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Ensuring Zero Trust IoT Data Privacy: Differential Privacy in Blockchain using Federated Learning

IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Hussain, Altaf Akbar, Wajahat Hussain, Tariq Bashir, Ali Kashif Dabel, Maryam M. Al Ali, Farman Yang, Bailin School of Electronic and Control Engineering Chang’an University Xi’an China School of Computer Science and Technology and School of Mathematics and Statistics Zhejiang Gongshang University Hangzhou China Department of Computing and Mathematics Manchester Metropolitan University UK Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Saudi Arabia Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul South Korea

In the increasingly digitized world, the privacy and security of sensitive data shared via IoT devices are paramount. Traditional privacy-preserving methods like k-anonymity and ldiversity are becoming outdated due to technological advancements. In addition, data owners often worry about misuse and unauthorized access to their personal information. To address this, we propose a secure data-sharing framework that uses local differential privacy (LDP) within a permissioned blockchain, enhanced by federated learning (FL) in a zero-trust environment. To further protect sensitive data shared by IoT devices, we use the Interplanetary File System (IPFS) and cryptographic hash functions to create unique digital fingerprints for files. We mainly evaluate our system based on latency, throughput, privacy accuracy, and transaction efficiency, comparing the performance to a benchmark model. The experimental results show that the proposed system outperforms its counterpart in terms of latency, throughput, and transaction efficiency. The proposed model achieved a lower average latency of 4.0 seconds compared to the benchmark model’s 5.3 seconds. In terms of throughput, the proposed model achieved a higher throughput of 10.53 TPS (transactions per second) compared to the benchmark model’s 8 TPS. Furthermore, the proposed system achieves 85% accuracy, whereas the counterpart achieves only 49%. IEEE

关键词： Anonymity

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Malware analysis and multi-label category detection issues: Ensemble-based approaches 3

Malware analysis and multi-label category detection issues: ...

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3rd International Conference on Intelligent Data science Technologies and Applications, IDSTA 2022

作者： Alsmadi, Izzat Al-Ahmad, Bilal Alsmadi, Mohammad Department of Computing and Cyber Security Texas A&m San Antonio United States The University of Jordan Computer Information Systems Department Aqaba Jordan Applied Science University Information Technology Amman Jordan

ISBN: (数字)9781665499606

ISBN: (纸本)9781665499606

Detection of malware and security attacks is a complex process that can vary in its details and analysis activities. As part of the detection process, malware scanners try to categorize a malware once it is detected under one of the known malware categories (e.g. worms, spywares, viruses, etc.). However, many studies and researches indicate problems with scanners categorizing or identifying a particular malware under more than one malware category. This paper, and several others, show that machine learning can be used for malware detection especially with ensemble base prediction methods. In this paper, we evaluated several custom-built ensemble models. We focused on multi-label malware classification as individual or classical classifiers showed low accuracy in such *** paper showed that recent machine models such as ensemble and deep learning can be used for malware detection with better performance in comparison with classical models. This is very critical in such a dynamic and yet important detection systems where challenges such as the detection of unknown or zero-day malware will continue to exist and evolve. © 2022 IEEE.

关键词： Scanning

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Permutation Superposition Oracles for Quantum Query Lower Bounds

arXiv

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arXiv 2024年

作者： Majenz, Christian Malavolta, Giulio Walter, Michael Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Department of Computing Sciences Bocconi University Italy Max Planck Institute for Security and Privacy Germany Faculty of Computer Science Ruhr-Universität Bochum Germany

We propose a generalization of Zhandry's compressed oracle method to random permutations, where an algorithm can query both the permutation and its inverse. We show how to use the resulting oracle simulation to bound the success probability of an algorithm for any predicate on input-output pairs, a key feature of Zhandry's technique that had hitherto resisted attempts at generalization to random permutations. One key technical ingredient is to use strictly monotone factorizations to represent the permutation in the oracle's database. As an application of our framework, we show that the one-round sponge construction is unconditionally preimage resistant in the random permutation model. This proves a conjecture by Unruh. © 2024, CC BY.

关键词： Query processing

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From Black Boxes to Transparent Insights: Enhancing Industrial Control Systems Anomaly Detection with Deep Autoencoder Models

From Black Boxes to Transparent Insights: Enhancing Industri...

