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SEADer++: social engineering attack detection in online environments using machine learning

Journal of Information and Telecommunication 2020年第3期4卷 346-362页

作者： Lansley, Merton Mouton, Francois Kapetanakis, Stelios Polatidis, Nikolaos Department of Informatics University of Sussex Brighton UK Department of Applied Science Noroff University College Oslo Norway Department of Computer Science University of the Western Cape Bellville South Africa Department of Computer Science University of the Western Cape Bellville South Africa School of Computing Engineering and Mathematics University of Brighton Brighton UK

ABSTRACTFormulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to *** engineering attacks are one of the most well-known and easiest to apply attacks in the cybersecurity domain. Research has shown that the majority of attacks against computer systems was based on the use of social engineering methods. Considering the importance of emerging fields such as machine learning and cybersecurity we have developed a method that detects social engineering attacks that is based on natural language processing and artificial neural networks. This method can be applied in offline texts or online environments and flag a conversation as a social engineering attack or not. Initially, the conversation text is parsed and checked for grammatical errors using natural language processing techniques and then an artificial neural network is used to classify possible attacks. The proposed method has been evaluated using a real dataset and a semi-synthetic dataset with very high accuracy results. Furthermore, alternative classification methods have been used for comparisons in both datasets.

关键词： Social engineering online chat environments natural language processing machine learning neural networks ensemble learning

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Natural convection heat transport performance of nanofluids under the influence of inclined magnetic field

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Results in Physics 2024年 58卷

作者： Islam, Tarikul Fayz-Al-Asad, Md. Khatun, M.A. Parveen, N. Ahmad, Hijaz Askar, Sameh CMUP University of Porto Porto Portugal Department of Mathematics American International University –Bangladesh Khilkhet Kuratoli Dhaka-1229 Bangladesh Department of Mathematics Jagannath University Dhaka 1000 Bangladesh Department of Mathematics Bangladesh University of Engineering and Technology Dhaka-1000 Bangladesh Near East University Operational Research Center in Healthcare Nicosia PC: 99138 TRNC Mersin 10 Turkey Center for Applied Mathematics and Bioinformatics Gulf University for Science and Technology Mishref Kuwait Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Mathematics and Informatics Azerbaijan University Jeyhun Hajibeyli street 71 AZ1007 Baku Azerbaijan Department of Statistics and Operations Research College of Science King Saud University P.O. Box 2455 Riyadh 11451 Saudi Arabia

Two-dimensional free convective temperature transportation and fluid flow within the semi-circular cavity occupied by nanofluid with influence of inclined MHD is studied numerically. While bottom wall is warmed by three distinct thermal conditions (uniform, parabolic, and sinusoidal), the upper circular wall is warmed at lower temperatures. A slopping magnetic field acts upon the enclosure whose strength is unchanging. The governing non-linear equations is resolved with the numerical scheme Galerkin type finite element technique. A strong agreement is found with the earlier work. The outcomes for the model parameters likely Rayleigh number, sloping angle of magnetic field, nanoparticles volume, size, shape, and Brownian effects, and Hartmann number are demonstrated through streamlines, isothermal lines, and mean Nusselt number. In addition, investigation is conducted the thermal features of various nanoparticles and the nanofluid produced from them. The outcomes reveal that thermal transfer rate is significantly enhanced by volume fraction nanoparticles and buoyancy-driven parameter Rayleigh number whilst reduces for higher Hartman number. The inclined angles of magnetic field and different thermal conditions have a positive influence on thermal transport. For Cu-H₂O nanofluids, thermal transport is enhanced by 10.98 % in a uniform thermal system, where it increases by 14.42 % in the parabolic thermal system and by 15.53 % in a sinusoidal thermal system for the only 5 % volume of nanoparticles. Moreover, nano-sized particles’ shape factor has a noteworthy role in thermal transport. It is found that heat transfer rate rises by 6.79 % for blade-shaped nanoparticles. Additionally, as considering Brownian effects of nanoparticles, the temperature transport rate is improved by 21.45 % for small-sized nanoparticles. © 2024 The Authors

关键词： Finite Element Method Inclined magnetic field Nanoparticles Natural convection Semi-circular cavity

