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Satellite Communication Vulnerabilities and Threat Landscape

Satellite Communication Vulnerabilities and Threat Landscape

引用

2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Khare, Shanu Talwandi, Navjot Singh Chandigarh University Dept of Computer Science Engineering Punjab India Chandigarh University Dept of Apex Computer Science Engineering Punjab India

ISBN: (纸本)9798331540821

Satellite communication (satcom) has become an integral part of modern society, providing essential services in various sectors such as military, aviation, maritime, and emergency response. Despite its importance, satcom systems remain vulnerable to numerous threats that could result in significant consequences. This paper examines the vulnerabilities and threat landscape associated with satellite communication. Several factors contribute to the susceptibility of satcom systems, including their reliance on radio frequency signals, which can be easily intercepted, jammed, or spoofed. Additionally, satcom equipment and infrastructure may lack adequate security features, making them easy targets for cybercriminals. Potential adversaries include state-sponsored actors, hacktivists, terrorists, and criminal organizations seeking financial gain or causing chaos. These groups have varying levels of motivation, resources, and expertise, enabling them to develop sophisticated attack methods. Some common satcom exploitation techniques include signal jamming, eavesdropping, hijacking, and cyberattacks targeting ground stations, satellites, or user terminals. The impact of satcom vulnerabilities varies depending on the sector but generally includes service disruptions, loss of sensitive information, safety risks, and economic losses. To address these challenges, it's crucial to implement robust security measures, such as encryption, authentication, network segmentation, regular software updates, and employee training. Furthermore, international cooperation and collaboration between public and private entities are necessary to ensure effective threat intelligence sharing and incident response. © 2024 IEEE.

关键词： International cooperation

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Unravelling the Enigma of Polycystic Ovary Syndrome (PCOS): Using MLAlgorithms 2

Unravelling the Enigma of Polycystic Ovary Syndrome (PCOS): ...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Juneja, Sonam Bhati, Bhoopesh Singh Atwal, Shikha Anand, Monika Maiti, Souvik Chandigarh University Dept. of Computer Science and Engineering Gharuan India Iiit Dept. of Computer Science and Engineering Sonepat India

ISBN: (纸本)9798350371055

The difficulties and ramifications of PCOS, which affects a sizable portion of women who are of reproductive age, are discussed in this review. The article covers the various clinical manifestations of PCOS, how it affects both reproductive and non-reproductive health, and how it's linked to psychological distress and metabolic disorders. It highlights how critical lifestyle modifications, early detection, and accurate diagnosis are. Additionally, the study presents machine learning techniques for PCOS detection, demonstrating the effectiveness of techniques like Random Forests, CNN, and SVM. An innovative CDSS that uses the Red Deer Algorithm shows encouraging accuracy. The necessity of continued research, diversified datasets, and cooperative efforts to enhance PCOS detection at the nexus of technology and healthcare is highlighted in the abstract's conclusion. © 2024 IEEE.

关键词： Deep Learning KNN Machine Learning PCOS Random Forest SVM

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Architectural Model of Deep Convolution Neural Network Approach for Brain Tumor Prediction

Architectural Model of Deep Convolution Neural Network Appro...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Panigrahi, Raghunath Sahu, Rekha Nayak, Suvendu Chandan Kabat, Manas Ranjan IMIT Dept. of Computer Science and Engineering Cuttack India Silicon University Dept. of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350369175

