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A generation of dataset towards an Anomaly-Based Intrusion Detection System to detect Denial of Sleep Attacks in Internet of Things (IoT)

A generation of dataset towards an Anomaly-Based Intrusion D...

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International Conference on Advances in ICT for Emerging Regions

作者： Ishara Dissanayake Hesiri Dhammika Weerasinghe Anuradhi Welhenge Dept. of Computer Engineering University of Sri Jayewardenepura Ratmalana Sri Lanka Dept. of Computer Systems Engineering University of Kelaniya Kelaniya Sri Lanka

In this digital era, Wireless Sensor networks (WSN), or else, Internet of Things (IoT) when these sensor networks are accessible via the internet, is a common and widely used technology. These tiny sensor nodes in these networks help to enhance and automate processes in various fields such as, smart homes and cities, healthcare, smart vehicular networks, Smart Grids and so on. Although this technology is widely used in many fields, these tiny devices are equipped with limited amounts of resources. Limited capacity of battery power is one of the major concerns with IoT when it comes to some specific applications. In addition to this, this limited battery capacity is vulnerable for power depletion attacks which can reduce the lifetime of these devices significantly. So, there is an urgent need of solid solution to preserve this battery power as much as possible. One proper solution for this is, a machine learning based intelligent intrusion detection system that can detect power depletion attacks against these IoT applications. However, the lack of a proper dataset to train and evaluate these machine learning models appears as a major obstacle for an intelligent intrusion detection system. So, in this paper, we are going to discuss how we can use COOJA simulator to generate a proper dataset that can be used to develop and evaluate machine learning models to detect Denial of Sleep attacks against Wireless Sensor Networks. Here, we are going to discuss 3 strategies (UDP flood attack, wormhole attack and externally generated legitimate request attack) to simulate Denial of Sleep attacks for WSNs, so that, any person can generate their own dataset according to their requirements. At the end of the paper, we have evaluated each strategy to deplete the power of the sensor nodes. Then, our conclusions are given.

关键词： Wireless sensor networks Data security Urban areas Intrusion detection Machine learning Solids Batteries

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A Novel Architecture of CXL Protocol Data Link Layer for Low Latency Memory Access

A Novel Architecture of CXL Protocol Data Link Layer for Low...

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International Conference on Microelectronics, ICM

作者： Basma H. Mohamed Esmail Hany Mahmoud El-Tahawy Mohamed Abdel-Salam M. Watheq El-Kharashi Mona Safar Computer and Systems Engineering Dept. Ain Shams University Cairo Egypt Siemens EDA Cairo Egypt Cairo Egypt

With the increase in memory-intensive applications, there is a need to provide low latency paths for memory access and coherent caching between host processors and storage devices. This paper proposes a novel architecture for the Data Link Layer of Compute Express Link (CXL) protocol, as defined in CXL Specification Revision 2.0. The architecture presented here contains the transmission and reception chains, which ensures the reliable conveyance of the packets between two CXL components using the CXL protocol. This paper explains how the proposed architecture ensures reliable data conveyance, responses and requests, through the addition of a 16-bit Cyclic Redundancy Check (CRC) to each 512-bit FLow control unIT (FLIT) in the transmitter side, and how the packets are processed in receiver side. The architecture is intended to have simple implementation in addition to having minimum datapath delays. The whole architecture is designed using HDL then verified using test-benches for different protocol scenarios to make sure that the functionality of the system is achieved. In addition, the whole design is synthesized using United Microelectronics Corporation (UMC) 130 nm [1] and Taiwan Semicomductors Manufactor Company (TSMC) 28 nm [2] technologies to get the maximum achieved frequency and calculate the corresponding power and area. The proposed hardware implementation manages to work up to 1 GHz using TSMC 28 nm technology and 438.59 MHz using UMC 130 nm technology. Its estimated dynamic and leakage power consumption by UMC 130 nm technology at 1 GHz are 95.3698 mW and 171.17 mW, respectively. In addition, its area cost extracted after synthesis is 242124.8 $\mu \mathrm{m}^{2}$.

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Movement-Based Interaction: An Approach to Visual and Auditory Stimulus and Memory*

Movement-Based Interaction: An Approach to Visual and Audito...

