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11th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2022

作者： Ikhwan, Syariful Wibowo, Adi Warsito, Budi School of Postgraduate Studies Diponegoro University Doctoral Program of Information System Semarang Indonesia Institut Teknologi Telkom Purwokerto D3 Telecommunication Engineering Banyumas Indonesia Diponegoro University Computer Science Informatics Department of Science and Mathematics Faculty Semarang Indonesia Diponegoro University Statistics Department of Science and Mathematics Faculty Semarang Indonesia

ISBN: (纸本)9781665460309

The increasing use of IoT devices on future networks is very helpful for humans in their lives. However, the increase in devices connected to IoT networks also increases the potential for attacks against those networks. Vulnerabilities in Internet of Things (IoT) networks can be exposed at any time. Artificial intelligence can be used to protect the IoT network by being able to detect attacks on the network so that they can be prevented. In this study, network detection was carried out using the Deep Neural Network (DNN) algorithm. The test was carried out using the UNSW Bot-IoT dataset with a comparison of training data of 75% of the overall data. The results obtained show the ability of the algorithm to detect attacks on average with 99.999% accuracy. The validation loss and training loss look very small. In this study, there is a validation loss that still occurs in overfitting, but the difference is very small. © 2022 IEEE.

关键词： Internet of things

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Parking Occupancy Prediction and Traffic Assignment in a University Environment 10

Parking Occupancy Prediction and Traffic Assignment in a Uni...

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10th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2022

作者： Farag, Mohamed Hilal, Amr El-Tawab, Samy Arab Academy for Science Technology & Maritime Transport Alexandria Egypt Virginia Tech Transportation Institute Virginia Tech BlacksburgVA United States Informatics Lab Virginia Tech BlacksburgVA United States Alexandria University Department of Computer and Systems Engineering Egypt James Madison University College of Integrated Science & Engineering HarrisonburgVA United States

ISBN: (纸本)9781665464635

The fourth industrial revolution has given rise to large-scale data-driven models like smart cities and Intelligent transportation. Within these models, applications like smart parking have been growing rapidly in research and industry. However, different scenarios and environments (e.g., shopping areas, residential places, and business complexes) can require special handling due to the various factors impacting people's schedules and behavior. In this paper, we provide an initial investigation of traffic assignment based on parking prediction for a mid-size university environment where parking is concentrated in three parking garages around the campus. Our initial investigation includes results for parking prediction using a statistical method and plans for an augmenting study using variations of Neural Networks. On top of the parking prediction layer, we propose an application layer that directs and fuses the model predictions to produce the parking options provided to the application user. The presented investigation can help the university administration in their consideration of building additional garages. © 2022 IEEE.

关键词： Forecasting

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Hybrid genetic-discretized algorithm to handle data uncertainty in diagnosing stenosis of coronary arteries

Hybrid genetic-discretized algorithm to handle data uncertai...

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作者： Alizadehsani, Roohallah Roshanzamir, Mohamad Abdar, Moloud Beykikhoshk, Adham Khosravi, Abbas Nahavandi, Saeid Plawiak, Pawel Tan, Ru San Acharya, U Rajendra Deakin University GeelongVIC Australia Department of Engineering Fasa Branch Islamic Azad University Fars Fasa Iran Centre for Pattern Recognition and Data Analytics Deakin University GeelongVIC Australia Department of Information and Communications Technology Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 st. F-3 31-155 Krakow Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland National Heart Centre Singapore Singapore Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

