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ZCube: A Zero-Trust, Zero-Knowledge, and Zero-Memory Platform for Privacy and yet Secured Access 6

ZCube: A Zero-Trust, Zero-Knowledge, and Zero-Memory Platfor...

引用

6th IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications, TPS-ISA 2024

作者： Quach, Vinh Dantu, Ram Talapuru, Sirisha Zaman, Shakila Pokharel, Apurba University of North Texas Dept. of Computer Science and Engineering DentonTX United States

ISBN: (纸本)9798350386745

As concerns over privacy and trust escalate among individuals and corporations, traditional memory-based trust systems are increasingly seen as inadequate and undesirable. The rise of microservice architecture complicates this by making data paths harder to track. Given these concerns, the critical question arises: How can we establish trust or verify an entity's credibility without collecting private information or relying on comparisons with pre-existing data, while knowing the exact data path of each request? To address this challenge, we introduce a lightweight, scalable, ZCube platform that employs nested Zero Knowledge Proofs (ZKPs), eliminating reliance on memory or evaluations based on past actions. This platform verifies and maintains trust in real-time without centralizing trust in any specific part of the system. It anticipates intentions and rigorously monitors them, ensuring operational integrity while preserving privacy. Distinctive for its ZKP trustless setup incorporating blockchain, each request begins anew and is rigorously monitored bidirectionally during execution. Our tests on the realistic example of a microservice-based distributed system, OpenTelemetry Demo, confirm that our novel approach - combining proof segmentation with plan adherence - exhibits fast response times of less than 200ms on average for most test cases. This ZCube platform surpasses traditional methods in efficiency and security. Our analysis shows comprehensive security and enhanced performance while effectively maintaining bidirectional trustlessness. © 2024 IEEE.

关键词： Differential privacy

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Exploring Large Language Models for Semantic Analysis and Categorization of Android Malware 40

Exploring Large Language Models for Semantic Analysis and Ca...

引用

40th Annual computer Security Applications Conference Workshops, ACSACW 2024

作者： Walton, Brandon J Khatun, Mst Eshita Ghawaly, James M Ali-Gombe, Aisha Louisiana State University Dept. of Computer Science and Engineering Baton Rouge United States

ISBN: (纸本)9798331532819

Malware analysis is a complex process of examining and evaluating malicious software's functionality, origin, and potential impact. This arduous process typically involves dissecting the software to understand its components, infection vector, propagation mechanism, and payload. Over the years, deep reverse engineering of malware has become increasingly tedious, mainly due to modern malicious codebases' fast evolution and sophistication. Essentially, analysts are tasked with identifying the elusive needle in the haystack within the complexities of zero-day malware, all while under tight time constraints. Thus, in this paper, we explore leveraging Large Language Models (LLMs) for semantic malware analysis to expedite the analysis of known and novel samples. Built on GPT-4o-mini model, MalParse is designed to augment malware analysis for Android through a hierarchical-Tiered summarization chain and strategic prompt engineering. Additionally, MalParse performs malware categorization, distinguishing potential malware from benign applications, thereby saving time during the malware reverse engineering process. Despite not being fine-Tuned for Android malware analysis, we demonstrate that through optimized and advanced prompt engineering MalParse can achieve up to 77% classification accuracy while providing highly robust summaries at functional, class, and package levels. In addition, leveraging the backward tracing of the summaries from package to function levels allowed us to pinpoint the precise code snippets responsible for malicious behavior. © 2024 IEEE.

关键词： Android malware

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DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clustering and Deep Learning for Scalable Indoor Localization Using Wi-Fi RSSI 13

DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clusterin...

引用

Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Mahali, Muhammad Izzuddin Leu, Jenq-Shiou Achmad Sulistyo Putro, Nur Widyawan Prakosa, Setya Avian, Cries National Taiwan University of Science and Technology Dept. of Electronic and Computer Engineering Taipei Taiwan Universitas Negeri Yogyakarta Dept. of Electronics and Informatics Engineering Education Yogyakarta Indonesia Universitas Gadjah Mada Dept. of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350373332

This research introduces a novel method for indoor positioning by integrating Fuzzy C-Means clustering with deep learning autoencoders, leveraging Wi-Fi RSSI fingerprinting for precise location prediction and building and floor level classification within a unified framework. The approach combines the comprehensibility of Fuzzy C-Means (FCM) with the robust feature extraction capabilities of deep autoencoders through a dual-phase strategy: extensive offline training utilizing RSSI fingerprint data followed by real-time online position prediction. The model's performance was rigorously tested using 5-Fold cross-validation, examining its effectiveness both with and without the integration of FCM on various cluster numbers. Evaluation metrics, including Euclidean distance error for regression and accuracy, F1-score, precision, and specificity for classification effectiveness, were used for comparative analysis. The results were visualized through regression plots, confusion matrices, and learning curves, supplemented by t-SNE visualizations that provide profound insights into the feature space. These detailed analyses highlight the considerable promise of this integrated approach in enhancing the accuracy and reliability of indoor positioning systems. © 2024 IEEE.

