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20th International Joint Conference on computer science and Software engineering, JCSSE 2023

作者： Samakit, Tanapat Anutariya, Chutiporn Buranarach, Marut School of Engineering and Technology Asian Institute of Technology Computer Science & Information Management Ict Department Thailand National Electronics and Computer Technology Center Data Science and Analytics Research Group Thailand

ISBN: (纸本)9798350300505

Open Government Data (OGD) refers to the provision of data produced by the government to the general public, in a format that is readily readable and can be used by machines with ease. It can also promote transparency, improve decision-making, enhance accountability, create economic opportunities, and encourage civic engagement. The OGD can help citizens understand the government and its legitimacy and transparency. Thus, when the government shares its data with people, it helps to create trust by being transparent, accountable, and promoting innovative solutions that benefit everyone. However, each published dataset has no indication of its quality assessment at all;thus, making it difficult for citizens to assess the reliability of the data from the OGD. Therefore, a data quality assessment for OGD should be developed. This will help create effective datasets which in turn help users understand the data. This study proposes QUALYST, a system that assesses Thailand's OGD dataset and validates it for analytic and visualization purposes. The study focuses on designing the data storage and implementing the assessment system. Furthermore, the proposed data quality dimensions, the developed data pipeline, and the assessment process are elaborated. Finally, the prototype system is demonstrated using Thailand's OGD datasets with examples in a visualized format. © 2023 IEEE.

关键词： Transparency

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Toward Drought Modeling in South Asia: Machine Learning Approaches, Challenges, and Opportunities

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IEEE Access 2025年 13卷 87654-87671页

作者： Reshan, Mana Saleh Al Raza, Muhammad Owais Mahoto, Naeem Ahmed Rajab, Adel Shaikh, Asadullah Elmagzoub, Mohamed A. Rajab, Khairan D. Najran University College of Computer Science and Information Systems Department of Information Systems Najran61441 Saudi Arabia College of Computer Science and Information Systems Najran61441 Saudi Arabia Istanbul Sabahattin Zaim University Department of Computer Engineering İstanbul34303 Turkey Mehran University of Engineering and Technology Department of Software Engineering Sindh Jamshoro76062 Pakistan Najran University College of Computer Science and Information Systems Department of Computer Science Najran61441 Saudi Arabia Najran University College of Computer Science and Information Systems Department of Network and Communication Engineering Najran61441 Saudi Arabia

Drought is an environmental and economic problem. Sustainable ecosystems, water resources, food security, and ecosystem sustainability. Machine all are severely affected by drought. Due to the increasing frequency and severity of droughts caused by climate change. Effective drought modeling is crucial for early warning systems and risk mitigation. Recent advances in machine learning (ML) and deep learning (DL) techniques have been developed as potential drought modeling tools, which offer accurate and reliable drought detection. This review paper summarizes the drought modeling(Drought Prediction, Drought Detection and Drought Forecasting) approaches. This paper focuses on three main aspect. 1) The selection of the region for this study, for this study South Asia(SA) is selected as region of interest (ROI) that offer accurate drought modeling, providing policymakers and decision-makers with insightful information. The geographical scope of this study is the region of South Asia. This region is selected because of its heavy reliance on agriculture. 2) This paper focuses on the current and future trends, challenges, and advances of and vulnerability to droughts. The review offers a thorough grasp of how drought conditions are evaluated by gathering and analyzing the most important drought indicators and metrics specific to South Asia. The paper explores the current state-of-the-art in ML and DL for drought modeling. 3) This review encapsulates the indicator and metrics (Complex Machine learning and deep learning models) for drought modeling which are most relevant to the SA region. This study sum up as most common challenges in drought modeling are, highlighting current challenges such as incomplete and inconsistent datasets, lack of explainable and interpretable models, and unavailability of data for model uncertainty analysis. This study proposes that these problems can be solved with modern machine learning techniques such as explainable machine learning and federa

关键词： Federated learning

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Cognitive Defense Cyber Attack Prediction and Security Design in Machine Learning Model 6

Cognitive Defense Cyber Attack Prediction and Security Desig...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Devadarshini, P.T. Chandrashekar, B. Pundir, Sumit Tiwari, Mohit Madala, Ravikiran Indhuma, E. Easwari Engineering College Department of Artificial Intelligence and Data Science TamilNadu Chennai India St. Martin's Engineering College Department of Computer Science and Engineering Kompalli Telangana Secunderabad India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttrakhand Dehradun India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India Network and Data Center Engineer Ph.D Information Technology University of Cumberlands DurhamNC United States St.Martln's Engineering College Department of Computer Science and Engineering Telangana Secunderabad India

