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Optimizing Privacy-Accuracy Trade-Off in IoT Intrusion Detection: An Analysis of FedAvg and FedProx with Differential Privacy

Optimizing Privacy-Accuracy Trade-Off in IoT Intrusion Detec...

引用

IEEE International WIE Conference on Electrical and computer engineering (WIECON-ECE)

作者： Sayeda Suaiba Anwar Mohammed Moshiul Hoque Dept. of Computer Science and Engineering Chittagong University of Engineering & Technology East Delta University Chattagram Bangladesh

ISBN: (数字)9798331535476

ISBN: (纸本)9798331535483

Ensuring the privacy and accuracy of interconnected network devices is crucial in the Internet of Things (IoT) landscape, especially when deploying Intrusion Detection Systems (IDS) on devices with constrained resources. This study offers a method to improve intrusion detection in IoT environments by combining Federated Learning with Differential Privacy. The proposed approach builds an efficient intrusion detection model by fusing Bidirectional Long Short-Term Memory (BiLSTM) with Convolutional Neural Networks (CNN). We evaluate this model on the Bot-IoT dataset, meticulously curated by the University of New South Wales (UNSW). This work aims to balance high detection accuracy with robust privacy protection, a crucial yet often neglected aspect of IoT security. We use federated learning to distribute intrusion detection tasks across various IoT devices while protecting data privacy, using differential privacy algorithms to assess and limit information leakage. This study evaluates two aggregation strategies, Federated Averaging (FedAvg) and Federated Proximal (FedProx), and examines how varying epsilon values and noise multipliers affect model accuracy. The findings indicate t hat a noise multiplier o f roughly 0.5 yields a steady accuracy curve, with FedProx achieving higher accuracy than FedAvg.

关键词： Differential privacy Privacy Accuracy Federated learning Computational modeling Noise Intrusion detection Data models Internet of Things Convolutional neural networks

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Estimating Surface Soil Moisture based on Remote Sensing Data and Crop Yield Classification using Machine Learning

Estimating Surface Soil Moisture based on Remote Sensing Dat...

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International Conference on Communication and Electronics Systems (ICCES)

作者： G. Anuradha Maddi Susrith Kondreddy Gnana Siddhardha Dept. of computer science and engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India Dept. of Artificial Intelligence and Data Science Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Soil moisture plays a critical role in environmental monitoring and sustainable agricultural practices due to its significant impact on crop growth, water availability, and overall ecosystem health. Properly understanding and managing soil moisture is vital for efficient water usage, especially in agriculture. Traditional methods in measuring soil moisture have been confined to ground-based small-scale methods, which are high price due to high labor intensity and limited spatial coverage. This project addresses its limitation in the spatial view by integrating remote sensing data from the Sentinel-1 satellite. Sentinel-1 is particularly effective in monitoring the soil moisture as it captures data in all climate conditions. By combining the satellite data with Google Earth Engine (GEE) we create a soil moisture dataset. For analyzing the joint satellite and ground truth data, the machine learning technique using Random Forest (RF) is applied and classification of crop yield is done.

关键词： Earth Satellites Soil measurements Soil moisture Surface soil Sentinel-1 Moisture Sensors Remote sensing Crop yield

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HALDA: An Adaptive IoT Algorithm for Reducing Water Losses in Jordanian Homes

HALDA: An Adaptive IoT Algorithm for Reducing Water Losses i...

