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A Fused Machine Learning Approach for Intrusion Detection System

Computers, Materials & Continua 2023年第2期74卷 2607-2623页

作者： Muhammad Sajid Farooq Sagheer Abbas Atta-ur-Rahman Kiran Sultan Muhammad Adnan Khan Amir Mosavi School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam 31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of *** interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and *** learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit *** intrusion detection system controls the flow of network traffic with the help of computer *** deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network *** this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network *** Intrusion detection scheme empowered with a fused machine learning technique(IDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious *** proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection.

关键词： Fused machine learning heterogeneous network intrusion detection

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Liver Tumor Decision Support System on Human Magnetic Resonance Images:A Comparative Study

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Computer Systems science & engineering 2023年第8期46卷 1653-1671页

作者： Hiam Alquran Yazan Al-Issa Mohammed Alslatie Isam Abu-Qasmieh Amin Alqudah Wan Azani Mustafa Yasmin Mohd Yacob Department of Biomedical Systems and Informatics Engineering Yarmouk University 556Irbid21163Jordan Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid22110Jordan Department of Computer Engineering Yarmouk University 556Irbid21163Jordan The of Biomedical Technology King Hussein Medical CenterRoyal Jordanian Medical ServiceAmman11855Jordan Faculty of Electrical Engineering&Technology Campus Pauh PutraUniversiti Malaysia PerlisArau02600PerlisMalaysia Faculty of Electronic Engineering&Technology Campus Pauh PutraUniversiti Malaysia PerlisArau02600PerlisMalaysia Advanced Computing Centre of Excellence(CoE)Universiti Malaysia PerlisArau02600PerlisMalaysia

Liver cancer is the second leading cause of cancer death *** tumor detection may help identify suitable treatment and increase the survival *** imaging is a non-invasive tool that can help uncover abnormalities in human *** Resonance Imaging(MRI),in particular,uses magnetic fields and radio waves to differentiate internal human organs ***,the interpretation of medical images requires the subjective expertise of a radiologist and ***,building an automated diagnosis computer-based system can help specialists reduce incorrect *** paper proposes a hybrid automated system to compare the performance of 3D features and 2D features in classifying magnetic resonance liver tumor *** paper proposed two models;the first one employed the 3D features while the second exploited the 2D *** first system uses 3D texture attributes,3D shape features,and 3D graphical deep descriptors beside an ensemble classifier to differentiate between four 3D tumor *** top of that,the proposed method is applied to 2D slices for comparison *** proposed approach attained 100%accuracy in discriminating between all types of tumors,100%Area Under the Curve(AUC),100%sensitivity,and 100%specificity and precision as well in 3D liver *** the other hand,the performance is lower in 2D *** maximum accuracy reached 96.4%for two classes and 92.1%for four *** top-class performance of the proposed system can be attributed to the exploitation of various types of feature selection methods besides utilizing the ReliefF features selection technique to choose the most relevant features associated with different *** novelty of this work appeared in building a highly accurate system under specific circumstances without any processing for the images and human input,besides comparing the performance between 2D and 3D *** the future,the presented work can be extended to be

关键词： Liver tumors ensemble classifier 3D shape features 3D cooccurrence matrix ResNet101

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Ameliorating the re/dehydrogenation behaviour of MgH2 by zinc titanate addition

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Journal of Magnesium and Alloys 2023年第6期11卷 2205-2215页

作者： N.A.Ali N.A.Sazelee M.F.Md Din M.M.Nasef A.A.Jalil Haizen Liu M.Ismail Energy Storage Research Group Faculty of Ocean Engineering Technology and InformaticsUniversiti Malaysia Terengganu21030 Kuala NerusTerengganuMalaysia Department of Electrical and Electronic Engineering Faculty of EngineeringNational Defence University of MalaysiaKem Sungai BesiKuala LumpurMalaysia Center of Hydrogen Energy Institute of Future EnergyUniversiti Teknologi Malaysia81310 UTM Johor BahruJohorMalaysia Department of Chemical and Environmental Engineering Malaysia Japan International Institute of TechnologyUniversiti Teknologi MalaysiaJalan Sultan Yahya PetraKuala Lumpur 54100Malaysia Faculty of Chemical and Energy Engineering Universiti Teknologi Malaysia81310 UTM Johor BahruJohorMalaysia Guangxi Novel Battery Materials Research Center of Engineering Technology State Key Laboratory of Featured Metal Materials and Life-cycle Safety for Composite StructuresSchool of Physical Science and TechnologyGuangxi UniversityNanning 530004China State Key Laboratory of Featured Metal Materials and Life-cycle Safety for Composite Structures Nanning 530004China