ISBN: (纸本)9798350304480

Companies of all sizes in today's linked digital world are susceptible to cyber dangers. In this study, we provide a novel approach to cyber defence that integrates machine learning with cognitive defensive techniques to identify and prevent cyber attacks in their tracks. We built three ML models that use cognitive defensive strategies and other cutting-edge security measures like encryption and intrusion detection. A 95% accuracy rate was reached by our models, which is far greater than where things stand right now models. Our work has the potential to have a huge effect since the models we've developed may one day help businesses take a more preventative stance against cyber threats. Our findings may potentially be used to fields outside than cyber security. Overall, our study shows that machine learning and cognitive defense are useful for foreseeing and stopping cyber threats, and points the way toward fruitful future study in this area. © 2023 IEEE.

关键词： Accuracy Advanced security features Cognitive defense Cyber security Cyber-attacks Encryption Intrusion detection Machine learning Prediction Prevention Proactive approach Threat landscape

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Multi-Layer Perceptrons and Convolutional Neural networks Based Side-Channel Attacks on AES Encryption

Multi-Layer Perceptrons and Convolutional Neural Networks Ba...

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2023 International Conference on engineering Technology and Technopreneurship, ICE2T 2023

作者： Ahmed, Amjed Abbas Hasan, Mohammad Kamrul Center for Cyber Security Faculty of Information Science and Technology Bangi 43600 Malaysia Department of Computer Techniques Engineering Baghdad Iraq

ISBN: (纸本)9798350304107

Side - channel attacks (SCA) exploit vulnerabilities introduced by insecure implementations in order to get information about the data that is being processed or the data itself via device leakages. The majority of modern attack techniques use AES systems, and several studies have exploited side-channel leakages. Exploiting leakages for the first and final rounds, often known as outer rounds, is frequently the foundation of the strategy. There is a possibility that outer round leakages cannot be attacked due to inadequate countermeasures or device design. The attacker has to focus their attention on the inner rounds. Deep learning side-channel attacks, whether they include profiling or not, have the potential to recover secret keys stored in cryptographic devices that use masked AES. Mask value profiling is being used in more recent techniques for the extraction of secret keys. The objective of our research is to defeat masked AES by deep learning methods like as multi-layer perceptrons and convolutional neural networks are used. In an environment when profiling is not taking place, we extract sensitive information using DDLA. The HW (Hamming Weight) model is used by our approach in order to carry out DDLA by means of an innovative binary labeling strategy. Using the ChipWhisperer power traces and the ASCAD dataset, we show profiling and non-profiling attacks. The accuracy of key recovery is at its maximum when the AES implementation is either masked or unmasked. © 2023 IEEE.

关键词： Convolutional neural networks

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Minimizing Carbon Emissions in Container Shipping Using Quantum-Inspired Approaches

Minimizing Carbon Emissions in Container Shipping Using Quan...

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2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024

作者： Ou, Chia-Ho Liu, Shi-Yu She, Huai-Zhong Hong, Wen-Hao Kao, De-Lun Chen, Chih-Yu Siangprasertkij, Krittin Department of Computer Science and Information Engineering National Pingtung University Pingtung Taiwan Quantum Information Center Chung Yuan Christian University Taoyuan Taiwan International School of Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9798350389210

International trade heavily relies on shipping, yet container mobility and its varying demand create imbalances in container stocks, this results in frequent scheduling of empty containers by shipping companies, leading to unnecessary voyages that adversely impact marine ecosystems and exacerbate global warming. This study applies quantum and digital annealing to optimize empty container scheduling, aiming to minimize the carbon emissions resulting from the scheduling of empty containers to mitigate damage to marine ecosystems and the effects of global warming. Comparing to Simulated Annealing, Compal Quantix, and D-Wave Hybrid, Fujitsu DA3 stands out for its superior performance in both calculating the optimal solution and reducing computation time. © 2024 IEEE.

关键词： Simulated annealing

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A Hierarchical Spatial Transformer for Massive Point Samples in Continuous Space 37

A Hierarchical Spatial Transformer for Massive Point Samples...