Magnetic Resonance Imaging (MRI) is most commonly used technique to detect a tumor by physicians in medical science. The MRI images are three-dimensional images with the number of slices that are analyzed to identify the defective cells in the naked eye that leads to a time-consuming process. Machine Learning (ML) techniques are investigated as a solution to improve tumor identification in a better way. Early and accurate detection is crucial for effective treatment planning and improving patient outcomes. Traditionally, brain tumor diagnosis relies on manual analysis of MRI (Magnetic Resonance Imaging) scans by radiologists, which can be time-consuming and prone to human error. The advent of deep learning, particularly Convolutional Neural Networks (CNNs), has opened new avenues for automating and improving the accuracy of brain tumor prediction. In this paper, we study the Convolutional Neural Network(CNN) model which is popular in image analysis. Similar way, we develop a CNN model to process and classify the MRI images to detect the type of brain tumor. We examine the model's performance by using 5712 MRI images from a standard dataset available in the public domain. The aim of the work is to classify the tumor in four classes like: Glioma, Meningioma, Pituitary and No Tumour using the proposed CNN model. Our model follows data preprocessing, image encoding, filtering and classification. The proposed model scores 78% accuracy as its performance in the dataset which needs more investigations and comparison with other techniques in further study. © 2024 IEEE.

关键词： Convolutional neural networks

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SCHC over DtS-IoT: Performance of SCHC Confirmation Modes in Satellite IoT 10

SCHC over DtS-IoT: Performance of SCHC Confirmation Modes in...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Munoz-Lara, Rodrigo Cespedes, Sandra Diaz, Marcos University of Chile Dept.of Electrical Engineering Santiago Chile Concordia University Dept. of Computer Science and Software Eng. MontrealQC Canada

ISBN: (纸本)9798350373011

Static Context Header Compression and Fragmentation (SCHC) is a standard defined as an adaptation layer for supporting IPv6, UDP, and CoAP protocols in low power wide area network (LPWAN) technologies. SCHC has fragmentation modes that use confirmation mechanisms to deliver reliable communications. Direct-to-satellite Internet of Things (DtS-IoT) directly communicates between end devices and the satellite in an IoT environment. If SCHC is implemented in a DtSIoT environment, applications using IPv6 could have global coverage through the Internet of Things (IoT). Unfortunately, it is not known whether SCHC can operate with the current acknowledgement mechanisms in a DtS-IoT environment, and if so, it is not known which one performs better. This work evaluates SCHC acknowledgement modes for ACK-on-Error fragmentation mode, including the new SCHC Compound ACK mode. It determines which mode has the lowest transfer delay for different LoRa data rates and frame error rates in a DtS-IoT environment with LEO satellites. © 2024 IEEE.

关键词： Internet of things

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Secured Entry Control: An IoT and Deep Learning based Mask Detection System for COVID-19 Suspected Patient Screening and Access Regulation 3

Secured Entry Control: An IoT and Deep Learning based Mask D...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Sultana, Abida Peyal, Hasibul Islam Islam, Md. Injamul Abdal, Md. Nazmul Miraz, Shafiun Kontho, Md. Mahin Rahaman Leion, Zannatul Mawa Mamun, Abdul Al Rajshahi University of Engineering & Technology Dept. of Electrical & Computer Engineering Rajshahi Bangladesh Uttara University Dept. of Computer Science & Engineering Dhaka Bangladesh University of Liberal Arts Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi Bangladesh Tejgaon College Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350388282

The new coronavirus SARS-CoV-2, which triggered the COVID-19 pandemic, has had an unparalleled effect on economies, cultures, and world health. In response to the critical need for strict COVID-19 screening systems in public areas, this study presents a creative Secured Entry Control system. Detecting and controlling possible COVID-19 carriers attempting to enter the country is made possible by this system, which makes use of deep learning algorithms and IoT technology. The mask detection algorithm, MobileNetV2 model, has an exceptional validation accuracy of 98.96\%. The model's reliability is supported by performance evaluations using ROC curves, confusion matrix analysis, and an AUC value of 98.96\%, which is close to the optimal AUC score of 100\%. Because MobileNetV2 is well-suited for low-processing devices, it is easy to deploy it on Raspberry Pi, which helps to create an affordable system. Furthermore, by spotting increased body temperatures, a contactless temperature sensor improves the system's ability to identify possible COVID-19 carriers. The functioning of the system is confirmed by the working prototype that is presented in the Experimental Results section. The main goal of this research is to create an autonomous system that is affordable and selectively allows access to people who are less likely to transmit COVID-19 in public areas. To achieve the overall objective of reducing the spread of COVID-19 in public spaces, this research highlights the successful integration of mask identification algorithms and IoT. © 2024 IEEE.