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Experiment@ International Conference (exp.at)

作者： Maria Rita Nogueira Paulo Menezes Institute of Systems and Robotics University of Coimbra Coimbra Portugal Dept. of Electrical and Computer Engineering University of Coimbra Coimbra Portugal

ISBN: (数字)9798350314403

ISBN: (纸本)9798350314410

Movement-based interaction design is an innovative field that leverages the body's natural movements to create intuitive and engaging interfaces. This approach takes a sensorial approach to visual and auditory memory, seeking to enhance user engagement and create more meaningful experiences. The use of movement-based interfaces has significant implications for senior citizens, who may have mobility limitations or cognitive impairments. Using movement-based interfaces, seniors can engage in physical activity and maintain cognitive function, essential aspects for healthy aging. Movement-based interfaces can also help children develop physical coordination, cognitive skills, and social interaction. Furthermore, movement-based interaction design can benefit everyone's physical well-being by promoting physical activity and reducing sedentary behavior. Studies have demonstrated that movement-based interfaces can enhance posture, physical activity levels, and general health. By combining movement with sensory experiences, movement-based interaction design can create a more engaging and enjoyable experience for users, which can motivate them to maintain physical and cognitive health. In this article, we present two case studies that research the effectiveness of movement-based interaction design in enhancing visual and auditory memory. Overall, the results of these case studies suggest that movement-based interaction design is an effective approach to enhancing visual and auditory memory. The introduction of movement-based interfaces can give participants a more enjoyable and memorable experience, improving user engagement and information retention.

关键词： Visualization Pediatrics Aging Older adults

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Fuzzy-TOPSIS and the Urgency-Seriousness-Growth Scoring Technique to Determine Weights of Uncertainty Issues in School Reopening Factors During COVID-19

Fuzzy-TOPSIS and the Urgency-Seriousness-Growth Scoring Tech...

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IEEE International Conference on Fuzzy systems (FUZZ-IEEE)

作者： Feby Artwodini Muqtadiroh Tsuyoshi Usagawa Diana Purwitasari Riris Diana Rachmayanti Eko Mulyanto Yuniarno Supeno Mardi Susiki Nugroho Mauridhi Hery Purnomo Dept. of Electrical Engineering Dept. of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Computer Science and Electrical Engineering Kumamoto University Kumamoto Japan Dept. of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Health Promotion and Behavioral Sciences Faculty of Public Health Universitas Airlangga Surabaya Indonesia Dept. of Electrical Engineering Dept. of Computer Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

The decision to open schools during a pandemic is considerable controversy because it increases the risk of a spike in COVID-19 cases more quickly. However, the decision to open schools is currently based solely on national policies, which generalize opening schools in every region, potentially leading to unintended consequences during the COVID-19 crisis. This study aims to present new decision-making to rank regions determining which schools are safe to open. By focusing on problem prioritization, we integrate the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) based on a fuzzy approach and the Urgency-Seriousness-Growth (USG) scoring technique, which we call FT-USG. First, we determine the weight of the expert's argument while putting special attention on the level of urgency, seriousness, and growth of COVID-19 to the school opening issue. The fuzzy number is then calculated based on the average weight generated from the USG result. Thus, the weighted normalization computations can be executed on each factor (criteria). Then, we calculate the rankings for each alternative using the Fuzzy TOPSIS method to determine which region and school can safely reopen. To provide a comprehensive ranking that serves as a basis for decision-making, we conduct a case study to show how the suggested approach is used to address the problems.

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Scalability in Autoencoder-based OFDM Communication System

Scalability in Autoencoder-based OFDM Communication System

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computer Vision and Machine Intelligence (CVMI), International Conference on

作者： Seizan Tsugawa Takao Toma Shiho Oshiro Tomohisa Wada Computer Science and Intelligent Systems Program University of the Ryukyus Okinawa Japan Magna Design Net Inc Okinawa Japan Information Technology Center University of the Ryukyus Okinawa Japan Dept. of Engineering Area of Computer Science and Intelligent Systems University of the Ryukyus Okinawa Japan