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37th Conference on Neural information Processing Systems, NeurIPS 2023

作者： He, Wenchong Jiang, Zhe Xiao, Tingsong Xu, Zelin Chen, Shigang Fick, Ronald Medina, Miles Angelini, Christine Department of Computer & Information Science & Engineering University of Florida United States Center for Coastal Solutions University of Florida United States

ISBN: (纸本)9781713899921

Transformers are widely used deep learning architectures. Existing transformers are mostly designed for sequences (texts or time series), images or videos, and graphs. This paper proposes a novel transformer model for massive (up to a million) point samples in continuous space. Such data are ubiquitous in environment sciences (e.g., sensor observations), numerical simulations (e.g., particle-laden flow, astrophysics), and location-based services (e.g., POIs and trajectories). However, designing a transformer for massive spatial points is non-trivial due to several challenges, including implicit long-range and multi-scale dependency on irregular points in continuous space, a non-uniform point distribution, the potential high computational costs of calculating all-pair attention across massive points, and the risks of over-confident predictions due to varying point density. To address these challenges, we propose a new hierarchical spatial transformer model, which includes multi-resolution representation learning within a quad-tree hierarchy and efficient spatial attention via coarse approximation. We also design an uncertainty quantification branch to estimate prediction confidence related to input feature noise and point sparsity. We provide a theoretical analysis of computational time complexity and memory costs. Extensive experiments on both real-world and synthetic datasets show that our method outperforms multiple baselines in prediction accuracy and our model can scale up to one million points on one NVIDIA A100 GPU. The code is available at https://***/spatialdatasciencegroup/HST. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Location based services

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6GAI: Active IPv6 Address Generation via Adversarial Training with Leaked information 27

6GAI: Active IPv6 Address Generation via Adversarial Trainin...

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27th International Conference on computer Supported Cooperative Work in Design, CSCWD 2024

作者： Jiao, Liang Zhu, Yujia Zhang, Wenxiu Zhao, Lei Zhou, Yi Liu, Qingyun Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China National Computer Network Emergency Response Technical Team Coordination Center of China Shandong Branchs Beijing China School of Cyber Security University of Chinese Academy of Sciences National Computer Network Beijing China School of Information Science And Engineering University of Jinan Jinan China National Computer Network Emergency Response Technical Team Coordination Center of China Guangdong Branch Guangdong China

ISBN: (纸本)9798350349184

Global IPv6 scanning has always been a challenge for researchers because of the limited network speed and computational power. In this paper, we introduce 6GAI to implement more efficient target address generation. 6GAI is built with Generative Adversarial Net (GAN) integrated with Convolutional Bottleneck Attention Module (CBAM). 6GAI allows the discriminative net to leak generated address's high-level features extracted by CBAM to the generative net, while the generative net incorporates such informative signals into all generation steps through an additional Manger module, which takes the extracted features of current generated address nybbles and outputs a latent vector to guide the Worker module for active IPv6 address generation. This work outperformed the state-of-the-art target generation algorithms on two datasets including one public dataset and one independently collected dataset. © 2024 IEEE.

关键词： Deep learning

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A Secured and Optimized Broadcast Authentication Scheme for the Internet of Medical Things 4

A Secured and Optimized Broadcast Authentication Scheme for ...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Riaz, Syed Waleed Khan, Fazlullah Ullah, Insaf Alshawi, Bandar Alturki, Ryan Wedyan, Mohammad Abdul Wali Khan University Mardan Computer Science Department KPK Pakistan School of Computer Science University of Nottingham Ningbo China Faculty of Science and Engineering Zhejiang Ningbo315104 China Institute for Analytics and Data Science University of Essex ColchesterCO4 3SQ United Kingdom College of Computing Umm Al-Qura University Department of Computer and Network Engineering Makkah Saudi Arabia College of Computing Umm Al-Qura University Department of Software Engineering Makkah Saudi Arabia Faculty of Information Technology and Computer Sciences Department of Computer Sciences Irbid21163 Jordan

ISBN: (纸本)9798350395914

This Internet of Medical Things (IoMT), facilitates the medical stop regarding real-time monitoring of patients, medical emergency management, remote surgery, patient information management, medical equipment, drug monitoring, etc. However, IoMT devices communicate with each other in an open environment that makes them vulnerable to a wide range of malicious threats from malicious entities. To protect the devices and their associated data, we designed a broadcast authentication scheme for IoMT devices using identity-based public key cryptography that exploits the lightweight features of the Hyper-Elliptic Curve (HEC). We then performed the security analysis based on the Random Oracle Model (ROM), in which we have proved that the proposed scheme is unforgeable under the hardiness of the hyperelliptic curve discrete logarithm problem. The proposed scheme is analyzed in terms of computational and communication overhead and the experimental results justify the superiority of the proposed work in comparison to the existing schemes. © 2024 IEEE.