引用

Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Mohammad Abu Arqoub Eman Alduweib Waseem Alromema Ali A. Titinchi dept. of Computer Science University of Petra Amman Jordan dept. of Computer Science and Information Taibah University Medinah Saudi Arabia dept. of Electrical and Computer Engineering University of Nizwa Nizwa Sultanate of Oman

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Water is considered an extremely valuable commod-ity in Jordan, which happens to be among those countries of the world that have the lowest water availability per capita. This paper introduces the Hybrid Adaptive Leak Detection Algorithm (HALDA), an IoT-based framework adapted for resi-dential water management in resource-constrained environments. HALDA integrates real-time sensing, adaptive thresholding, and predictive analytics into leakage detection and mitigation at the household level. The system, by continuously sending notifications related to data-driven analysis, helps lower not only acute water wastage events, but also encourages better pro-sustainable habits among home holders. The accuracy results for the experiments in detecting accurate detection and power draw from ten conducted field trials all over Amman was as highly as 93 %, with each sensor as well as being that low at each 50 mW, reducing widely previously undetected leaks. Results stress the fact that HALDA is one of the few promising approaches toward finding a cost-effective and energy-efficient solution to the water scarcity problem of Jordan and many other arid and semi-arid regions of the world.

关键词： Accuracy Data integrity Urban areas Water conservation Predictive models Real-time systems Blockchains Leak detection Predictive analytics Water resources

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Variational Perturbation Personalized Federated Learning via Prior-Posterior Distance

Variational Perturbation Personalized Federated Learning via...

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

作者： Hefeng Zhou Yuanbin Wang Jun Wang Jiong Lou Wugedele Bao Chentao Wu Jie Li Dept. of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Dept. of Electronic Engineering Shanghai Jiao Tong University Shanghai China Dept. of Computer Science University of Warwick Coventry United Kingdom School of Computer Science Hohhot Minzu College Hohhot China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Personalized Federated Learning (pFL) mitigates the impact of statistical heterogeneity on FL architecture to some extent by allowing participants to use personalized models based on local data distributions. The existing pFL methods optimize from the perspective of model structure, attempting to adopt strategies that maintain model processing or quickly adapt to local data distribution capabilities. Our proposed method draws inspiration from the concept of variational inference, guiding model updates by comparing prior and posterior data distributions, and innovatively applying model variational perturbations to improve robustness. Finally, we conducted multidimensional experiments and the results show that our method outperforms the current baseline. Code: https://***/RezinChow/VPFL.

关键词： Adaptation models Codes Federated learning Perturbation methods Current measurement Signal processing Data models Robustness Speech processing Optimization

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Image Degradation in Time Due to Interacting Hot Pixels 36

Image Degradation in Time Due to Interacting Hot Pixels

引用

36th IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems, DFT 2023

作者： Chapman, Glenn H. Coelho Silva Menes, Klinsmann J. Wu, Li-Yu Koren, Israel Koren, Zahava Simon Fraser University School of Engineering Science BurnabyV5A 1S6 Canada University of Massachusetts Dept. of Electrical and Computer Engineering AmherstMA01003 United States

ISBN: (纸本)9798350315004

The number of hot pixels (induced by cosmic rays) in digital imaging sensors increases as the camera ages, with rates strongly dependent on pixel size. This increasing number of defects results in a higher probability that two defects will occur within a 5x5 pixel square. When this occurs, the currently employed color demosaicing and JPEG image compression algorithms spread the damage into a 16x16 pixel area, creating significant color changes resulting in noticeable image degradation. Experiments show, for example, that a 20 Mpixel DSLR camera needs only 127 hot pixels to generate a P=4.6% probability of an image degradation in 1.4 years, and P increases with the time squared. In addition, this probability grows inversely with the pixel size squared. We show that the time for a given probability of image degradation varies linearly with the square of the pixel size, inversely with both the square root of the imager area and the ISO. This accelerated image degradation in time will have a significant impact on the lifetime of cameras that are expected to provide accurate images for a long period of time, e.g., cameras in self-driving cars. Experimental tests have identified multiple interacting hot pixels in several cameras. © 2023 IEEE.

关键词： Cameras

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Classification of Functional and Nonfunctional Hand Movement Using Deep Learning and First-Person View Video

Classification of Functional and Nonfunctional Hand Movement...

