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Developing xApps for Rogue Base Station Detection in SDR-Enabled O-RAN

Developing xApps for Rogue Base Station Detection in SDR-Ena...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Huang, Jun-Hong Cheng, Shin-Ming Kaliski, Rafael Hung, Cheng-Feng National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan National Sun Yat-sen University Dept. of Electrical Engineering Kaohsiung Taiwan

ISBN: (纸本)9781665494274

In order to support the diverse requirements of 5G communications, a multitude of RAN components are required. To enable multiple vendor support for 5G, each of whom can independently choose components, Open-RAN (O-RAN) defined a set of standards to which the components must adhere. In addition, O-RAN defines the management elements used to manage each component to secure the 5G networks. While the proposed architecture can manage both 4G and 5G environments, including 5G NSA (Non-Standalone), it inherently suffers from the same vulnerabilities found in 4G LTE. Consequently, an attacker can use unprotected signaling and a low-cost Software Defined Radio (SDR) to launch rogue base station (RBS) attacks on the user equipment (UE), even in O-RAN architectures. In this paper, we consider the stability of signals collected from high-quality operational BSs versus cheap RBSs. Using signal stability features, we develop a machine learning (ML) based RBS detector located on the UE. With the aid of an O-RAN xAPP, ML models can be retrained using the data collected from multiple UEs, and the updated model can be delivered to UEs to enable higher detection accuracy. We conduct extensive experiments by implementing an RBS using a USRP B210, enabling O-RAN using E- Release, and data collected from operational BSs. Moreover, the detector is implemented as an Android APP, which realizes the connection to the O-RAN xAPP. The experimental results show that our detector can achieve more than 99% accuracy, precision 'recall' and F1 score. © 2023 IEEE.

关键词： Base stations

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Faster R-CNN in Healthcare and Disease Detection: A Comprehensive Review

Faster R-CNN in Healthcare and Disease Detection: A Comprehe...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Tian, Jiawei Lee, Seungho Kang, Kyungtae Major in Bio Artificial Intelligence Hanyang University Dept. of Computer Science and Engineering Ansan-si Korea Republic of Hanyang University Dept. of Applied Artificial Intelligence Ansan-si Korea Republic of Hanyang University Dept. of Artificial Intelligence Ansan-si Korea Republic of

ISBN: (纸本)9798331510756

This review examines the applications, challenges, and prospects of Faster Region-based Convolutional Neural Networks (Faster R-CNN) in healthcare and disease detection. Through a meta-analysis of Web of science literature from 2017 to 2024, we provide insights into the evolving landscape of Faster R-CNN in medical contexts. The algorithm can be applied to medical image analysis across various modalities, including radiography, computed tomography, magnetic resonance imaging, ultrasound, microscopy, and endoscopy. Its applications extend to optical RGB images for dermatological and surgical uses, as well as broader healthcare areas such as posture detection, medication recognition, and assistive technologies. Despite its success, Faster R-CNN faces challenges in handling subtle abnormalities, addressing class imbalance in medical datasets, ensuring result interpretability, and managing patient privacy. Our analysis reveals dominant application areas, technological advancements, and integration trends with other artificial intelligence technologies. The review highlights Faster R-CNN's significant impact on improving diagnostic accuracy and healthcare delivery while acknowledging the need for continued research to address limitations. Emerging directions include real-time disease detection and advancements in personalized medicine, underscoring Faster R-CNN's potential to further transform healthcare practices and patient outcomes. © 2025 IEEE.