关键词： Authentication

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Multi-hop emergency message dissemination through optimal cooperative forwarder in grid-based 5G-VANETs

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4461-4476页

作者： Ullah, Sami Abbas, Ghulam Waqas, Muhammad Abbas, Ziaul Haq Halim, Zahid Telecommunications and Networking Research Center GIK Institute of Engineering Sciences and Technology Topi23460 Pakistan Department of Computer Science Shaheed Benazir Bhutto University Sheringal Upper Dir Pakistan Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and Technology Topi23460 Pakistan School of Engineering Edith Cowan University PerthWA6027 Australia Department of Computer Engineering College of Information Technology University of Bahrain Zallaq32038 Bahrain Faculty of Electrical Engineering GIK Institute of Engineering Sciences and Technology Topi23460 Pakistan

Emergency message (EM) dissemination is a significant process in vehicular ad hoc networks (VANETs) and plays a vital role in road safety. Nonetheless, EMs’ dissemination while avoiding broadcast storms poses a considerable challenge. Moreover, selecting optimal cooperative forwarders is also challenging because of unreliability, high end-to-end delay, and packet loss caused by localization errors. Besides, the conventional broadcast-based schemes usually choose forwarder vehicles based on distance, neglecting the influence of links’ qualities. To that end, we propose multi-hop EM dissemination (MEMD) through optimal cooperative forwarder in grid-based fifth-generation (5G) VANET environment. To ensure reliable EMs’ dissemination, we incorporate vehicle-to-everything communication instead of the conventional short-range vehicle-to-vehicle communication. The proposed scheme obtains vehicle positions by integrating GPS measurements with a map-matching technique to avoid vehicle localization errors. Instead of a single parameter, the proposed scheme selects an optimal cooperative forwarder by using our proposed fitness function, which considers different mobility and social parameters. The proposed scheme categorizes EMs’ dissemination into single-hop and multi-hop broadcasting to avoid unnecessary rebroadcasting and communication congestion in the network. Simulation results demonstrate that for varying density, the proposed scheme achieves an average increase of 12%, 9%, and 8.9% in coverage, and 7.95%, 4%, and 3.4% in PDR, and an average reduction of 13.5%, 7.2%, and 12.7% in E2E delay, and 20.9%, 11.3%, and 4% in rebroadcast ratio, as compared to LCL, REMD, and EMDZoI, respectively. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Vehicular ad hoc networks

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Automatic Clustering of User Behaviour Profiles for Web Recommendation System

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Intelligent Automation & Soft Computing 2023年第3期35卷 3365-3384页

作者： S.Sadesh Osamah Ibrahim Khalaf Mohammad Shorfuzzaman Abdulmajeed Alsufyani K.Sangeetha Mueen Uddin Department of Computer Science and Engineering Velalar College of Engineering and TechnologyErode638012Tamil NaduIndia Al-Nahrain Nanorenewable Energy Research Center Al-Nahrain UniversityBaghdad64074Iraq Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060Tamil NaduIndia School of Digital Science University Brunei DarussalamJln Tungku LinkGadong BE1410Brunei Darussalam

Web usage mining,content mining,and structure mining comprise the web mining ***-Page Recommendation(WPR)development by incor-porating Data Mining Techniques(DMT)did not include end-users with improved performance in the obtainedﬁltering *** cluster user proﬁle-based clustering process is delayed when it has a low precision *** Chain Monte Carlo-Dynamic Clustering(MC2-DC)is based on the User Behavior Proﬁle(UBP)model group’s similar user behavior on a dynamic update of *** Reversible-Jump Concept(RJC)reviews the history with updated UBP and moves to appropriate ***’s Filtering Framework(HFF)is designed toﬁlter user data based on personalised information on automatically updated UBP through the Search Engine(SE).The Hamilton Filtered Regime Switching User Query Probability(HFRSUQP)works forward the updated UBP for easy and accurateﬁltering of users’interests and improves WPR.A Probabilistic User Result Feature Ranking based on Gaussian Distribution(PURFR-GD)has been developed to user rank results in a web mining ***-GD decreases the delay time in the end-to-end workﬂow for SE personalization in various meth-ods by using the Gaussian Distribution Function(GDF).The theoretical analysis and experiment results of the proposed MC2-DC method automatically increase the updated UBP accuracy by 18.78%.HFRSUQP enabled extensive Maximize Log-Likelihood(ML-L)increases to 15.28%of User Personalized information Search Retrieval Rate(UPISRT).For feature ranking,the PURFR-GD model deﬁnes higher Classiﬁcation Accuracy(CA)and Precision Ratio(PR)while uti-lising minimum Execution Time(ET).Furthermore,UPISRT's ranking perfor-mance has improved by 20%.

关键词： Data mining web mining process search engine web-page recommendation accuracy

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