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REAL-TIME GEOINFORMATION SYSTEMS TO IMPROVE THE QUALITY, SCALABILITY, AND COST OF INTERNET OF THINGS FOR AGRI-ENVIRONMENT RESEARCH

arXiv

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arXiv 2024年

作者： Runck, Bryan C. Schulz, Bobby Bishop, Jeff Carlson, Nathan Chantigian, Bryan Deters, Gary Erdmann, Jesse Ewing, Patrick M. Felzan, Michael Fu, Xiao Greyling, Jan Hogan, Christopher J. Hollman, Andrew Joglekar, Ali Junker, Kris Kantar, Michael Kaunda, Lumbani Krishna, Mohana Lynch, Benjamin Marchetto, Peter Marsolek, Megan McKay, Troy Morris, Brad Niaghi, Ali Rashid Pamulaparthy, Keerthi Pardey, Philip Piotrowski, Ann Poudyal, Christina Prather, Tom Raghavan, Barath Reiter, Maggie Rosen, Lucas Salazar, Benjamin Scobbie, Andrew Sharma, Vasudha Silverstein, Kevin A.T. Singh, Gurparteet Strock, Jeff Subedi, Samikshya Tang, Evan Turturillo, Gianna Watkins, Eric Webster, Blake Wilgenbusch, James GEMS Informatics Center University of Minnesota Twin Cities United States Department of Geography Environment and Society University of Minnesota Twin Cities United States Minnesota Supercomputing Institute University of Minnesota United States Department of Electrical Engineering University of Minnesota Twin Cities United States Department of Applied Economics University of Minnesota Twin Cities United States Department of Horticultural Science University of Minnesota Twin Cities United States Department of Computer Science University of Southern California United States Sensing Inc. Conservify Graphspan LLC USDA-ARS Food Systems Research Unit BurlingtonVT05405 United States Amazon United States Department of Soil Water and Climate University of Minnesota Twin Cities United States Department of Computer Science and Engineering University of Minnesota Twin Cities United States Departmnet of Agricultural Education Communication and Marketing University of Minnesota Twin Cities United States Benson Hill United States Southwest Research and Outreach Center University of Minnesota Twin Cities United States Code Weavers Department of Mechanical Engineering University of Minnesota Twin Cities United States University of Hawaii at Manoa HonoluluHI United States Minnesota Agricultural Experiment Station University of Minnesota Twin Cities United States Sunday Stellenbosch AgroInformatics Initiative Stellenbosch University South Africa Ameriprise Financial Services LLC Optum University of Minnesota Twin Cities United States

With the increasing emphasis on machine learning and artificial intelligence to drive knowledge discovery in the agricultural sciences, spatial internet of things (IoT) technologies have become increasingly important for collecting real-time, high resolution data for these models. However, managing large fleets of devices while maintaining high data quality remains an ongoing challenge as scientists iterate from prototype to mature end-to-end applications. Here, we provide a set of case studies using the framework of technology readiness levels for an open source spatial IoT system. The spatial IoT systems underwent 3 major and 14 minor system versions, had over 2,727 devices manufactured both in academic and commercial contexts, and are either in active or planned deployment across four continents. Our results show the evolution of a generalizable, open source spatial IoT system designed for agricultural scientists, and provide a model for academic researchers to overcome the challenges that exist in going from one-off prototypes to thousands of internet-connected devices. © 2024, CC BY-NC-ND.

关键词： Internet of things

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AB1556 MACHINE LEARNING APPROACH FOR CLASSIFICATION OF ARTHRITIS ACTIVITY STATE, USING DATA FROM A SINGLE ACCELEROMETER

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Annals of the Rheumatic Diseases 2023年 82卷 2012-2012页

作者： P. Mielnik A. Myhre Hjelle A. Traseth K. Tokarz B. Pollen M. Fojcik Helse Forde Section for Rheumatology； Department for Neurology Rheumatology and Physical Medicine Forde Norway Institute of Informatics Silesian University of Technology Department of Graphics Computer Vision and Digital Systems Gliwice Poland Western Norway University of Applied Sciences Faculty of Engineering and Science Forde Norway