Magnesium hydride(MgH_(2))is the most feasible and effective solid-state hydrogen storage material,which has excellent reversibility but initiates decomposing at high temperatures and has slow kinetics ***,zinc titanate(Zn_(2)TiO_(4))synthesised by the solid-state method was used as an additive to lower the initial temperature for dehydrogenation and enhance the re/dehydrogenation behaviour of MgH_(2).With the presence of Zn_(2)TiO_(4),the starting temperature for the dehydrogenation of MgH_(2)was remarkably lowered to around 290℃–305℃.In addition,within 300 s,the MgH_(2)–Zn_(2)TiO_(4)sample absorbed 5.0 wt.%of H_(2)and 2.2–3.6 wt.%H_(2)was liberated from the composite sample in 30 min,which is faster by 22–36 times than as-milled MgH_(2).The activation energy of the MgH_(2)for the dehydrogenation process was also downshifted to 105.5 k J/mol with the addition of Zn_(2)TiO_(4)indicating a decrease of 22%than as-milled MgH_(2).The superior behaviour of MgH_(2)was due to the formation of Mg Zn_(2),MgO and MgTiO_(3),which are responsible for ameliorating the re/dehydrogenation behaviour of MgH_(2).These findings provide a new understanding of the hydrogen storage behaviour of the catalysed-MgH_(2)system.

关键词： Hydrogen storage Solid-state storage MgH_(2) Additive Zn_(2)TiO_(4)

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Comparison of Machine Learning Methods for Air Quality Prediction Based on Particulate Matter (PM2.5)

Comparison of Machine Learning Methods for Air Quality Predi...

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Advancement in Data science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Farhanna Mar'i Susi Marianingsih Rifqi Abdillah Ersha Aisyah Elfaiz Riza Akhsani Setyo Prayoga Muhamad Fahmi Department of Informatics Engineering Faculty of Engineering Universitas Negeri Surabaya Surabaya Indonesia Department of Information System Jayapura University of Science and Technology Jayapura Indonesia Department of Information Technology Education Faculty of Engineering Universitas Negeri Surabaya Surabaya Indonesia Department of Biomedical Engineering Faculty of Science and Technology Universitas Airlangga Surabaya Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Particulate PM 2.5 has a major effect on human health compared to other types of pollutants such as NO 2 , 03, CO, and SO 2 . PM 2.5 is the worst type of major pollutant for human health that can cause various types of serious diseases and even death. PM 2.5 is defined as an ambient air particle, which is one of the particle elements found in the free air of the earth's surface in the troposphere layer, which is very small in size, reaching <2.5 microns which contains causing substances include polycyclic aromatic hydrocarbons, lipopolysaccharides, and transition metals (copper, manganese, and zinc). Prediction of air quality index values in national cities can help the government make policies to carry out air pollution prevention and mitigation measures by looking at local air quality trends in advance, especially when there is a significant increase in the amount of PM 2.5 pollutants. The method of predicting air quality index values can be done using machine learning techniques with time series data analysis. This study will compare machine learning methods with two approaches, namely statistical regression with RF and ARIMA and deep learning method, namely LSTM. Based on the research results, the best machine learning method approach is statistical regression, namely the RF method with accuracy evaluation using RMSE, MAE, and MSE, which are respectively with a minimum value of 0.162, 0.108, and 0.026, followed by the LSTM and ARIMA methods.

关键词： Radio frequency Deep learning Prevention and mitigation Time series analysis Urban areas Air quality Indexes Long short term memory Zinc Testing

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Performance Comparison of Interpolation Methods for Enriching WiFi Fingerprint Database 24

Performance Comparison of Interpolation Methods for Enrichin...

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9th International Conference on Cloud Computing and Internet of Things, CCIOT 2024

作者： Vu-Thanh, Trung He, Jing Duong-Bao, Ninh Thi, Luong Nguyen Lee, Seon-Woo Nguyen-Huu, Khanh College of Computer Science and Electronic Engineering Hunan University Changsha China Faculty of Mathematics and Informatics Dalat University Dalat Viet Nam Faculty of Information Technology Dalat University Dalat Viet Nam Division of Software Hallym University Chuncheon Korea Republic of Department of Electronics and Telecommunications Dalat University Dalat Viet Nam