关键词： COVID-19

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Cloud based IoT platforms for Home Automation System 15

Cloud based IoT platforms for Home Automation System

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Ishaq, Malik Muzamil Sethi, Pankaj Kumar Bhat, Shariq Rashid Talwandi, Navjot Singh Sarawagi, Karan Chandigarh University Dept of Computer Science Engineering Punjab India Guru Kashi University Punjab Dept of Computer Science Engineering India

ISBN: (纸本)9798350370249

This research project focuses on developing and implementing a Cloud-based IoT platform for smart home automation, focusing on enhancing user experience and remote control functionality. The study uses hardware components like the Pir Sensor, MQ-2 Sensor, and DHT11 Sensor to construct easy to use and intelligent home environment. The research explores the existing knowledge on IoT in smart home automation, cloud-based IoT platforms, and user-centric aspects of such systems. The hardware components are thoroughly described and integrated into the smart home system, emphasizing connectivity and communication protocols. The research investigates the design of a user-friendly interface, advanced remote control functionalities, and the interlinking of AI-ML to automate smart home operations. Measures are taken to ensure data privacy and security within the cloud-based IoT environment, including data encryption, access control, and authorization mechanisms. The results and findings section showcases improvements in user experience and remote control functionalities, while the discussion section identifies potential limitations and challenges. The research project contributes to the evolution of smart home automation and underscores significant enhancements in user experience, remote control functionality, and data privacy within cloud-based IoT © 2024 IEEE.

关键词： Smart homes

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Automated AI Tool for Log File Analysis 6

Automated AI Tool for Log File Analysis

引用

6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Lohar, Prerna Baraskar, Trupti Dept.of Computer Engineering & Technology Dr. Vishwanath Karad MIT World Peace University Pune India

ISBN: (纸本)9798331522667

Log file analysis has a critical role in monitoring and maintaining software systems, yet the manual inspection of logs becomes increasingly impractical with the growing volume of data. This survey paper explores recent advancements in automated log file analysis, with a particular focus on the integration of AI techniques, which includes ML models and NLP. The study identifies key challenges in traditional methods, such as the need for human interpretation, difficulty in detecting new errors, and issues in backtracking within continuous data streams. Moreover, we examine state-of-the-art AI approaches, like LLaMA 2, to streamline log analysis by automating error detection, summarization, and anomaly identification. Research deficiencies are identified, notably the necessity for advanced methodologies to manage variety of log formats, automated model optimization, and ongoing learning processes. This comprehensive review endeavors to provide a thorough examination of the current landscape, encompassing perspectives on potential outcomes and prospective trajectories in the domain of AI-enhanced log file analysis. In addition to providing insights into prospective solutions and future directions in AI-driven log file analysis, this study attempts to give an in-depth analysis of the current situation. © 2025 IEEE.

关键词： Economic and social effects

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An Effective Brain Tumor Segmentation and Classification Framework using Transformer-based Res-Unet++ and ShuffleNetV2 3

An Effective Brain Tumor Segmentation and Classification Fra...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Kumar, Veesam Pavan Ranjan Pattanaik, Satya Sunil Kumar, V.V. Dept of Computer Science and Engineering Odisha Rourkela769015 India Dept of Computer Science and Engineering Odisha Bhubaneswar752052 India Pbr Visvodaya Institute of Kavali Dept of Computer Science and Engineering Andhra Pradesh 524201 India