Background Machine learning (ML) potential is not fully exploited in diagnostics and follow up of autoimmune inflammatory rheumatic disease (AIIRD). It is despite the broader use of ML in, e.g. imaging diagnostics. The specific tools for AIIRD are lacking. Objectives This is an interim analysis of data from the first checkpoint of the proof-of-concept study on using accelerometer (ACC) data in follow up of patients with AIIRD. The main goal of the study was to investigate the value of single ACC data in the classification of arthritis activity status. Methods Subjects with AIIRD are enrolled in the study when they start of new treatment due to the disease activity. Several comorbidities, such as severe neurological and cardiological disorders, as wells as impaired mobility, are part of the protocol exclusion criteria. Volunteers without AIIRD who fulfil other inclusion/exclusion criteria are included as controls. The study was approved by local competent authorities and informed consent was given by all participants. We used data collected up till the first study checkpoint. This analysis covers nine patients with AIIRD (5 rheumatoid and four psoriasis arthritis) and 13 controls. Five patients had 3 study visits, 1 had 2 visits and 3 only one. Controls have only one visit per protocol. We analysed ACC data from 3 minutes of clapping using a home-brewed device based on Arduino nano 33 BLE with 6-axis MTU by Nordic Semiconductor. Data was divided into 6-second chunks that were found optimal in our prior study. We conducted binary classification between any/ no arthritis in any of the upper extremities. We used accuracy and area under curve (AUC) as an efficacy function derived from the receiver operating characteristic curve (ROC). We extracted 54 features from 3 ACC axis signal. The features encompassed, among others, Fourier's components, autoregression coefficients, median absolute deviation (MAD), variance, Fourier's entropy, etc. We built linear discriminant anal

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The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue

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Medical Image Analysis 2024年 97卷 103257页

作者： Weitz, Philippe Valkonen, Masi Solorzano, Leslie Carr, Circe Kartasalo, Kimmo Boissin, Constance Koivukoski, Sonja Kuusela, Aino Rasic, Dusan Feng, Yanbo Pouplier, Sandra Sinius Sharma, Abhinav Eriksson, Kajsa Ledesma Robertson, Stephanie Marzahl, Christian Gatenbee, Chandler D. Anderson, Alexander R.A. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Budelmann, Daniel Weiss, Nick Heldmann, Stefan Lotz, Johannes Wolterink, Jelmer M. De Santi, Bruno Patil, Abhijeet Sethi, Amit Kondo, Satoshi Kasai, Satoshi Hirasawa, Kousuke Farrokh, Mahtab Kumar, Neeraj Greiner, Russell Latonen, Leena Laenkholm, Anne-Vibeke Hartman, Johan Ruusuvuori, Pekka Rantalainen, Mattias Department of Medical Epidemiology and Biostatistics Karolinska Insitutet Stockholm Sweden Institute of Biomedicine University of Turku Turku Finland Institute of Biomedicine University of Eastern Finland Kuopio Finland Department of Surgical Pathology Zealand University Hospital Roskilde Denmark Department of Oncology and Pathology Karolinska Institutet Stockholm Sweden Gestalt Diagnostics Spokane United States Department of Integrated Mathematical Oncology Moffitt Cancer Center Tampa United States Informatics Institute University of Applied Sciences Western Switzerland Switzerland Department of Measurement and Electronics AGH University of Kraków Poland Department of Computer Science University of Geneva Geneva Switzerland Department of Neuroscience University of Padova Italy Medical Faculty University of Geneva Switzerland Fraunhofer Institute for Digital Medicine MEVIS Lübeck Germany Department of Applied Mathematics Technical Medical Centre University of Twente Enschede Netherlands Multimodality Medical Imaging Technical Medical Centre University of Twente Enschede Netherlands Department of Electrical Engineering Indian Institute of Technology Bombay India Graduate School of Engineering Muroran Institute of Technology Hokkaido Japan Faculty of Medical Technology Niigata University of Health and Welfare Niigata Japan FORXAI Business Operations Konica Minolta Inc. Osaka Japan Department of Computing Science University of Alberta EdmontonAlberta United States Alberta Machine Intelligence Institute Edmonton Canada MedTechLabs BioClinicum Karolinska University Hospital Stockholm Sweden Faculty of Medicine and Health Technology Tampere University Tampere Finland

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results provide a comparison of the performance of current WSI registration methods and guide researchers in selecting and developing methods. © 2024

