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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Dutta, Rajdeep Kumar Chhetry, Bishal Matam, Rakesh Barbhuiya, Ferdous Ahmed Indian Institute of Information Technology Dept. of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798350391725

The detection of reconnaissance attacks is crucial for safeguarding Internet of Things (IoT) environments, which are inherently more vulnerable and resource-constrained compared to traditional computing systems. Traditional centralised detection methods face significant challenges, such as privacy concerns and limited scalability due to the need to aggregate raw data on a central server. These issues become particularly pronounced in IoT environments where devices are diverse and geographically distributed. To address these challenges, we propose ReconGuard, a novel approach leveraging Model Contrastive Federated Learning (MCFL). ReconGuard enables collaborative training across multiple IoT devices without the necessity of sharing raw data, thereby preserving user privacy and enhancing data security. The approach integrates contrastive learning techniques, which improve the model's ability to discriminate between benign and malicious activities by contrasting positive (similar) and negative (dissimilar) data pairs. Our experimental results demonstrate that the ReconGuard-based detection system provides a scalable and privacy-preserving solution for identifying reconnaissance activities. These activities are often the initial step in more severe cyber threats, such as botnet attacks, which can lead to significant disruption and damage. By effectively detecting reconnaissance activities, ReconGuard enhances the overall cybersecurity framework of IoT environments. This research presents a viable and innovative method for improving cybersecurity in IoT systems, addressing the critical need for scalable, efficient, and privacy-preserving intrusion detection solutions. The use of MCFL in ReconGuard not only mitigates the challenges of data centralisation but also leverages the distributed nature of IoT networks to enhance robustness against cyber threats. © 2024 IEEE.

关键词： Contrastive Learning

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Image-Based IoT Malware Detection Method Using Deep CNNs 1

Image-Based IoT Malware Detection Method Using Deep CNNs

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Al-Musawi, Mariam H. Khammas, Ban Mohammed Dept. of Computer Networks Engineering College of Information Engineering Al Nahrain University Baghdad Iraq

ISBN: (纸本)9798331533557

The widespread deployment of Internet of Things (IoT) devices in various fields, such as agriculture and healthcare, has generated a vast amount of data, hence increasing the risk of data breaches. On the other hand, the demand for low-cost and easy-to-use smart products made them vulnerable to malware attacks, which aim to compromise or exploit IoT devices, resulting in damage and data loss. It has increased the necessity for effective and resilient strategies to safeguard these devices from malware threats. The study examines the feasibility of identifying IoT malware with several pre-trained deep convolutional neural network (CNN) models, such as AlexNet, VGG-16, VGG-19, InceptionV3, and MobileNet. The objective of this study is to employ various deep learning models to distinguish between malicious and benign images effectively. This study introduces a relatively new strategy that employs the chi-square goodness-of-fit method on an image dataset prior to model training, aiming to enhance performance and reduce the dataset size since it is noteworthy that this approach is not typically used directly on images. The suggested system's performance has been assessed using the IOT_Malware dataset based on standard performance metrics. The findings of this study indicate that VGG-19 and VGG-16 achieved the highest accuracy among the other models, with scores of 99.09% and 98.55%, respectively, demonstrating their perfect ability to produce the most accurate predictions, followed by MobileNet, InceptionV3, and AlexNet, which attained accuracies of 98.10%, 98.01%, and 97.65%, respectively. Moreover, this research paper presents a state-of-the-art evaluation to add to the system's reliability. © 2024 IEEE.