关键词： computerized tomography

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Leg Movement Controlled Robotic Leg for Menace Preclusion

Leg Movement Controlled Robotic Leg for Menace Preclusion

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2024 IEEE SmartBlock4Africa, SmartBlock4Africa 2024

作者： Sitti, Mensah Antwiwaa, Anita Danquah-Amoah, Archibald Govindan, Kannan Martey, Ezekiel Mensah University of Mines and Technology Dept of Computer Science and Engineering Tarkwa Ghana All Nations University College Dept. of Electronics and Communication Engineering Koforidua Ghana Accra Technical University Dept. of Medical Laboratory Accra Ghana University of Health and Allied Sciences Dept. of Orthotics and Prosthetics Ho Ghana

ISBN: (纸本)9798350376838

Movement of autonomous humanoid is a key area, as we gradually accept robots. This research proposes an 'Artificial Human Leg Model' which can precisely follow the real movements of human leg by means of image processing thereby avoiding sensor noise and damage and providing more adjustable response. Webcams are used to capture the leg images followed by Morphological image processing to extract information from human leg movement. The four degrees of freedom implemented in the proposed method are rotation of base, leg like motions, knee like motions, and steps motions. The robot leg is controlled successfully using human leg movement, where the system can travel a distance, manoeuvre in an uneven surface and move the robot from one place to other with precision. The user can control the leg entirely based on his/her intentions to accomplish the required task. The proposed real-time method of implementing human leg movement also ensures immediate control and performance. Distant control of the robotic leg does not only ensure complete safety to human, but also allows flexibility in controlling distant objects. The overall goal of the proposed system is to guarantee safety, flexibility and accuracy in movements when performing sensitive tasks in day-to-day life activities thus offering suitability for menace preclusion. © 2024 IEEE.

关键词： Motion capture

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Predictive Scaling of Elastic Pod Instances for Modern Applications on Public Cloud through Long Short-Term Memory. 1

Predictive Scaling of Elastic Pod Instances for Modern Appli...

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1st International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2022

作者： Bharanidharan, G. Jayalakshmi, S. Mayilvahanan, P. Dept. of Computer Science Vistas Pallavaram Chennai India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665497107

In Cloud Computing (CC) environment, container virtualization through Docker engine is bringing the new digital transformation in the enterprise multi-tier application architecture in this modern era. Elastic pod containers attracts and helps the application developers in developing and executing the cloud-native modern applications with key benefits such as light weight, agility to launch, easy deployment through images, consuming less power, minimum cost, less carbon footprints with increased resource utilization and provisioning on public cloud data centers. In existing, reactive auto-scaling mechanism of pod resources is used to add or remove resources manually or rule based for handling static workloads from users and most of the times it may lead to over provision or under provisioning of instances that violates QOS and SLA. In this paper, predictive horizontal scaling of pods utilizing custom metrics with orchestration through Kuberenetes is proposed with LSTM (Long Short-Term Memory) based on Deep Learning (DL) technique. DL is data hungry for right prediction of replicas of a cluster that is needed in advance to run cyclic workloads and to handle the sudden spike of demand by using the cloud large dataset real time traces. Moreover, LSTM model is compared with GRU (Gated Recurrent Unit) and experimental results shows that the results of LSTM prediction has less absolute error rate on comparing with GRU to keep the resource provisioning accuracy better for running the modern workloads seamlessly on public cloud. © 2022 IEEE.

关键词： Long short-term memory

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A Comparative Study on Keyword Extraction and Generation of Synonyms in Natural Language Processing

A Comparative Study on Keyword Extraction and Generation of ...

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Advances in Power, Signal, and Information Technology (APSIT), International Conference in

作者： Rasmi Rani Dhala A.V.S Pavan Kumar Soumya Priyadarsini Panda Dept. of Computer Science and Engineering GIET University Gunupur Dept. of Computer Science and Engineering SIT Bhubaneswar

Natural Language Processing (NLP) is an emerging field that aims to enable machines to understand and interpret human language. Keyword extraction and synonym generation are essential tasks in natural language processing. They play a significant role in information retrieval, text classification, and sentiment analysis. In this paper, we explore three different approaches to keyword extraction and synonym generation: rule-based model, statistical model, and extreme learning machine (ELM) model. We compare the performance of each method on a corpus of text and analyze the strengths and weaknesses of each approach. Our results show that the ELM model outperforms the other two methods in terms of accuracy and efficiency.