关键词： Tissue

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Secure Human Action Recognition by Encrypted Neural Network Inference

arXiv

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arXiv 2021年

作者： Kim, Miran Jiang, Xiaoqian Lauter, Kristin Ismayilzada, Elkhan Shams, Shayan Department of Mathematics Hanyang University Seoul Korea Republic of Department of Computer Science Hanyang University Seoul Korea Republic of School of Biomedical Informatics University of Texas Health Science Center HoustonTX United States SeattleWA United States Department of Computer Science and Engineering Ulsan National Institute of Science and Technology Ulsan Korea Republic of Department of Applied Data Science San Jose State University San JoseCA United States

Advanced computer vision technology can provide near real-time home monitoring to support "aging in place" by detecting falls and symptoms related to seizures and stroke. Affordable webcams, together with cloud computing services (to run machine learning algorithms), can potentially bring significant social benefits. However, it has not been deployed in practice because of privacy concerns. In this paper, we propose a strategy that uses homomorphic encryption to resolve this dilemma, which guarantees information confidentiality while retaining action detection. Our protocol for secure inference can distinguish falls from activities of daily living with 86.21% sensitivity and 99.14% specificity, with an average inference latency of 1.2 seconds and 2.4 seconds on real-world test datasets using small and large neural nets, respectively. We show that our method enables a 613x speedup over the latency-optimized LoLa and achieves an average of 3.1x throughput increase in secure inference compared to the throughput-optimized nGraph-HE2. Copyright © 2021, The Authors. All rights reserved.

关键词： Cryptography

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On the variational perspectives to the graph isomorphism problem

arXiv

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arXiv 2021年

作者： Chatterjee, Turbasu Mohtashim, Shah Ishmam Kundu, Akash Department of Computer Science and Engineering Calcutta Institute of Engineering and Management Maulana Abul Kalam Azad University of Technology Kolkata India Department of Chemistry University of Dhaka Dhaka Bangladesh Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland Joint Doctoral School Silesian University of Technology Akademicka 2a Gliwice44-100 Poland

This paper studies the Graph Isomorphism Problem from a variational algorithmic perspective, specifically studying the Quadratic Unconstrained Binary Optimization (QUBO) formulation of the Graph Isomorphism Problem and subsequent execution using the Quantum Approximate Optimization Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE). This study presents the results of these algorithms and the variations that occur therein for graphs of four and five nodes. The main findings of this paper include the clustering in the energy landscape for the QAOA in isomorphic graphs having an equal number of nodes and edges. This trend found in the QAOA study was further reinforced by studying the ground state energy reduction using VQEs. Furthermore, this paper examines the trend under which isomorphic pairs of graphs vary in the ground state energies, with varying edges and nodes. © 2021, CC BY.

关键词： Ground state

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Generative Federated Learning with Small and Large Models In Consumer Electronics for Privacy preserving Data Fusion in Healthcare Internet of Things

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IEEE Transactions on Consumer Electronics 2025年

作者： Ghazal, Taher M. Islam, Shayla Hasan, Mohammad Kamrul Abu-Shareha, Ahmad A. Mokhtar, Umi A. Khan, M. Attique Baili, Jamel Saeed, Ali Q. Bhattt, Mohammed Wasim Ahmad, Munir Department of Networks and Cybersecurity Hourani Center for Applied Scientific Research Amman Jordan UCSI University Institute of Computer Science and Digital Innovation Kuala Lumpur56000 Malaysia Center for Cyber Security Faculty of Information Science and Technology Malaysia Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Department of Data Science and Artificial Intelligence Amman Jordan Prince Mohammad bin Fahd University Saudi Arabia King Khalid University College of Computer Science Department of Computer Engineering Abha61413 Saudi Arabia Northern Technical University Computer Center Ninevah Iraq Korea University College of Informatics Seoul02841 Korea Republic of