关键词： Convolutional neural networks

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Intelligent Behavior Recognition based on Gyroscope and Accelerometer

Intelligent Behavior Recognition based on Gyroscope and Acce...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Huang, Pin-Yu Lu, Yung-Han Lin, Chun-Ting He, Bo-Xian Pai, Tun-Wen Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324174

In an aging society, the rate of dementia would be dramatically increased. It is urgent to provide intelligent facilitative devices and appropriate medical care for those who with dementia symptoms, especially for the elders. Accordingly, the primary research target of this study is to automatically detect and monitor behavior of the elderly suffered from dementia. Although previous research has been conducted to identify various human movement patterns, the experimental data were all received from cameras. Furthermore, these related applications are not specifically designed to identify the behavior of the elderly with dementia. In order to improve previous research methods, our study adopted wearable multi-axis sensors to collect corresponding motion data with protected privacy. In addition to collecting raw data containing time series signals, frequency and spatial domain features were applied to detect human behaviors, and Random Forest algorithm was applied to construct a motion prediction model. The classification system for real-time motion recognition contained two main actions of users including holding/putting hand gestures and walking model. Finally, an additional camera on the chest was designed to be triggered and to take a shot according to previous settings. It could enhance practical applicability of the proposed intelligent system to assist the elderly with dementia to find her/his lost objects. This study developed an app for recording individual behaviors, checking the location and time of lost objects, and even recording activity information to provide healthcare professionals with decision-making suggestions for subsequent precision medical care. © 2023 IEEE.

关键词： Accelerometers

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A Multistage Cascaded Neural Network Model to Identify the Severity of Epileptic Attack

A Multistage Cascaded Neural Network Model to Identify the S...

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2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Simon, Martha Thampi, Lidiya Lilly Dept. of Computer Science College of Engineering Trivandrum India Indian Institute of Information Technology Kottayam Dept. of Computer Science Kottayam India

ISBN: (纸本)9798350376135

Epilepsy is a neurological disorder characterized by recurrent seizures, impacting the quality of life for millions of individuals worldwide. Timely and accurate detection of epileptic seizures is crucial for proper diagnosis and effective treatment. Electroencephalogram (EEG) signals provide vital information about brain activity and serve as a valuable tool for epilepsy detection. The methodology opted for this study was an enhanced multilayer feed forward neural network for automatic detection of epilepsy from EEG signals and hence predicting the severity. The authors build a multistage cascaded neural network model for achieving the same. Initially a Simple Artificial Neural Network (SimpleANN) is designed to classify EEG segments into seizure, healthy and interictal. Later a Multilayer Feed Forward Neural Network (MFFNN) is employed for detecting the severity of epileptic patients and hence can be classified into primary and secondary stages. The experimental results showed that the proposed SimpleANN classifier had provided noticeable results, and outperforms all tested classifiers. The study involves the collection of EEG data from a diverse group of patients, including those with epilepsy and healthy individuals. This automated epilepsy identifying system passes through different stages including decomposition, feature extraction for achieving a better performance model. The proposed system was tested and compared with SVM, KNN, MLP and Decision Tree under different measurement metrics. ANN has managed to give an accuracy of 80 - 90% which is significantly higher as compared to other machine learning models. The proposed work can automatically learn complex patterns from EEG data provides an advantage in identifying subtle seizure related features that may not be evident through manual analysis. Furthermore, the model offers real-time capabilities, making it suitable for continuous monitoring of patients at risk of seizures. © 2024 IEEE.

关键词： Electroencephalography

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Benchmarking Network Functionality: Performance Evaluation of SDN Controllers on Different Network Functions 100

Benchmarking Network Functionality: Performance Evaluation o...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Monir, Md Fahad Fawad Hasan, Azwad Hoque, Md Mozammal Ahmed, Tarem Granelli, Fabrizio Independent University Dept. of Computer Science and Engineering Bangladesh University of Houston Cullen College of Engineering TX United States Dept. of Information Engineering and Computer Science Trento Italy