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Mobile, Intelligent, and Ubiquitous computing Conference (MIUCC), International

作者： Eslam Amer Bander Ail Saleh Al-rimy Shaker El-Sappagh Sultan Almalki Tami Alghamdi School of Computing University of Portsmouth Portsmouth UK Department of Computer Science Galala University Suez Egypt Computer Department Applied College Najran University Najran Saudi Arabia Faculty of Computing and Information Al-Baha University Al-Baha Saudi Arabia

ISBN: (数字)9798350367775

ISBN: (纸本)9798350367782

In the realm of securing Industrial Control Systems (ICS), time-series anomaly detection is pivotal, particularly for identifying unknown or zero-day attacks by detecting deviations from established normal behavior. Deep learning models, while effective, often act as black boxes, obscuring how individual features impact detection outcomes. Given the vulnerability of machine learning models to adversarial attacks and their potential reliance on shortcuts, it's crucial that anomaly detection methods are interpretable and grounded in reliable principles. In this work, we introduce novel explainable anomaly detection models that incorporate attention mechanisms in both CNN and LSTM frameworks, enhancing the interpretability and performance of the detection process. Our approach leverages Explainable AI (XAI) techniques to analyze ICS anomaly detection models trained on multidimensional time-series data from the Secure Water Treatment (SWaT) dataset. We specifically compare the effectiveness of two explainability methods—SHAP and counter-factual explanations—within the context of attention-enhanced LSTM-AE and CNN-AE models. Our results demonstrate that our models achieve the highest F-score compared to other peer models, offering superior consistency and descriptive accuracy. This advancement not only improves the interpretability of feature influences on anomaly classification but also significantly enhances the reliability of detection in critical industrial systems.

关键词： Deep learning Analytical models Attention mechanisms Industrial control Computational modeling Closed box Feature extraction Integrated circuit modeling Anomaly detection Long short term memory

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Zeroth-Order Methods for Constrained Nonconvex Nonsmooth Stochastic Optimization 41

Zeroth-Order Methods for Constrained Nonconvex Nonsmooth Sto...

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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Zhuanghua Chen, Cheng Luo, Luo Low, Bryan Kian Hsiang Department of Computer Science National University of Singapore Singapore Singapore CNRS@CREATE LTD 1 Create Way #08-01 CREATE Tower 138602 Singapore Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China School of Data Science Fudan University Shanghai China Shanghai Key Laboratory for Contemporary Applied Mathematics China

This paper studies the problem of solving nonconvex nonsmooth optimization over a closed convex set. Most previous works tackle such problems by transforming the constrained problem into an unconstrained problem. However, they only provide asymptotic convergence analysis for their methods. In this work, we provide the non-asymptotic convergence analysis for solving constrained nonconvex nonsmooth optimization. We first generalize classical gradient mapping and the Frank-Wolfe gap in the nonsmooth setting. Then we introduce novel notions of approximate stationarity concerning such generalized quantities. We also propose several stochastic zeroth-order algorithms for the problem, along with their non-asymptotic convergence guarantees of obtaining the proposed approximate stationarity. Finally, we conduct numerical experiments that demonstrate the effectiveness of our algorithms. Copyright 2024 by the author(s)

关键词： Optimization algorithms

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A New Image Watermarking Scheme Using Genetic Algorithm and Residual Numbers with Discrete Wavelet Transform

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Journal of Information Security 2023年第4期14卷 422-436页

作者： Peter Awonnatemi Agbedemnab Mohammed Akolgo Moses Apambila Agebure Department of Information Systems and Technology School of Computing and Information Sciences C.K. Tedam University of Technology and Applied Sciences Navrongo Ghana Department of Computer Science School of Computing and Information Sciences C.K. Tedam University of Technology and Applied Sciences Navrongo Ghana

Transmission of data over the internet has become a critical issue as a result of the advancement in technology, since it is possible for pirates to steal the intellectual property of content owners. This paper presents a new digital watermarking scheme that combines some operators of the Genetic Algorithm (GA) and the Residue Number (RN) System (RNS) to perform encryption on an image, which is embedded into a cover image for the purposes of watermarking. Thus, an image watermarking scheme uses an encrypted image. The secret image is embedded in decomposed frames of the cover image achieved by applying a three-level Discrete Wavelet Transform (DWT). This is to ensure that the secret information is not exposed even when there is a successful attack on the cover information. Content creators can prove ownership of the multimedia content by unveiling the secret information in a court of law. The proposed scheme was tested with sample data using MATLAB2022 and the results of the simulation show a great deal of imperceptibility and robustness as compared to similar existing schemes.

关键词： Discrete Wavelet Transform (DWT) Digital Watermarking Encryption Genetic Algorithm (GA) Residue Number System (RNS) GARN

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Automatic recognition of depression based on audio and video:A review

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World Journal of Psychiatry 2024年第2期14卷 225-233页

作者： Meng-Meng Han Xing-Yun Li Xin-Yu Yi Yun-Shao Zheng Wei-Li Xia Ya-Fei Liu Qing-Xiang Wang Shandong Mental Health Center Shandong UniversityJinan 250014Shandong ProvinceChina Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353Shandong ProvinceChina Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and TechnologyQilu University of Technology(Shandong Academy of Sciences)Jinan 250353Shandong ProvinceChina Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250353Shandong ProvinceChina Department of Ward Two Shandong Mental Health CenterShandong UniversityJinan 250014Shandong ProvinceChina

Depression is a common mental health *** current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression ***-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively *** physicians usually require extensive training and experience to capture changes in these *** in deep learning technology have provided technical support for capturing non-biological *** researchers have proposed automatic depression estimation(ADE)systems based on sounds and videos to assist physicians in capturing these features and conducting depression *** article summarizes commonly used public datasets and recent research on audio-and video-based ADE based on three perspectives:Datasets,deficiencies in existing research,and future development directions.