Healthcare Internet of Things (HIoT) requires large-scale privacy features to ensure maximum security in sharing sensitive physiological data in consumer electronics. Recent approaches utilize the fusion concept to provide maximum privacy in health data sharing. Embedded signing data fusion with the health observed data ensures privacy preserved sharing across heterogeneous medical consumer devices for diagnosis. This article proposes a Dependency-correlated Data Fusion Scheme (DcDFS) to maximize the privacy of the health data-sharing process. The proposed scheme prepares separate key signing procedures using triple-DES (data encryption standard) to embed with the accumulated health data. The fusion process is carried out by defining key headers and integrity footers for authentication and verification. Therefore, the fusion generates a combined sequence of linear authentication and validation procedures enclosing the health data. In this scheme, the role of federated learning is to prevent permuted sequences for the same health data. This research integrates Small Language Model (SLM) and Large Language Model (LLM) into the federated learning module to support secure, scalable, and intelligent healthcare data sharing. Their collaboration enhances context-aware training while preserving privacy across decentralized, encrypted environments. A similar sequence mapped between the header and footer is responsible for discarding unauthorized data handling. The learning process verifies the permutation for many-to-one header to footer and vice versa. Therefore, the proposed fusion scheme generates a linear dependency between the actual and security-related data for maximum privacy. The proposed scheme achieves the following: the computation time is confined by 12.424%, the privacy leakage by 12.923%, and the computation efficiency is improved by 11.46%, as observed under the maximum sequences. © 1975-2011 IEEE.

关键词： Sensitive data

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EXTENDED ANALYSIS OF DATA CLEANING FOR ELECTRICAL ENERGY CONSUMPTION DATA OF PUBLIC BUILDINGS

EXTENDED ANALYSIS OF DATA CLEANING FOR ELECTRICAL ENERGY CON...

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The 12th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MEDPOWER 2020)

作者： D. I. Jurj A. Polycarpou L. Czumbil A. G. Berciu M. Lancrajan D. Barar D. D. Micu Electrical Engineering Department Technical University of Cluj-Napoca Cluj-Napoca Romania Department of Electrical and Computer Engineering and Informatics Frederick University Nicosia Cyprus Applied Computational Intelligence Babeș-Bolyai University Cluj-Napoca Romania

In the past years both companies and academics communities pulled their efforts in generating input that consist in new abstractions, interfaces, approaches for scalability and crowdsourcing techniques. Quantitative and qualitative methods were created with the scope of error reduction and were covered in multiple surveys and overviews in order to cope with outlier detection. The aim of tis paper is to provide an outlier analysis over the consumption data of twelve public buildings from the Technical University of Cluj-Napoca, collected during an EU 2020 Horizon project.

关键词： building management systems data handling power consumption power engineering computing crowdsourcing

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IoT-UAV Enabled Intelligent Resource Management in Low-Carbon Smart Agriculture Using Federated Reinforcement Learning

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IEEE Transactions on Consumer Electronics 2025年

作者： Alasbali, Nada Masood, Fahad Alnazzawi, Noha Ghaban, Wad Alazeb, Abdulwahab Basurra, Shadi Saeed, Faisal King Khalid University College of Computer Science Department of Informatics and Computing Systems Abha61421 Saudi Arabia Department of Computing Abasyn University Peshawar25000 Pakistan Royal Commission for Jubail and Yanbu Yanbu Industrial College Computer Science and Engineering Department Yanbu46411 Saudi Arabia University of Tabuk Applied College Tabuk47512 Saudi Arabia Najran University College of Computer Science and Information Systems Department of Computer Science Najran61441 Saudi Arabia Birmingham City University College of Computing BirminghamB4 7XG United Kingdom

The Internet of Things (IoT) and unmanned aerial vehicles (UAVs) continue to advance the low-carbon smart agriculture technologies for next-generation consumer electronics and unlock more informed agricultural practices. Reinforcement learning (RL), federated learning (FL), and federated reinforcement learning (FRL) have demonstrated notable achievements in resolving complex problems, including resource allocation, energy efficiency, anomaly detection, and bandwidth utilization for multimodal tasks. This research explores multimodal data analysis and resource optimization using FRL for agricultural consumer electronics. The proposed framework employs IoT devices to monitor temperature, humidity, soil temperature, and soil moisture in real time, while UAVs provide aerial imagery for soil moisture, crop growth, and pest identification across three fields. This framework supports distributed learning, which trains local RL models on each node and combines them into the global model. The proposed FRL model demonstrated significant enhancements, including a 17% reduction in energy consumption for IoT devices and a 15% reduction for UAVs compared to non-FRL methods. This research emphasizes the effectiveness of FRL in integrating IoT and UAV for efficient resource allocation, energy efficiency, and reduced carbon emissions for low-carbon agricultural consumer electronics. © 1975-2011 IEEE.

关键词： Resource allocation

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