关键词： Fuzzy clustering

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Textual Authenticity in the AI Era: Evaluating BERT and RoBERTa with Logistic Regression and Neural Networks for Text Classification 16

Textual Authenticity in the AI Era: Evaluating BERT and RoBE...

引用

16th International Symposium on Electronics and Telecommunications, ISETC 2024

作者： Hazim, Layth Rafea Ata, Oguz Altinbas University Dept. of Electrical and Computer Engineering Istanbul Turkey College of Computer Science and Mathematics Tikrit University Tikrit Iraq Altinbas University Dept. of Software Engineering Istanbul Turkey

ISBN: (纸本)9798350390865

AI-generated content impersonating human writing is an issue that has gained attention as AI spreads its wings. This particular study serves as a comparison between the existing Logistic Regression and Feedforward Neural Networks (FNNs) by employing sentence-BERT-appended models and RoBERTa in the determination of authenticity in a given text. A dataset that was balanced between human-written and AI-generated texts was utilized. Techniques like tokenization and normalization were first used, followed by feature extraction using transformer-based models. Cross-validation and confusion matrix analysis that used measures such as accuracy, precision, recall, F1 score, and ROC AUC were included to guarantee the models' robustness. The hybrid RoBERTa-FNN model that was deposed challengers was the most outstanding model in respect of precision and recall, and the highest accuracy (99.95%) was obtained as mentioned in the data. The improved performance serves as a proof of how effectively RoBERTa uses its embeddings to represent context on the fine-grained level required for this kind of text classification. This work is a stepping stone to the creation of strong AI text detection systems, besides our advancements to the knowledge of models and embedding performance with respect to text classification. The results lay emphasis on the selection of model configuration and the embedding technique, as they are the key factors in achieving the best results in practical applications. © 2024 IEEE.

关键词： Logistic regression

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Real-Time Traffic Management System Using YOLOv8 and CNN: A Deep Learning Approach with IoT Integration 1

Real-Time Traffic Management System Using YOLOv8 and CNN: A ...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Saklani, Sumit Manchanda, Mahesh Sharma, Raman Singh, Devendra Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Application Dehradun India

ISBN: (纸本)9798331518578

Traffic management also plays a crucial role in urban planning and development, with pressing challenges related to congestion, safety, and environmental impact. In this study, we proposed a real time traffic control system using YOLOv8 and CNNs combined with Internet of Things (IoT) technologies. As a high-performing object detection model, YOLOv8 is able to accurately and rapidly detect vehicles, pedestrians, and other traffic objects. When combined with CNN based data analytics, the proposed system can successfully monitor traffic flow, discover irregularities, and control the signals dynamically. Creating a cohesive framework for intelligent decision-making IoT integration ensures real-time data acquisition from sensors, cameras and edge devices. IoT framework for a smart urban environment the proposed architecture provides the versatility through scalability, low latency, and adjustment to several urban perspective scenarios. The combination leads to greatly enhanced traffic flow efficiency, accident prevention and environmental impact reduction as experimental results show. With data till October 2023, this study resolves a gap between the developing field of deep learning and the Internet of Things (IoT), that creates to be utilized to connect to advanced town environments. © 2025 IEEE.

关键词： Decision making

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Sea Erosion Prediction and Monitoring Using IOT and ML 2

Sea Erosion Prediction and Monitoring Using IOT and ML

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Usha Bala, V. Harshavardhan Sai, Y. Slipa Shramila, V. Asritha, M. Bharath, M. Chandu, A. Anil Neerukonda Institute of Technology & Sciences Dept. of Computer Science & Engineering Visakhapatnam India

ISBN: (纸本)9798350366570

One of the major environmental issues that the world is currently facing is sea erosion, which requires an innovative solution for early detection and mitigation. Our paper focuses on the integrated system that combines both IoT and Machine Learning. The four main important environmental factors that are responsible for sea erosion are temperature, wave height, wave speed, and soil moisture. IoT sensors deployed along the prototype design are used to monitor these environmental parameters. Another key component is the machine learning algorithm, which is used analyse the interactions, patterns, and relations among the environmental data. By analysing the patterns among the previous data, the model can predict the likelihood of sea erosion events. This allows for the early detection of erosion events and timely interventions that can minimize the damage or loss caused by sea erosion. The main aim of this project is to help the coastal management authority take smart decisions and proactive steps using both real-time and past data analysis. © 2024 IEEE.

关键词： Soil erosion

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RT-MDM: Real-Time Scheduling Framework for Multi-DNN on MCU Using External Memory 24

RT-MDM: Real-Time Scheduling Framework for Multi-DNN on MCU ...