This paper proposed Scalability in Autoencoder-based Orthogonal Frequency Division Multiplexing(OFDM) communication system. In the previous research, only the comparison between IEEE802.11a and Autoencoder by the conventional OFDM communication system was performed, and it was proved that the communication system created by Autoencoder exceeded the performance of the conventional system. Therefore, in this paper, it uses IEEE802.11n and compare whether it can be improved by expanding the bandwidth and using ***802.11n standard has an FFT length of 128, a subcarrier number of 114 (108 for data), and modulation schemes of Quadrature Phase Shift Keying(QPSK), 16 Quadrature Amplitude Modulation (16QAM). The GI length is 800ns and the symbol length is 4000ns. In the simulation, a computer simulation was performed using a conventional OFDM communication system and a communication system generated by Autoencoder. Assuming that the simulation environment had an Signal-to-Noise Ratio (SNR) of 0 to 30 and an amplitude r of 0.0 to 1.0, the Symbol Error Rate (SER) status for each SNR was output. As a result of computer simulation, QPSK converged at SNR 27 ㏈ at IEEE 802.11a, but was able to reduce SER overwhelmingly to SNR 12 ㏈ at IEEE 802.11n. Also, in 16QAM the convergence at r=0.0 is the same as for SNR22 ㏈, but in IEEE 802.11a it does not converge after r=0.6, but in IEEE 802.11n it does not converge only at r=0.9 and r=1.0 . As a future task, it will use IEEE802.11ac, which enables communication speeds several times faster than IEEE802.11n, examine whether it is possible to further improve accuracy. And it will continue our research to correspond MIMO communication.

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Genetic Algorithm-Based Feature Selection for Accurate Breast Cancer Classification

Genetic Algorithm-Based Feature Selection for Accurate Breas...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shabbir, Attia Ali, Raja Hashim Shabbir, Muhammad Zeeshan Abideen, Zain Ul Khan, Talha Ali Ijaz, Ali Zeeshan Ali, Nisar Imad, Muhammad Bakar, Muhammad Abu Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Department of Electrical Engineering Lahore Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

Breast cancer is a significant global healthcare challenge, particularly in developing and underdeveloped countries, with profound physical, emotional, and psychological consequences, including mortality. Timely diagnosis and accurate treatment are crucial in addressing this issue. We propose the utilization of a feature selection technique to identify the most relevant features from among all features for breast cancer diagnosis, and show that Genetic Algorithms are impressive for this task. The study compares the results of GA with no selection and an alternative method, Principle Component Analysis (PCA). Three machine learning models, all based on supervised learning with data split into training and test data, are employed for binary classification using the selected feature subset. The evaluation metrics employed encompass accuracy, precision, recall, and F1-score. Among the selected models, Random Forest demonstrates the most favorable outcomes, achieving an accuracy score of 0.96, precision score of 0.96, recall value of 0.98, and an F1-score of 0.97. These results underscore the effectiveness of GA in feature selection for breast cancer diagnosis. Consequently, the integration of Genetic Algorithms (GA) with Random Forest showcases the superior performance among the evaluated models. © 2023 IEEE.

关键词： Genetic algorithms

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A Hybrid BCI for Robotic Device Navigation

A Hybrid BCI for Robotic Device Navigation

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Annual Conference on Information Sciences and systems (CISS)

作者： Yih-Choung Yu Hayden Fisher Angela Busheska Lily Thompson Lisa Gabel Dept. of Electrical and Computer Engineering Lafayette College Easton PA USA Children’s Hospital of Philadelphia Philadelphia PA USA Neuroscience Program Lafayette College Easton PA USA Dept. of Psychology & Neuroscience Program Lafayette College Easton PA USA

Applications of brain-computer interface (BCI) systems have grown in importance for assisting individuals with severe motor disabilities in navigating our increasingly technologically dependent society. With applications such as electric wheelchairs and advanced prosthetics in mind, the goal of this research is to develop a system that enables the use of electroencephalographic (EEG) and electromyographic (EMG) signals to control the movement of a robot. An EEG cap was used to obtain occipital alpha power density, frontal muscular artifacts, and sensorimotor mu rhythms, which were then sent back to a PC via Bluetooth for further processing. Signal-processing algorithms and models were developed and implemented to determine the user’s mental activity and send signals to the external physical device. The preliminary results from the pilot experiments were very promising. The algorithms will be implemented for real-time signal processing and tested with a BCI-controlled robotic device.