ISBN: (纸本)9798400717161

Recently, WiFi Fingerprinting-based indoor positioning systems have gained significant attention due to the low cost of infrastructure deployment. In the data collection process, a higher density of reference points may lead to better positioning results. However, it makes this process time-consuming and laborious. To solve this problem, this study applies and compares different interpolation methods to enrich the fingerprint database without increasing the cost of the collection process. To evaluate the performance of the chosen methods, five interpolation methods are implemented in a multi-condition WiFi Fingerprinting dataset. Moreover, to check the robustness of the methods, different numbers of reference points are considered when creating the new interpolated fingerprint databases. According to the results, inverse distance weighting (IDW) is the most suitable interpolation method with low complexity and stable performance. The IDW-based database achieves an average interpolation error of less than 6 dBm compared to the original database. When applying this interpolated database to perform positioning tasks, it helps reduce the collection cost by 30% with slightly lower positioning results compared to the original database. This work demonstrates the feasibility of using interpolation to ease the data collection process of WiFi Fingerprinting, especially in complex and big areas such as airports or hospitals. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Wi-Fi

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Preliminary Study on the Effect of Traffic Representation on Accuracy Degradation in Machine Learning-based IoT Device Identification

Preliminary Study on the Effect of Traffic Representation on...

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2022 IEEE International Conference on Computing, ICOCO 2022

作者： Aqil, Nik Afifi, Firdaus Zaki, Faiz Anuar, Nor Badrul Faculty of Computer Science and Information Technology Universiti Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Kuala Nerus Terengganu21030 Malaysia

ISBN: (纸本)9781665489966

The Internet of Things (IoT) has gained attention for its rapid growth in the past few years. IoT devices such as temperature and humidity sensors and voice controllers are implemented widely, from household appliances to industrial machines. However, with the rapid growth and benefits IoT offers, we are exposed to various security vulnerabilities, such as data breaches and IoT-specific malware. Researchers are using IoT device identification as a solution for IoT security issues. IoT device identification helps network administrators identify network traffic into its originating devices. However, researchers often overlook an important issue in IoT device identification, which is accuracy degradation over time. Thus, this paper explores the severity of accuracy degradation in IoT device identification on different traffic representation approaches, which are flow, sub-flow, and packet. This paper utilizes a private, and the UNSW IoT Traffic Traces public dataset. Based on the experimental findings, the sub-flow-based approach recorded the best overall performance, with only 8% degradation in the private dataset and 1% degradation in the public dataset. Meanwhile, even though the packet-based approach only degraded 5% on the private dataset, it recorded up to an 11% accuracy decrease in the public dataset. © 2022 IEEE.

关键词： Internet of things

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Research Progress On Name Data Networking To Achieve A Superior National Product In Indonesia 8

Research Progress On Name Data Networking To Achieve A Super...

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8th International Conference on Wireless and Telematics, ICWT 2022

作者： Syambas, Nana Rachmana Juhana, Tutun Hendrawan Mulyana, Eueung Edward, Ian Joseph Matheus Situmorang, Hamonangan Mayasari, Ratna Negara, Ridha Muldina Yovita, Leanna Vidya Wibowo, Tody Ariefianto Ahdan, Syaiful Nurkahfi, Galih Nugraha Nurhayati, Ade Mulya, Hafiz Budiana, Mochamad Soebagja Institute of Technology School of Electrical Engineering and Informatics Bandung Bandung Indonesia Telkom University School of Electrical Engineering Bandung Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Lampung35132 Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung40132 Indonesia Faculty of Engineering Institut Teknologi Telkom Jakarta Jakarta11710 Indonesia

ISBN: (数字)9781665451222

ISBN: (纸本)9781665451222

Global traffic data are proliferating, including in Indonesia. The number of internet users in Indonesia reached 205 million in January 2022. This data means that 73.7% of Indonesia's population has used the internet. The median internet speed for mobile phones in Indonesia is 15.82 Mbps, while the median internet connection speed for Wi-Fi in Indonesia is 20.13 Mbps. As predicted by many, real-time traffic such as multimedia streaming dominates more than 79% of traffic on the internet network. This condition will be a severe challenge for the internet network, which is required to improve the Quality of Experience (QoE) for user mobility, such as reducing delay, data loss, and network costs. However, IP-based networks are no longer efficient at managing traffic. Named Data Network (NDN) is a promising technology for building an agile communication model that reduces delays through a distributed and adaptive name-based data delivery approach. NDN replaces the 'where' paradigm with the concept of 'what'. User requests are no longer directed to a specific IP address but to specific content. This paradigm causes responses to content requests to be served by a specific server and can also be served by the closest device to the requested data. NDN router has CS to cache the data, significantly reducing delays and improving the internet network's quality of Service (QoS). Motivated by this, in 2019, we began intensive research to achieve a national flagship product, an NDN router with different functions from ordinary IP routers. NDN routers have cache, forwarding, and routing functions that affect data security on name-based networks. Designing scalable NDN routers is a new challenge as NDN requires fast hierarchical name-based lookups, perpackage data field state updates, and large-scale forward tables. We have a research team that has conducted NDN research through simulation, emulation, and testbed approaches using virtual machines to get the best NDN router design be

关键词： Testbeds

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Spectral Generalized Category Discovery by training on combined labels

Spectral Generalized Category Discovery by training on combi...