引用

61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Kang, Sukmin Lee, Seongtae Koo, Hyunwoo Chwa, Hoon Sung Lee, Jinkyu Dept. of Semiconductor and Display Engineering Sungkyunkwan University Korea Republic of Samsung Electronics Co. Ltd. Korea Republic of Dept. of Computer Science and Engineering Sungkyunkwan University Korea Republic of School of Electronic and Electrical Engineering Sungkyunkwan University Korea Republic of Dept. of Electrical Engineering and Computer Science Dgist Korea Republic of

ISBN: (纸本)9798400706011

As the application scope of DNNs executed on microcontroller units (MCUs) extends to time-critical systems, it becomes important to ensure timing guarantees for increasing demand of DNN inferences. To this end, this paper proposes RT-MDM, the first RealTime scheduling framework for Multiple DNN tasks executed on an MCU using external memory. Identifying execution-order dependencies among segmented DNN models and memory requirements for parallel execution subject to the dependencies, we propose (i) a segment-group-based memory management policy that achieves isolated memory usage within a segment group and sharded memory usage across different segment groups, and (ii) an intra-task scheduler specialized for the proposed policy. Implementing RT-MDM on an actual system and optimizing its parameters for DNN segmentation and segment-group mapping, we demonstrate the effectiveness of RT-MDM in accommodating more DNN tasks while providing their timing guarantees. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Memory management

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Real World Deployment of Dual Branch MRC Enhanced by Adaptive Algorithms LMS and RLS in 5G Networks to Improve SNR and QoS 4

Real World Deployment of Dual Branch MRC Enhanced by Adaptiv...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Upadhyay, Deepak Upadhyay, Abhay Gupta, Mridul Sharma, Kunj Bihari Maindola, Meenakshi Garg, Shobhit Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Electronics and Communication Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Management Studies Dehradun India

ISBN: (纸本)9798350349900

This research is aimed at understanding the performance benefits of the use of adaptive algorithms in dual-branch MRC systems as a solution for developing 5G networks. We developed controlled simulations to determine the performance influences of the algorithms in SNR, QoS, BER, and throughput under different network conditions. Our results indicate that the algorithms significantly reduce BER safe power divergence that improves the reliability and performance of the communication platform. The adaptive systems in addition reveal a significantly impressive ability to maintain a higher rate of throughput that is necessary in the current networks due to the increasing volume of data consumed by different users. In conclusion, therefore, this research has showed that adaptive MRC techniques might be a highly-viable option toward enabling the development of 5G technology, provided a suitable method for boosting performance during uncertain communications. © 2024 IEEE.

关键词： 5G mobile communication systems

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Epileptic Seizure Detection Using Quantum Support Vector Classifier

Epileptic Seizure Detection Using Quantum Support Vector Cla...

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2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Meesala, Gopichand Soni, Barkha Pandey, Manish Khare, Nilay Maulana Azad National Institute of Technology Dept. of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350381887

Epilepsy is the second most prevalent neurological disorder. It poses significant challenges to both diagnosis and treatment. The information obtained from electroencephalography serves as crucial for understanding the dynamics of epileptic brain activity. It offers a non-invasive means for monitoring and detecting epileptic seizures. This paper explores the use of quantum machine learning algorithms to improve the precision and effectiveness of utilizing EEG data for epileptic seizure detection. Quantum machine learning, leveraging principles from quantum mechanics, holds promise for addressing these challenges by harnessing quantum computational power and exploiting quantum entanglement to process information more efficiently. Quantum features and quantum kernels are employed to capture intricate patterns within the EEG signals, allowing for improved discrimination between normal brain activity and seizure events. Results indicate that the Quantum Support Vector Classifier exhibit a satisfactory classification accuracy compared to traditional techniques despite having constraints with resources. The findings suggest that quantum computing holds great promise in revolutionizing the field of epilepsy diagnosis by providing more robust and accurate tools for distinguishing between epileptic and non-epileptic EEG patterns. © 2024 IEEE.

关键词： Quantum entanglement

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Epileptic Seizure Detection using Variational Quantum Classifier

Epileptic Seizure Detection using Variational Quantum Classi...

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2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Meesala, Gopichand Kumar, Lalit Pandey, Manish Khare, Nilay Maulana Azad National Institute of Technology Dept. of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350381887

Epilepsy, a neurological disorder characterized by recurrent and unpredictable seizures, demands timely and accurate detection for effective management and patient safety. While traditional machine learning approaches have shown promise in seizure detection, this study explores the potential of quantum computing, specifically utilizing a Variational Quantum Classifier. The Variational Quantum Classifier, grounded in quantum mechanics, aims to enhance computational capabilities for seizure detection. Leveraging quantum computing's intrinsic parallelism and superposition properties, the model focuses on processing complex and high-dimensional Electroencephalogram signals commonly used in epilepsy diagnosis. The model undergoes training on a dataset containing both seizure and non-seizure instances, optimizing parameters for increased accuracy. Performance evaluations utilize metrics such as sensitivity, specificity, and overall classification accuracy. © 2024 IEEE.

关键词： Quantum computers

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