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Black Hole Attacks Detection in Mobile Ad Hoc Networks (MANETs) using Bayesian Inference Method 11

Black Hole Attacks Detection in Mobile Ad Hoc Networks (MANE...

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11th International Conference on electrical engineering, computer science and Informatics, EECSI 2024

作者： Afifah, Nurul Fadli, Muhammad Septian, Tri Wanda Sari, Winda Kurnia Hermansyah, Adi Marieska, Mastura Diana Sriwijaya University Faculty of Comp. Science Computer Engineering Dept. Palembang Indonesia Sriwijaya University COMNETS Research Group Faculty of Comp. Science Palembang Indonesia Sriwijaya University Faculty of Comp. Science Information System Dept. Palembang Indonesia

ISBN: (纸本)9798350355314

MANET has vulnerability in terms of security systems against black hole attacks. The main target of black hole as a malicious node is to absorbs all data. The aim of this research is to identify black hole attacks against MANE Tusing Bayesian inference. The methodology of this research involves several testing phases. In the first phase, all data packets sent by the source node to destination within the range of the black hole node are lost. The second and third testing phases showed that the Bayesian method was successful in detecting black holes by preventing data transmission. The results indicated that traffic in the MANET runs smoothly when the nodes are operating under normal conditions. However, when a black hole node is introduced, a significant number of data packets are lost. Implementing the Bayesian method offers a solution to this issue. © 2024 IEEE.

关键词： Mobile ad hoc networks

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Real-time Human Movement Recognition and Interaction in Virtual Fitness using Image Recognition and Motion Analysis 12

Real-time Human Movement Recognition and Interaction in Virt...

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12th International Conference on Awareness science and Technology, iCAST 2023

作者： Yeh, ChunHsiu Shen, Wei-Cheng Ma, Chi-Wei Yeh, Qiu-Tong Kuo, Chung-Wei Chen, Jong-Shin Dept. of Information Engineering Computer Science Feng Chia University Taichung Taiwan CollegeofComputerScience Jimei University Xiamen China Dept. of Information Communication Engineering ChaoYang University of Technology Taichung Taiwan

ISBN: (纸本)9798350324693

This research focuses on the application of image recognition, motion detection, and artificial intelligence techniques to achieve real-time identification and analysis of human movements in a dynamic manner. The primary goal is to establish a seamless association between captured human movements and corresponding actions performed by virtual avatars. The implementation involves the utilization of image processing methodologies from python's OpenCV and pygame libraries. MediaPipe technology is employed to capture users' images and facilitate accurate detection of specific movement patterns. By calculating the angles between users' limbs and torso, the correctness of certain movements can be assessed. The research is practically developed and applied to a simulated fitness system, where an interactive exercise game platform is devised. The system is characterized by its engaging user interface, enhancing the ease and enjoyment of fitness activities. The seamless integration of interactive virtual avatars and real users within physical activities serves to stimulate greater participation, particularly among the elderly. The amalgamation of image recognition and motion detection technologies presents opportunities for combining fitness and entertainment in diverse life scenarios, offering an enhanced experience of physical exercise and enjoyment. © 2023 IEEE.

关键词： Image recognition

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Classification Anterior and Posterior of Knee Osteoarthritis X-Ray Images Grade KL-2 Using Deep Learning with Random Brightness Augmentation

Classification Anterior and Posterior of Knee Osteoarthritis...