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International Symposium on Applied Machine Intelligence and informatics (SAMI)

作者： Ruixuan Mao Modafar Al-Shouha Gábor Szűcs Department of Data Science Faculty of Informatics Eötvös Loránd University Budapest Hungary Department of Telecommunications and Media Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary

Hyperspectral imaging poses major challenges for supervised classification methods due to the high dimensionality of the data. This classification was solved in previous works with known classes. In this paper we present a different aim where the goal is to discover and distinguish novel categories for hyperspectral images in scenarios where some classes are unknown. This belongs to Generalized Category Discovery (GCD) problem with a further task for automatic clustering of unlabeled data with partial knowledge. Our contribution is an additional supervised learning stage on combined labels coming from two sources. Our approach consists of three stages, (i) dimension reduction, (ii) clusters assignment with the Hungarian method, and (iii) classification, where a special training set is constructed from ground true labels and the filtered test set with (dummy) labels that are the predicted categories from the clustering part. The proposed method (S-GCD) was evaluated on two datasets, and our experiments demonstrate that classification on combined labels greatly improves both classification and clustering scores.

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Selection of a Simple 5G Handover Algorithm: Preparation for Named Data Networking

Selection of a Simple 5G Handover Algorithm: Preparation for...

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International Conference on Wireless and Telematics (ICWT)

作者： Ade Nurhayati Syaiful Ahdan Tutun Juhana Nana Rachmana Syambas School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Faculty of Applied Science Telkom University Jakarta Indonesia Faculty of Engineering & Computer Science Universitas Teknokrat Indonesia Indonesia

ISBN: (数字)9798350377057

ISBN: (纸本)9798350377064

Named Data Networking as an alternative network for 5G network traffic is required to be able to provide better performance compared to other networks such as internet protocol networks. In NDN wireless, it is known that there is producer mobility and consumer mobility, so optimal mobility management is needed, including the handover process. In this paper the author proposes a simple RSSI-based 5G handover algorithm with distance as a reference RSSI value. This algorithm is a preparation for the handover algorithm on the NDN wireless network. By using the assumed distance value MS to eNB as the basic value for calculating RSSI, handover determination is determined by comparing the RSSI value to the RSSI threshold value. With the scenario of different numbers of users and cluster sizes, from the handover process involving 8 eNB. It can be calculated how big the handover probability is for each user. From the simulation results, it can be concluded that the more users and the larger the cluster size, the greater the handover probability value. This is linear, with the wider the cluster, the probability of getting an RSSI signal from the original eNB becomes smaller, making it possible to get an RSSI signal from neighboring eNB, so that a handover occurs.

关键词： 5G mobile communication Wireless networks Simulation Clustering algorithms Telecommunication traffic Handover Probability

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Penetration Testing for IoT Security: The Case Study of a Wireless IP Security CAM 2

Penetration Testing for IoT Security: The Case Study of a Wi...

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2nd IEEE International Conference on AI in Cybersecurity, ICAIC 2023

作者： Almazrouei, Omar Magalingam, Pritheega Kamrul Hasan, Mohammad Almehrzi, Majed Alshamsi, Ahmed Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Advanced Informatics Department Kuala Lumpur Malaysia Universiti Kebngsaan Malaysia Center for Cyber Security Faculty of Information Sceince and Technology Bangi Selangor43600 Malaysia College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates Masdar Institute of Science and Technology Department of Engineering Systems and Management Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350396966

As the trend to use the Internet of Things (IoT) applications and devices increases, security and privacy have become key concerns. IoT application adoption has increased significantly over time, with sensitive data frequently gathered by IoT devices, accidentally or consciously. According to recent research, numerous types of IoT devices have substantial vulnerabilities, and in many cases, no security procedures are in place to secure them. The focus of this study was the security issues of Internet protocol (IP) cameras. The vulnerabilities of IP cameras were investigated in more depth, as well as their influence on security and privacy at the user level, in order to assist companies and security experts in predicting attacker behavior and securing the systems. The objective of this study was to research and uncover the security and privacy vulnerabilities associated with an IP camera. This was accomplished by performing a direct inspection of the camera. A real-world test was carried out with a VAVA Outdoor Wireless IP Security Cam, which was employed as a home security camera. Information was gathered from numerous sources on the Internet for this purpose, and then the software and hardware were used to examine the security features of this device. The findings of this study revealed that the IP camera contains security faults and vulnerabilities that put user safety at risk. © 2023 IEEE.

关键词： Internet of things

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