ISBN: (纸本)9798331517786

Network Softwarization revolutionizes infrastructure by introducing programmable network functions, leading to scalable, agile, and efficient networks. This innovation facilitates adaptable, secure Next Generation Networks, transforming connectivity. This study conducts a comparative analysis of SDN-based network functions utilizing three different controllers: Open vSwitch (OVS), OpenFlow Reference controller, and RYU, to understand their impact on packet loss and jitter. The OpenFlow Reference controller, primarily used for educational and basic experimental purposes, serves as a baseline. The OVS controller, operating within the Open vSwitch framework in Mininet, offers a practical level of network management capability, more advanced than the OpenFlow Reference controller yet not as comprehensive as the RYU Controller, a production-grade SDN controller. Our research aims to fill the gap in comparative performance analysis of these controllers, particularly in hybrid round-robin-based load balancing and firewall implementations. Recognizing the operational reality of SDN deployments, we explore both reactive and proactive modes, acknowledging that real-world applications like firewalls and load balancers often operate proactively with pre-populated rules, affecting data plane performance. The findings provide insights into each controller's effectiveness, with OVS and RYU showing enhanced capability in reducing packet loss and jitter compared to the OpenFlow Reference controller. This research not only sheds light on the functional behaviors and performance trade-offs of these controllers but also contributes to the broader SDN and NFV discourse, offering a foundation for future academic and practical advancements in the field. © 2024 IEEE.

关键词： Packet loss

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Active Element Arrangement and Prediction Model for Channel Estimation in IRS-Assisted Systems

Active Element Arrangement and Prediction Model for Channel ...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Tsuchiya, Yoshihiko Suga, Norisato Uruma, Kazunori Fujisawa, Masaya Tokyo University of Science Dept. of Information and Computer Technology Tokyo Japan Shibaura Institute of Technology Dept. of Information and Communication Engineering Tokyo Japan Kogakuin University Dept. of Computer Science Tokyo Japan

ISBN: (纸本)9798350350821

Intelligent reflecting surfaces (IRS) that can dynamically control the phase of radio waves and reflect them are attracting attention to realize non line-of-sight communication in the high-frequency band. Channel state information (CSI) is estimated to properly control the phase, however the overhead of the estimation reduces communication efficiency. To reduce the overhead, IRS systems with active elements that perform channel estimation are considered. One approach treats the partial CSI obtained from the active elements as an image and utilizes a super-resolution technique to predict the entire CSI. However, because the active elements are arranged in grid, prediction is difficult when the active elements spacing and the CSI fluctuation period match. In this study, we propose a new arrangement that gathers the active elements in the center of the IRS. Furthermore, we regard prediction as an image-inpainting problem that recovers missing pixels from known pixel values, and we propose applying the U-Net to solve this problem. Numerical experiments show that the proposed method outperforms conventional methods in prediction accuracy and spectral efficiency. © 2024 IEEE.

关键词： Channel estimation

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Improving Workstation Ergonomics using Posture Detection for Corporate Professionals 3

Improving Workstation Ergonomics using Posture Detection for...

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3rd IEEE International Conference on Blockchain and Distributed Systems security, ICBDS 2024

作者： Shah, Aesha Bhut, Dev Prajapati, Mansi Shah, Vatsal Mangrulkar, Ramchandra Narvekar, Meera Dj Sanghvi College of Engineering Dept. of Computer Engineering Mumbai India Dj Sanghvi College of Engineering Dept. of Information Technology Mumbai India

ISBN: (纸本)9798350354348

Extended periods of sitting in corporate environments have been known to lead to a lot of health concerns, including lower back aches and neck strain. This sedentary lifestyle not only put employees' well-being at risk but also hinders overall work efficiency and job satisfaction. These detrimental effects of prolonged sitting necessitate an intervention to mitigate these issues and enhance workplace health. The proposed application seamlessly integrates into the work environment by using camera input from the device's webcam and MediaPipe Pose for posture detection and correction. It delivers personalized prompts and reminders, fostering user awareness and enabling self-improvement. Through this comprehensive approach, which includes promoting breaks to address the detrimental effects of prolonged sitting, this solution creates a healthier and more productive workplace. Moreover, the application prioritizes user privacy by performing all computations locally on the user's device. This on-device processing not only safeguards privacy but also allows for uninterrupted operation even in the absence of an internet connection. © 2024 IEEE.