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2022 International Conference on computer engineering, Network and Intelligent Multimedia, CENIM 2022

作者： Supatman Yuniarno, Eko Mulyanto Purnomo, Mauridhi Hery Institut Teknologi Sepuluh Nopember FTI UMBY Dept. of Electrical Engineering Dept. of Informatics Surabaya60111 Indonesia Institut Teknologi Sepuluh Nopember Dept. of Electrical Engineering Dept. of Computer Engineering Surabaya60111 Indonesia

ISBN: (纸本)9781665476508

Osteoarthritis of the knee (KOA) is a narrowing of the joint space area (JSA) due to lack of fluid in the knee joint, resulting in pain when moving and when it is severe, the femur and tibia meet. Medical personnel and existing computer-based methods have been able to detect patients with X-rays but have not been able to detect where visually the posterior AJ is still wide while the anterior AJ is actually narrow, so that the patient still feels joint pain. A new approach is proposed for the classification of X-Ray Grade Kellgren-Lawrence (KL)-2 Osteoarthritis Initiative image datasets using Deep Learning Neural Networks (DCNN) by setting the Random Brightness Augmentation hyperparameter. The experimental results obtained X-Ray Grade KL-2 narrowing image classification 'Anterior View KOA' and 'Posterior View KOA' with training accuracy of 83.33% and validation accuracy of 54.69% at Random Brightness with a value of 30. © 2022 IEEE.

关键词： computer vision

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A Hybrid Approach for Multimedia Summarization

A Hybrid Approach for Multimedia Summarization

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2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022

作者： Grandhi, Manikanta Avinash Bhaskaran, Sreebha Amrita School of Engineering Bengaluru Dept. of Computer Science & Engineering Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9781665453424

Text summarization is the basic method in NLP (Natural Language Processing) application which makes the text data into a shorter version. All over the world a lot of multimedia data is generating which includes text, audio, video, image, etc., Generating the short summary from entire text will reduce our task of understanding the things. In the proposed work, film reviews data is taken to perform summarization. A film review is an article that reviews and analyses a film and is published in a magazine, newspaper, video, or scholarly journal. As a result, reading through the entire conversation will take a long time to comprehend the reviews and consider the film. To perform text summarization, we need to work on multi-media data to generate the short summary of the entire content. By scanning the images, audio, videos and converting them into text documents, multi-media data will reduce the difficulty for the machine to understand the important information from it. In this project, proposing a Hybrid (combination of Extractive summarization and Abstractive summarization) multi-media summarization method that uses NLP techniques, image processing, speech processing to retrieve the information contained in multimedia model. The proposed work includes the sequence of implementations namely, Multimedia processing, applying NLP techniques, Seq-to-Seq modelling to the text, and performing sentiment analysis using a Naive Bayes classifier. © 2022 IEEE.

关键词： Sentiment analysis

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Deep and Spatio-Temporal Detection for Abnormal Traffic in Cloud Data Centers

Deep and Spatio-Temporal Detection for Abnormal Traffic in C...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Yuan, Haitao Wang, Shen Bi, Jing Zhang, Jia School of Automation Science and Electrical Engineering Beihang University Beijing China Beijing University of Technology Faculty of Information Technology Beijing China Lyle School of Engineering at Southern Methodist University Dept. of Computer Science DallasTX United States

ISBN: (纸本)9798350337020

Current interactions of network traffic through cloud data centers have become an important process of network services. Precise and real-time detection and prediction of network traffic can assist system operators in effectively allocating resources, and assessing network performance based on actual service requirements, and analyzing network health. However, sources and distribution of network traffic are different, which makes accurate warnings of network attack traffic become a difficult problem. In recent years, neural networks have been proven to be effective in predicting time series data, particularly long short-term memory networks for capturing temporal features and convolutional methods for capturing spatial features. This work proposes a Deep Hybrid Spatio-Temporal (DHST) network method for abnormal traffic detection in cloud data centers, which combines a cooperative temporal convolutional network, an attention mechanism and a random inactivation method to capture the network traffic data's spatio-temporal features. It improves accuracy of abnormal traffic detection, and realizes classification of normal traffic and abnormal one. It achieves higher accuracy than typical detection methods when applied to a real-life dataset collected from Yahoo Webscope S5. © 2023 IEEE.

关键词： anomaly detection Deep learning network traffic spatio-temporal modeling temporal convolutional networks

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