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Deep learning and ensemble methods for anomaly detection in ICS security

International Journal of Information technology (Singapore) 2025年第3期17卷 1761-1775页

作者： Hossain, Md. Alamgir Hasan, Tahmid Karovic, jr, Vincent Abdeljaber, Hikmat A. M. Haque, Md. Alimul Ahmad, Sultan Zafar, Aasim Nazeer, Jabeen Mishra, B.K. Department of Computer Science and Engineering State University of Bangladesh South Purbachal Kanchan Dhaka 1461 Bangladesh Department of Computer Science and Engineering Prime University Dhaka Bangladesh Department of Information Systems Faculty of Management Comenius Bratislava Odbojárov 10 Bratislava 25 82005 Slovakia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman 11931 Jordan MEU Research Unit Middle East University Amman 11831 Lebanon Department of Computer Science Veer Kunwar Singh University Bihar Ara India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Computer Science Aligarh Muslim University U.P Aligarh 202002 India P.G. Department of Physics Veer Kunwar Singh University Bihar Ara India

This research addresses the escalating threats to industrial control systems by introducing a novel approach that combines deep learning for feature selection with a robust ensemble-based classification technique to enhance anomaly detection. Our method utilizes a tailored autoencoder architecture to efficiently select features, followed by a Random Forest classifier to ensure reliable and generalizable detection of anomalies. Evaluated on the HAI Security Dataset, the proposed approach demonstrates exceptional performance, with significant improvements in detecting anomalous activities in ICS. The results highlight the method’s potential to enhance ICS security by providing a scalable and adaptable framework that evolves with emerging threats. This study not only advances cybersecurity methodologies for industrial systems but also lays the groundwork for future research, emphasizing the need for innovative and effective security solutions in the face of evolving cyber challenges. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Anomaly detection in ICS Cyber-resilience in critical infrastructure Deep learning for feature selection Industrial control systems security Random forest classifier for ICS

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Real Time Sound Source Localization Using von-Mises ResNet

Real Time Sound Source Localization Using von-Mises ResNet

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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Bozkurtlar, Mert Yen, Benjamin Itoyama, Katsutoshi Nakadai, Kazuhiro School of Engineering Tokyo Institute of Technology Department of Systems and Control Engineering 2-12-1 Ookayama Meguro-ku Tokyo152-8552 Japan Istanbul Technical University Faculty of Management Department of Industrial Engineering Maçka/Beşiktaş/Harbiye Istanbul34367 Turkey Istanbul Technical University Faculty of Computer and Informatics Engineering Department of Computer Engineering Sariyer/Ayazaga Istanbul34485 Turkey The Nakadai Lab Japan Honda Research Institute Japan Co. Ltd 8-1 Honcho Saitama Wako351-0188 Japan

ISBN: (纸本)9798350312072

This paper addresses the task of learning periodic information using deep neural networks to achieve real-time, environment-independent sound source localization. Previous papers showed phase data is the most significant cue in sound source localization tasks and the proposed vM-B DNN was validated to be able to handle such periodic information using on synthesized data. However, they haven't shown its effectiveness and robustness in realistic use cases. This paper introduces a more complex model based on residual networks and adapts vM-B activation function for convolutional layers for use cases that require real-time predictions in dynamically changing environments. © 2024 IEEE.

关键词： Deep neural networks

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Massively Annotated Datasets for Assessment of Synthetic and Real Data in Face Recognition

arXiv

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arXiv 2024年

作者： Neto, Pedro C. Mamede, Rafael M. Albuquerque, Carolina Gonçalves, Tiago Sequeira, Ana F. Faculty of Engineering the University of Porto Porto Portugal Institute for Systems and Computer Engineering Technology and Science Porto Portugal