关键词： Depression recognition Deep learning Automatic depression estimation System Audio processing Image processing Feature fusion Future development

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OLBR: Optimization of Location-Based Routing Protocols in Vehicular Ad Hoc Networks

OLBR: Optimization of Location-Based Routing Protocols in Ve...

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Recent Innovation in Smart and Sustainable Technology (ICRISST), International Conference on

作者： Thangam S M Vivek Srikar Reddy Karthik Ullas N Abdul Khadar Jilani K Venkatesh J Jesy Janet Kumari A Saleem Raja Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India IT Department College of Computing and Information Sciences University of Technology and Applied Sciences-Shinas Sultanate of Oman

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309157

The proposed work employs ns-3, SUMO, and NetAnim to create geographical routing in vehicular ad hoc networks (VANETs). The research attempts to assess the performance of three well-known protocols AODV, DSDV, and OLSR in VANET settings. Utilizing SUMO’s realistic mobility models and NS3’s simulation capabilities, the project simulates node communication, protocol behaviour, and vehicle movement. The main purpose is to know how well various routing techniques work in dynamic vehicular situations.

关键词： Technological innovation Roads Vehicular ad hoc networks Packet loss Routing Routing protocols Delays Vehicle dynamics AODV Optimization

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Enhancement of Quality of Service in Mobile Ad-Hoc Networks using Hybrid Load Balanced Simulator: Annealing Simulated 11

Enhancement of Quality of Service in Mobile Ad-Hoc Networks ...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Kumar, Anil Shukla, R.K. Shukla, R.S. Pal, Shelendra Invertis University Depatment of Computer Applications Bareilly India Invertis University Department of Applied Sciences and Humanities Bareilly India College of Computing and Informatics Electronic University Department of Computer Science Saudi Arabia Tmu U.P. Moradabad India

ISBN: (纸本)9781665487320

Wireless topologies can change rapidly and unexpectedly. Hybrid networks are connected by gateways, which act as entry points for all traffic. Some gateways may be subject to high traffic loads and therefore may be overloaded. To compare the performance of the proposed work, various parameters including end-to-end latency, power consumption, and throughput are considered. Load Balancing Hybrid Simulator (LBHS) is an algorithm used in this paper to distribute traffic load among multiple gateways to solve load balancing problems in MANET. It combines a Hybridge Annealing Simulation (HAS) network technology with Decentralized Random Search (DRS). A network that supports QoS in MANET. The Dominant Minimal Set (DMS) problem is solved using AS. The DSS heuristic algorithm provides an algorithm with a simple structure and a high degree of exploration. In addition to faster convergence than other algorithms, it also implements an axiomatic algorithm to ensure the diversity of agents and avoid falling into local optimum. The test results show that the proposed Hybridge Annealing Simulated Decentralized Random Search (HAS-DRS) procedure performs better than the dominated connection set (DCS). © 2022 IEEE.

关键词： Simulated annealing

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Virtual Network Embedding: Literature Assessment, Recent Advancements, Opportunities, and Challenges

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IEEE Communications Surveys and Tutorials 2025年

作者： Satpathy, Anurag Sahoo, Manmath Narayan Swain, Chittaranjan Bellavista, Paolo Guizani, Mohsen Muhammad, Khan Bakshi, Sambit Missouri University of Science and Technology Department of Computer Science RollaMO United States National Institute of Technology Visual Surveillance Laboratory Department of Computer Science and Engineering Odisha Rourkela769008 India Atal Bihari Vajpayee Indian Institute of Information Technology and Management Department of Computer Science and Engineering Gwalior India Department of Computer Science and Engineering Italy Machine Learning Department Abu Dhabi United Arab Emirates Department of Applied Artificial Intelligence School of Convergence College of Computing and Informatics Seoul03063 Korea Republic of

Network virtualization (NV) allows service providers (SPs) to instantiate logically isolated entities called virtual networks (VNs) on top of a substrate network (SN). Though VNs bring about multiple benefits, particularly in terms of economic costs and elasticity, they also force various technical challenges to be addressed. The primary one is the issue of optimally allocating resources to VNs, also termed virtual network embedding (VNE). This paper presents an exhaustive survey of VNE by extensively covering the state-of-the-art research field in this very active field and focusing on the emerging research trends in industry and academia over the last decade. In addition, this survey originally contributes to the literature by proposing a novel taxonomy for existing VNE solutions and providing a thorough comparative study of their strategies. © 1998-2012 IEEE.

关键词： Virtual machine

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