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2023 Congress in computer science, computer engineering, and Applied Computing, CSCE 2023

作者： Tran, Tan Chang, Lin-Ching Lum, Peter S. Dept. of Electrical Engineering and Computer Science The Catholic University of America WashingtonDC20064 United States The Catholic University of America Dept. of Biomedical Engineering WashingtonDC20064 United States MedStar National Rehabilitation Network WashingtonDC20064 United States

ISBN: (纸本)9798350327595

Upper extremity (UE) rehabilitation of motor function is needed post-stroke. Although UE rehabilitation is widely used in practice, it is difficult to evaluate the effectiveness of UE treatments. There are various approaches to evaluate UE motor function such as performing tasks in the clinician's office, patient's self-report, or wearable devices equipped with accelerometer/gyro sensors to analyze the data captured from stroke survivors. Our previous study reported that using a Random Forest machine learning algorithm with accelerometry sensor data obtained by single wrist-worn sensor, it is possible to differentiate between functional and non-functional UE movements in stroke survivors with good accuracy. However, to build the model, a third-person view video was captured and manually annotated by experts to provide ground truth labels for training. This task is very time-consuming and subjective. In this study, we propose a framework that uses deep learning (DL) to classify functional and non-functional hand movement in videos captured from a wearable camera (i.e., first-person view videos). The proposed system is fully automated and consists of two DL networks. The first DL network performs hand pose estimation from a 2-second sequence of frames which extract pre-defined key points on the hands. The second DL network then takes these key points as input and classifies the sequence of frames to either functional or non-functional movement. The proposed system has two major impacts on rehabilitation. First, it can provide initial annotations on the frames of videos that can significantly reduce the time required for human annotations. Second, analyzing the effectiveness of UE rehabilitation using the first-person view videos enables us to measure the outcomes of UE treatments in stroke survivors' homes. In addition, the system can deliver timely feedback to stroke survivors that has the potential to increase the use of the affected UE in the community. © 2023 IEEE

关键词： deep learning - Regular Research Paper rehabilitation stroke upper extremity

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Detection of Bullying Text: A Multi-faceted Approach using Machine Learning and Natural Language Processing

Detection of Bullying Text: A Multi-faceted Approach using M...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Kotyada Mohan Kiran Kumar Karthik Ullas Vivek Srikar Reddy Munaga Sai Snehitha Mahadevi E Malkhed K Dinesh Kumar Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Electronics and Computer Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

With the dawn of the new age of communication, i.e., social media, the world has come into the palms of every individual at every corner of the world. However, easier access to messaging implies easier forms of abuse. As the usage of social media increases, the other side of communication, bullying, also increases. Cyberbullying refers to the concept of harassing individuals or groups through means of the internet with the help of messages or multimedia. Cyberbullying has a huge impact on mental health and research is ongoing in this area to accurately detect bullying text. Existing models are embedded into social media platforms to either ban or apply sanctions on the users involved in the passage of such materials. The proposed study aims to develop a model with a similar approach to detect cyberbullying in comment text. Also, sentiment analysis is performed on the comment text to get an idea of the message’s context, thus improving the chances of bullying detection. The work is divided into two stages. In the first stage, the text and the sentiment score are directly passed to the machine learning models. Later, during the second stage word embedding using Word2Vec, a natural language processing technique, is performed on the comment text to obtain vectorized features which are then given to the machine learning models along with sentiment features. It was observed that the latter model shows high levels of success and that it can be deployed in real-world applications.

关键词： Sentiment analysis Instruments Employment Cyberbullying Machine learning Mental health Real-time systems

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Topic Relation Graph Construction to Identify Challenges in Introducing Programming in Indonesian K-12 Education 1

Topic Relation Graph Construction to Identify Challenges in ...