Coronary artery disease (CAD) is the leading cause of morbidity and death worldwide. Invasive coronary angiography is the most accurate technique for diagnosing CAD, but is invasive and costly. Hence, analytical methods such as machine learning and data mining techniques are becoming increasingly more popular. Although physicians need to know which arteries are stenotic, most of the researchers focus only on CAD detection and few studies have investigated stenosis of the right coronary artery (RCA), left circumflex (LCX) artery and left anterior descending (LAD) artery separately. Meanwhile, most of the datasets in this field are noisy (data uncertainty). However, to the best of our knowledge, there is no study conducted to address this important problem. This study uses the extension of the Z-Alizadeh Sani dataset, containing 303 records with 54 features. A new feature selection algorithm is proposed in this work. Meanwhile, by discretization of data, we also handle the uncertainty in CAD prediction. To the best of our knowledge, this is the first study attempted to handle uncertainty in CAD prediction. Finally, the genetic algorithm (GA) is used to determine the hyper-parameters of the support vector machine (SVM) kernels. We have achieved high accuracy for the stenosis diagnosis of each main coronary artery. The results of this study can aid the clinicians to validate their manual stenosis diagnosis of RCA, LCX and LAD coronary arteries. © 2020 John Wiley & Sons, Ltd.

关键词： Feature Selection

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Multiplexed bi-layered realization of fault-tolerant quantum computation over optically networked trapped-ion modules

arXiv

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arXiv 2024年

作者： Chandra, Nitish Kumar Guha, Saikat Seshadreesan, Kaushik P. Department of Informatics and Networked Systems School of Computing and Information University of Pittsburgh PittsburghPA15213 United States Wyant College of Optical Sciences University of Arizona TucsonAZ85721 United States Department of Electrical and Computer Engineering A. James Clark School of Engineering University of Maryland College ParkMD20742 United States

We study an architecture for fault-tolerant measurement-based quantum computation (FT-MBQC) over optically-networked trapped-ion modules. The architecture is implemented with a finite number of modules and ions per module, and leverages photonic interactions for generating remote entanglement between modules and local Coulomb interactions for intra-modular entangling gates. We focus on generating the topologically protected Raussendorf-Harrington-Goyal (RHG) lattice cluster state, which is known to be robust against lattice bond failures and qubit noise, with the modules acting as lattice sites. To ensure that the remote entanglement generation rates surpass the bond-failure tolerance threshold of the RHG lattice, we employ spatial and temporal multiplexing. For realistic system timing parameters, we estimate the code cycle time of the RHG lattice and the ion resources required in a bi-layered implementation, where the number of modules matches the number of sites in two lattice layers, and qubits are reinitialized after measurement. For large distances between modules, we incorporate quantum repeaters between sites and analyze the benefits in terms of cumulative resource requirements. Finally, we derive and analyze a qubit noise-tolerance threshold inequality for the RHG lattice generation in the proposed architecture that accounts for noise from various sources. This includes the depolarizing noise arising from the photonically-mediated remote entanglement generation between modules due to finite optical detection efficiency, limited visibility, and presence of dark clicks, in addition to the noise from imperfect gates and measurements, and memory decoherence with time. Our work thus underscores the hardware and channel threshold requirements to realize distributed FT-MBQC in a leading qubit platform today—trapped ions. Copyright © 2024, The Authors. All rights reserved.

关键词： Qubits

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Multi-Armed Bandit-Aided Near-Optimal Over-The-Air Updates in Multi-Band V2X Systems

Multi-Armed Bandit-Aided Near-Optimal Over-The-Air Updates i...

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IEEE International Conference on computer Communication and the Internet (ICCCI)

作者： Sherief Hashima Mostafa M. Fouda Kohei Hatano Eiji Takimoto Zubair Md Fadlullah Computational Learning Theory Team RIKEN-AIP Fukuoka Japan Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Department of Informatics Kyushu University Fukuoka Japan Department of Computer Science Western University London ON Canada