ISBN: (纸本)9798350384598

In present generation, brain tumors are assumed as serious illnesses that lead to a high death rate. Recognizing and categorizing tumors in the brain because of the orientation, varying framework, irregular cell growth, and position is a challenging task in the field of medical image processing. Automatic classification and segmentation for detecting the presence of tumor cells are essential for preventing errors in diagnosis and maximizing the victim's rate of survival. Traditional machine learning strategy classifies tumors in the brain according to a particular characteristic chosen by a radiologist expert. It leads to an error during implementation as well as minimizes the efficiency of a strategy. Deep learning algorithms have acquired a great interest in the field of image categorization because of their effective performance in recent years. Deep learning serves as a flexible and powerful approach to resolve challenges in healthcare image evaluation, which includes, brain tumor, breast cancer, and lung cancer recognition. Automatic segmentation on huge datasets can attain improved performance by employing deep learning methods. Hence, with the support of deep learning intellectual brain tumor segmentation and classification technique is implemented in this paper. In the beginning, from the online resources, the significant images are aggregated and it is given to the Trans-Res-UNet++ (TRUNet++)-based segmentation process, where the abnormalities are segmented from the given input. Then the segmented image attained from the TRUNet++ model is fed into the ShufflenetV2 (SNetV2)-based classification process, where the tumor classification is performed. In the end, the effectiveness of the presented model is analyzed with the conventional brain tumor segmentation and classification models that guarantee the reliability and accuracy of the generated model. © 2024 IEEE.

关键词： Image segmentation

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An Effective Deep Learning Approach for Kidney Stone Detection in CT Scan Images using Convolutional Neural Network

An Effective Deep Learning Approach for Kidney Stone Detecti...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Islam, Md. Nurul Khan Chowdhury, Md. Fuad Hasan Nusrat, Fariha University of Scholars Dept. of Computer Science and Engineering Dhaka Bangladesh United International University Dept. of Computer Science and Engineering Dhaka Bangladesh Jahangirnagar University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

Kidney stones represent a significant global health concern, frequently causing individuals to seek immediate medical care due to intense pain. Radiological imaging modalities are one of the most common modes of diagnosis, but specialist interpretation is always important for accuracy. In this paper, we present computer vision in an automated kidney stone detection system that involves five stages: loading the dataset, preprocessing data, loading the InceptionV3 model, predicting, and evaluating performance. Various methods of preprocessing and data augmentation were utilized to make the model less biased and more robust. This study uses the InceptionV3 architecture to classify kidney stones in CT scan images of KUB (Kidney, Ureter, and Bladder). We utilize a total dataset of 433 participants, either diagnosed with kidney stones or in a normal state. 278 cases are diagnosed with kidney stones, and 165 are normal cases. The proposed approach achieved an impressive 98.55% accuracy across performance metrics when evaluated on an independent test dataset. © 2024 IEEE.

关键词： computerized tomography

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Privacy-Preserving Speech Analysis- A Review 6

Privacy-Preserving Speech Analysis- A Review

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6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Kumar, Pankaj Shukla, Mayank Malik, Medha Dept. of Computer Science and Engineering ABES Engineering College Ghaziabad India Dept. of Computer Science and Engineering ABES Engineering College Ghaziabad India

ISBN: (纸本)9798350374629

Speech-recognition technology is an essential component of many smart IoT devices. To make these systems secure and privacy-preserving, it is important to consider outsourcing speech-recognition functionality to a third-party storage provider. By removing the speech-recognition process from the device itself, the amount of personal data stored on the device is minimized. Additionally, the provider can ensure the strictest security standards are met when the audio data is stored, including the use of encryption and access control mechanisms. Furthermore, the provider can also ensure that the speech-recognition process is done using the latest technologies to ensure accurate recognition of the audio data. To ensure the end-user's privacy is kept intact, the provider should allow only authorized users to access the audio data. Additionally, the provider should also enable data access logging and audit trails allowing for the tracking of any access to the data. By outsourcing speech-recognition services to a third-party, smart IoT devices can ensure the highest levels of privacy and security while still enabling users to get the most out of their device. The server processes the encrypted data to generate recognition results, such as user commands and voice printing, without compromising the privacy of the user's voice data. To enhance voice data privacy, federated learning can be employed in the design of these models. This approach enables distributed training of the model without the need to transmit the user's voice data to a central server. The adoption of this approach in the IoT industry is gaining momentum as it offers the potential to create smarter and more secure IoT devices that inspire customer trust. ©2024 IEEE.

关键词： Speech recognition

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