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1st IEEE International Conference on Smart Technology, ICE-SMARTec 2023

作者： Ayub, Mewati Karnalim, Oscar Toba, Hapnes Wijanto, Maresha Caroline Dulimarta, Hans Saputra, Williyandie Kotambunan, Tabitha Emanuella Master Program of Computer Science Maranatha Christian University Bandung Indonesia Ewha Womans University Dept. of Computer Science and Engineering Seoul Korea Republic of School of Computing Grand Valley State University MI United States Informatics Engineering Maranatha Christian University Bandung Indonesia

ISBN: (纸本)9798350306156

Programming can help K-12 students to develop their 21st-century core skills. Despite the benefits, programming is not common to be delivered in Indonesian K-12 education. There is a need to understand potential challenges in introducing programming to K-12 students. We developed a questionnaire survey covering four identified dimensions of challenges: administrative, facilities, teachers, and students. We also asked about common programming assessments and their preferred software features for teaching programming. Forty K-12 teachers were invited to complete the survey. The responses were analyzed with thematic analysis using a bigram-based Latent Dirichlet Allocation topic modeling and descriptive statistics. Our study shows that the challenges include limited learning modules, an insufficient number of computers, limited programming skills, and limited computational thinking skills. Scratch was the most common programming language used and many programming assessments were about debugging a program or writing a small program. Visualization and animation can be helpful in teaching programming. © 2023 IEEE.

关键词： Students

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Multi Layered Defense Architecture for DDoS Protection in Private Cloud Environments

Multi Layered Defense Architecture for DDoS Protection in Pr...

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IoT, Communication and Automation Technology (ICICAT), International Conference on

作者： Shashwat Singh Anubha Sharma Shani Soni Ayushi Prakash Dept. of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad Uttar Pradesh India

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

Private cloud solutions have increasingly been adopted because of the demand from organizations for enhanced data security, control over regulatory compliance, and IT infrastructure has been rising. Although public cloud services offer built-in security solutions, private clouds must possess a custom built architectures to address critical challenges, including Distributed Denial of Service (DDoS) attacks, among *** are often deployed behind private cloud environments without the benefits of prebuilt SaaS based DDoS protection. In this exposition, we present an architecture for multilayer protection against DDoS attacks in private cloud environments, which includes the following key components: Global Load Balancer, Traffic Scrubbing Center, Signature based IDPS, and Command and Control Center. Further, we also present a custom Command and Control Center, which utilizes CNNs for runtime handling of network traffic flow, based on real-time monitoring and activity patterns, to mitigate the menace of DDoS. Experimental results show that the architecture is efficient in filtering volumetric and application layer DDoS attacks, ensuring a malicious packet drop rate greater than 95%, while maintaining latency below 150 ms.

关键词： Command and control systems Cloud computing Runtime Prevention and mitigation Telecommunication traffic Organizations Denial-of-service attack Nonhomogeneous media Real-time systems Protection

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Lite-Agro 2.0: Integrating Federated and TinyML in Pear Disease Classification IoAT-Edge AI 9

Lite-Agro 2.0: Integrating Federated and TinyML in Pear Dise...

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9th IEEE International Symposium on Smart Electronic Systems, iSES 2023

作者： Dockendorf, Catherine Mohanty, Saraju P. Mitra, Alakananda Kougianos, Elias University of North Texas Dept. of Computer Science and Engineering United States Institute of Agriculture and Natural Resource University of Nebraska-Lincoln United States University of North Department of Electrical Engineering TX United States

ISBN: (纸本)9798350383249

The success of Tiny Machine Learning (TinyML) is dependent on the process of the model training, along with optimized algorithms and quality image dataset. In the process of Pear disease identification through tree leaf analysis, the need to protect the privacy of protected data has led to the topic of Federated Learning (FL). Prior Lite-Agro 1.0 study aims to apply Neural Network trained Deep Learning models on lightweight MCUs. With this second Lite-Agro 2.0 expansion, we explore different models, the integration of Federated Averaging (FedAvg) algorithm and explore decentralized training of models on multiple client devices with an emphasis on preserving data privacy. In federated learning, the data does not leave the client device and individual weights are passed on and aggregated onto a global server which then generates a global model. This federated learning research benchmarks frameworks such as Flower. Dev, model performance, and tabulates training values such as accuracy and loss given various FedAvg algorithms. © 2023 IEEE.

关键词： Cyber Physical System

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