As autonomous and connected vehicles continue to garner much research attention, the automotive Over-The-Air (OTA) updates recently emerged as an important research topic. OTA is crucial to disseminate critical updates for safety and stability of on-board sensing and operational systems. In beyond 5G(BSG) systems, OTA may be regarded as cached and a service provided by cellular base stations and roadside units (RSUs). However, for large-size OTA dissemination, the Electronic Control Units (ECUs) of vehicles need to download scheduled segments of the OTA payload from the serving RSU in an opportunistic manner, i.e., while stopping at the traffic signal or waiting in traffic. To maximize the downloadable payload per vehicle served by a RSU within a limited time window, we consider multi-band RSUs and ECUs as transmitting and receiving nodes, respectively. We consider legacy RF (radio frequency), mmWave (millimeter Wave), and visible light communication (VLC) bands at the RSU to provide large capacity links to the ECUs, respectively. However, the sub-channels of these frequency bands suffer from different blockage characteristics. We formulate this as a tradeoff problem in this paper in the presence of vehicular blockers, and propose a Thompson Sampling (TS)-based opportunistic band selection to alleviate the computational burden on both the communicating RSU and ECU nodes. Based on extensive computer-based simulations, we demonstrate the performance of our proposal in contrast with an optimal (centralized) baseline, as well as other comparable heuristic-based solutions.

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Analyzing University Students' Awareness of Cybersecurity

Analyzing University Students' Awareness of Cybersecurity

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Md. Alimul Haque Sultan Ahmad Shameemul Haque Kailash Kumar Khushboo Mishra Binay Kumar Mishra Department of Computer Science Veer Kunwar Singh University Ara India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharja Saudi Arabia Al-Hafeez College Ara India College of Computing and Informatics Saudi Electronic University Riyadh Kingdom of Saudi Arabia Department of Physics Veer Kunwar Singh University Ara India

According to current research evidence, security awareness is an issue of great importance in cyber security. Although various investigations have been carried out on numerous areas of information and cybersecurity awareness, all are contradictory and depend on the circumstances. The purpose of this study is to determine young knowledge of online security risks and knowledge of countermeasures to protect them from online threats. A questionnaire technique is used for this, and an online survey through Google forms is used to collect the data from university students. The supplemental dataset in this study is utilized to assess the level of cybersecurity knowledge among university students, which is reported in detail. To recruit subjects, two different methods are used: the individual has to be of Indian descent and their age should be over 17. Participants are gathered using a combination of selective and snowball methods via institutional emails from universities and WhatsApp messaging to persons who met the criteria.

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Tight conic approximation of testing regions for quantum statistical models and measurements

arXiv

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arXiv 2023年

作者： Dall'Arno, Michele Buscemi, Francesco Department of Computer Science and Engineering Toyohashi University of Technology Japan Yukawa Institute for Theoretical Physics Kyoto University Japan Graduate School of Informatics Nagoya University Chikusa-ku Nagoya464-8601 Japan

Quantum statistical models (i.e., families of normalized density matrices) and quantum measurements (i.e., positive operator-valued measures) can be regarded as linear maps: the former, mapping the space of effects to the space of probability distributions;the latter, mapping the space of states to the space of probability distributions. The images of such linear maps are called the testing regions of the corresponding model or measurement. Testing regions are notoriously impractical to treat analytically in the quantum case. Our first result is to provide an implicit outer approximation of the testing region of any given quantum statistical model or measurement in any finite dimension: namely, a region in probability space that contains the desired image, but is defined implicitly, using a formula that depends only on the given model or measurement. The outer approximation that we construct is minimal among all such outer approximations, and close, in the sense that it becomes the maximal inner approximation up to a constant scaling factor. Finally, we apply our approximation formulas to characterize, in a semi-device independent way, the ability to transform one quantum statistical model or measurement into another. Copyright © 2023, The Authors. All rights reserved.

关键词： Mapping

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A Two-Tier Anomaly-based Intrusion Detection Approach for IoT-Enabled Smart Cities

A Two-Tier Anomaly-based Intrusion Detection Approach for Io...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Mosab Hamdan Arwa M. Eldhai Samah Abdelsalam Kifayat Ullah Ali Kashif Bashir MN Marsono Fabio Kon Daniel Macêdo Batista Department of Computer Science University of Sao Paulo (USP) Brazil Faculty of Engineering University of Science and Technology Sudan Faculty of Mathematical Sciences and Informatics Khartoum University Sudan Department of Computing and Mathematics Manchester Metropolitan University UK Faculty of Electrical Engineering Universiti Teknologi Malaysia Malaysia