关键词： Job satisfaction

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Revolutionizing Mango Crop Health: A Deep Learning Approach for Precise Identification of Leaf Diseases 10

Revolutionizing Mango Crop Health: A Deep Learning Approach ...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Navadharani, N. Mouthami, K. Devipriya, R. Anandamurugan, S. Kpr Institute of Engineering and Technology Dept. of Computer Science Engineering Coimbatore India Kongu Engineering College Dept. of Information Technology Erode India

ISBN: (纸本)9798350384369

Plant diseases pose significant challenges to the agricultural sector, leading to decreased crop production and financial losses. Mango, being a popular fruit worldwide, is susceptible to various leaf diseases. Traditional diagnosis of these disorders requires labor-intensive and time-consuming manual picture interpretation. To address this issue, deep learning models have been utilized to enhance the precision and effectiveness of identifying leaf illness in mango crops. In this study, ResNet50, ResNext50, Convnet, and Conventv2 deep learning models were compared to find the most successful approach. The results demonstrated that the Conventv2 model achieved the highest accuracy of 98.21%. The major aim of the research work is improvising sustainable agricultural practices by leveraging advanced technology for accurate plant disease classification. The findings have implications for improving mango crop management and ensuring a more sustainable and efficient agricultural industry. © 2024 IEEE.

关键词： Fruits

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Multisport Dynamics Explored Through Advanced Activity Recognition Techniques in Athletic Performance 8

Multisport Dynamics Explored Through Advanced Activity Recog...

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Salian, Supriya Preethi Salian, K. D'Souza, Ria Monterio, Rencita Fernandes, Rinette Veronica Almeida, Riya D. St. Joseph Engineering College Vamanjoor Dept. of Computer Science and Engineering Mangaluru India Dept. of Information Science and Engineering Udupi India

ISBN: (纸本)9798350350593

Recognition of human activities has drawn a lot of interest lately in the field of computer vision and machine learning. Group activity recognition is a significant subcategory in which several people participate in a common activity. The primary obstacle in these tasks is learning the relationships between individuals in a scene and understanding their evolution over time. The suggested study offers a new taxonomy to classify state-of-the-art (SOTA) group activity recognition approaches and subcategorizes the current literature. It also critically analyzes these techniques. To understand scenes involving multiple people, models must describe individual actions in context and infer collective activities. Accurately capturing relationships between actors and performing relational reasoning is crucial for comprehending group activities. However, modelling these relationships is challenging due to the limited availability of interaction information, relying only on individual action labels and collective activity labels. Inferring relationships from other aspects is thus essential. Group activity recognition has garnered increased research attention recently due to its importance in video understanding. The difficulties lie not only in recognizing individual actions but also in exploring scene information and collaborative relations among people. This research addresses these challenges by providing a comprehensive review of existing techniques, offering a structured approach to categorize them, and highlighting the importance of relational reasoning in understanding group activities. © 2024 IEEE.

关键词： Active learning

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EMC and EMF Safety Aspects of Wireless Power Transfer Systems for e-Mobility

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IEEE Electromagnetic Compatibility Magazine 2024年第3期13卷 74-92页

作者： Maradei, Francesca Ahn, Seungyoung Campi, Tommaso Covic, Grant Cruciani, Silvano Hirata, Akimasa Wang, Shuo Feliziani, Mauro Sapienza University Dept. of Astronautic Electrical and Energy Engineering Rome Italy Daejeon Korea Republic of The University of Auckland Auckland New Zealand Tor Vergata University Dept. of Industrial Engineering Rome Italy Nagoya Institute of Technology Dept. of Electrical and Mechanical Engineering Nagoya Japan University of Florida Dept. of Electrical & Computer Engineering GainesvilleFL United States University of L'Aquila Dept. of Industrial and Information Engineering L'Aquila Italy

This paper intends to provide a comprehensive over-view of the current state-of-the-art practices in wireless power transfer (WPT) technology within the automotive sector, while addressing pertinent issues related to electromagnetic compatibility (EMC) and electromagnetic field (EMF) safety compliance, as well as highlighting ongoing challenges and areas for further exploration. © 2012 IEEE.

关键词： Inductive power transmission

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