovatively provide an objective metric for backdoor attacks on regression tasks. Extensive experiments on four benchmark datasets demonstrate the effectiveness of the EventTrojan attack. Besides, it also has good resistance to several defense methods.

关键词： Measurement Resistance Adaptation models Multimedia systems Current measurement Artificial neural networks Benchmark testing

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