The Internet of Things (IoT), like other network infrastructures, requires Intrusion Detection Systems (IDSs) to be protected against attacks. When deploying an IDS in IoT-based smart city environments, the balance between latency and node capacity must be considered, which justifies a distributed IDS with specific classifiers based on the location of the system processing nodes. This paper proposes a two-level classification technique for collaborative anomaly-based IDSs deployed on fog and edge nodes. A Gradient Boosting Classifier (GBC) is used in the lower layer classifier at the edge, while a Convolutional Neural Network (CNN) is used in the upper layer classifier at the fog. Experimentation has demonstrated that the suggested IDS architecture outperforms previous solutions. For instance, in some scenarios, when comparing our proposal with Random Forest, the former obtained an accuracy equal to 99.1%, while the latter obtained 95.3%. Furthermore, our proposal can better select the most important network traffic features, reducing 76% of the data to be analyzed and improving privacy.

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Monitoring and Prediction Model of Drinking-Water Quality Based on IoT Architecture

Monitoring and Prediction Model of Drinking-Water Quality Ba...

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IEEE Eurasia Conference on IOT, Communication and engineering (ECICE)

作者： Rita Wiryasaputra Chin-Yin Huang Yu-Wei Chen Chao-Tung Yang Department of Industrial Engineering and Enterprise Information Tunghai University Taichung Taiwan Department of Informatics Krida Wacana Christian University Jakarta Indonesia Department of Computer Science Tunghai University Taichung Taiwan Research Center for Smart Sustainable Circular Economy Taichung Taiwan

Ensuring drinking-water safety is essential to support the realization of SDGs. Drinkable water has distinct characteristics in flavor, odor, and appearance. Poor water infrastructure and treatment affect these characteristics and can pose a risk to human health. This study resulted in the development of an IoT mechanism for detecting water quality which is affordable and can mitigate the health risks posed on humans. Various water parameters, including temperature, acidity or alkalinity, and impurities, were collected by assembling the Arduino in a low-power energy consumption schema. Considering the limited resource, the real-time notification of data monitoring through a mobile messenger complements web monitoring. The retrieved data was monitored via Grafana and utilized using the binary classifiers of machine learning techniques. Enhancing the artificial intelligence framework was conducted to evaluate the best model between the decision and non-decision trees. The ratio encompassed 60% data training: 10% data testing: and 20% data validation. According to the monitoring and prediction results, authorities can control and manage drinking-water supplies that are routinely tested.

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Deep Reinforcement Learning for Network Security Applications With A Safety Guide

Deep Reinforcement Learning for Network Security Application...

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IEEE International Conference on Communications in China (ICCC)

作者： Zhibo Liu Xiaozhen Lu Yuhan Chen Yilin Xiao Liang Xiao Yanling Bu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Department of Informatics and Communication Engineering Xiamen University Xiamen China

Most of the typical reinforcement learning algorithms help wireless devices choose the security policy such as the moving strategy and communication policy by exploring all the possible state-action pairs including the risky policies that cause a severe collision or network disaster. In this paper, we design a safe reinforcement learning algorithm for safety-critical applications (e.g., intelligent transportation systems) to guide the learning agent to avoid exploring risky policies. This algorithm uses Q-network (i.e., a convolutional neural network or a deep neural network) to choose the policy and designs a safety guide to modify the chosen policy that results in dangerous status. More specifically, the safety guide includes a risk alarm module that evaluates the immediate warning value corresponding to the risk of each state-action pair and a G-network that estimates the long-term risk value. By adding the long-term risk value and the long-term expected reward output by the Q-network, this algorithm uses a safety dock to modify the chosen policy. This algorithm uses the immediate warning value to formulate a safe buffer and a risky buffer for the G-network updating to ensure fully exploration in the initial learning process. As a case study, we apply the designed algorithm in a cargo transportation system, in which the experimental results verify the effectiveness of our algorithm compared with the benchmark safe deep Q-network.

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