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Performance Evaluation of Transformer-based NLP Models on Fake News Detection Datasets

Performance Evaluation of Transformer-based NLP Models on Fa...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Raveen Narendra Babu Chung-Horng Lung Marzia Zaman Dept. of Systems and Computer Engineering Carleton University Ottawa Canada R&D Cistel Technology Ottawa Canada

Fake news has become a major concern due to its spread on social media. To combat this, various machine learning (ML) techniques have been proposed. However, there is a lack of research on the performance of transformer models using datasets from a wide range of domains. This paper investigates the performance of ML algorithms on three fake news datasets: LIAR, FNC-1 and Balanced Dataset for Fake News Analysis. Pretrained transformer language models such as BERT, RoBERTa, ALBERT and DistilBERT were chosen for this paper. The performance of the models was consistent across all datasets. RoBERTa obtained an accuracy of 69% when trained on the LIAR dataset, an 11% improvement over the existing traditional and deep learning ML model implementations, and an accuracy of 97% when trained on the FNC-1 dataset, proving to be the best-performing model across all the fake news detection datasets utilized in the experiments. DistilBERT trains at a significantly faster rate than the other three variants. The experimental results from the paper can help the research community to continue investigating and gain insights into fake news detection.

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Overview and Experimental Study of Piezolectric Energy Harvesters for Roadway Applications

Overview and Experimental Study of Piezolectric Energy Harve...

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Communications, Information, Electronic and Energy systems (CIEES), International Conference on

作者： Ioannis Bogatsis Nick Rigogiannis Charalampos Tsakiridis Anastasios Kyritsis Nick Papanikolaou Dept. of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece Dept. of PV Systems and DER Centre for Renewable Energy Sources and Saving Athens Greece

The continuous trend for transportation electrification imposes modern and innovative solutions in terms of hybridization and energy harvesting in roadway infrastructures (e.g., highways, charging points etc.). In this context, piezoelectric energy harvesters have emerged as a promising solution (combined with small battery energy storage units), for suppling low-power wireless sensor networks, traffic lights and indicators. This paper presents a literature review regarding piezoelectric energy harvesting technology, focusing on road transportation applications. Piezoelectric element types, materials and characteristics, power conversion techniques and applications are comprehensively studied and discussed. In parallel, an experimental assessment is performed in laboratory scale (emulating realistic conditions) and on the road, under realistic conditions. Experimental measurements indicate the applicability and suitability of piezoelectric energy harvesting in roadways and transportation infrastructure.

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Unsupervised Graph-Generative Network-based PV Condition Monitoring systems

Unsupervised Graph-Generative Network-based PV Condition Mon...

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IEEE General Meeting Power& Energy Society

作者： Sarah Allahmoradi Shahabodin Afrasiabi Xiaodong Liang Mousa Afrasiabi Jamshid Aghaei Chi Yung Chung Dept. Electrical and Computer Engineering University of Saskatchewan Saskatoon Canada Department of Electrical Engineering School of Energy Systems Lappeenranta University of Technology Lappeenranta Finland Department of Electrical and Computer Engineering The Hong Kong Polytechnique University Hong Kong China

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Condition monitoring plays a pivotal role in the photovoltaic (PV) system’s operation. To overcome issues in conventional PV condition monitoring, in this paper, an unsupervised graph-generative network-based PV condition monitoring system is proposed by considering Gaussian/non-Gaussian noises. The designed network has the ability to handle an imbalanced dataset and high dimensional signals, establish the correlation between time-varying signals in PV systems, and learn spatial-temporal features. The proposed system is validated through an experimental dataset by considering seven abnormal conditions, including PV array faults, PV control system faults, and grid faults. The proposed method appears to be effective under Gaussian/non-Gaussian noises with an accuracy higher than 98%. The superiority of the proposed graph-generative network is demonstrated by comparing with well-known deep- and shallow-based PV condition monitoring systems with 5%- 30% improvements in terms of accuracy and reliability.

关键词： Condition monitoring Photovoltaic systems Accuracy Correlation Noise Probabilistic logic Control systems Robustness Time-varying systems Monitoring

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