Face recognition applications have grown in parallel with the size of datasets, complexity of deep learning models and computational power. However, while deep learning models evolve to become more capable and computational power keeps increasing, the datasets available are being retracted and removed from public access. Privacy and ethical concerns are relevant topics within these domains. Through generative artificial intelligence, researchers have put efforts into the development of completely synthetic datasets that can be used to train face recognition systems. Nonetheless, the recent advances have not been sufficient to achieve performance comparable to the state-of-the-art models trained on real data. To study the drift between the performance of models trained on real and synthetic datasets, we leverage a massive attribute classifier (MAC) to create annotations for four datasets: two real and two synthetic. From these annotations, we conduct studies on the distribution of each attribute within all four datasets. Additionally, we further inspect the differences between real and synthetic datasets on the attribute set. When comparing through the Kullback–Leibler divergence we have found differences between real and synthetic samples. Interestingly enough, we have verified that while real samples suffice to explain the synthetic distribution, the opposite could not be further from being true. Copyright © 2024, The Authors. All rights reserved.

关键词： Face recognition

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Performance of different Machine Learning Techniques for the Prediction of Heart Diseases

Performance of different Machine Learning Techniques for the...

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2021 International Conference on Forensics, Analytics, Big Data, Security, FABS 2021

作者： Sarveshvar, M.R. Gogoi, Anindita Chaubey, Amrit Kumar Rohit, S. Mahesh, T.R. Department of Computer Science and Engineering Faculty of Engineering and Technology Bangalore India

ISBN: (纸本)9781665420051

Machine Learning (ML), which is the most well known sub area Artificial Intelligence, is revolutionizing the area of research. ML is primarily an subset of Artificial Intelligence(AI) that has been a crucial component of digitalization solutions that has gotten a lot of buzz in the digital world. In this work, ML is utilized to determine whether or not a person has cardiac disease. ML may be used to determine if a person has a cardiovascular illness based on particular characteristics such as chest discomfort, cholesterol levels, age, and other factors. Cardiovascular disease diagnosis can be simplified using ML classification algorithms that are based on supervised learning. To distinguish those with cardiac illness from those who do not, many ML algorithms are being used including Naïve Bayes Classifier, Logistic Regression Classifier and Random Forest.. The dataset contains certain irrelevant features that are removed during the data cleaning stage, and the data is also standardized for better results. The results and analyses of the publicly available ML Heart Disease dataset are being compared in this paper using different ML methods. The accuracy as well as confusion matrix are also being used to validate a good amount of promising outcomes. © 2021 IEEE.

关键词： Machine learning

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Comparative Study of the Resolution of Ge-on-Si Photodetectors for 1μ m Infrared Signals 1

Comparative Study of the Resolution of Ge-on-Si Photodetecto...

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1st IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2022

作者： Jahangiri, Mojtaba Sberna, Paolo Sammak, Amir Nihtianov, Stoyan Mathematics and Computer Science Delft University of Technology Faculty of Electrical Engineering Department of Microelectronics Delft Netherlands Delft Netherlands

ISBN: (纸本)9798350398069

Most of the studies on narrow-band near-infrared detection reported so far are related to the 1.3μm and 1.55μm spectral windows. There is insufficient research work done on radiation detection in the narrow band around 1 μm wavelength, which is just outside the Si (0.95μ m) and GaAs (0.85μ m) effective cut-off spectral sensitivity. This paper presents a p+n Ge-on-Si detector with a customized large active window, employing the PureGaB technology, to detect radiation in a very narrow band around 1μ m. The advantages of the proposed detector are: (1) CMOS-compatibility and micro-spectroscopic capability;(2) low dark current and high photoresponsivity, compared to similar devices reported in the literature;(3) enhanced sensitivity to weak radiation by realizing an ultra-shallow and very thin depletion region. These detectors can be good candidates for measuring the YAG laser radiation and measuring stray radiation in photolithography. © 2022 IEEE.

关键词： III-V semiconductors

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Versatile Tasks on Integrated Aerial Platforms Using Only Onboard Sensors: Control, Estimation, and Validation

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IEEE Transactions on Robotics 2025年 41卷 3518-3538页

作者： Wang, Kaidi Lai, Ganghua Yu, Yushu Du, Jianrui Sun, Jiali Xu, Bin Franchi, Antonio Sun, Fuchun Beijing Institute of Technology School of Mechatronical Engineering Beijing100081 China Beijing Institute of Technology School of Mechanical Engineering Beijing100081 China University of Twente Robotics and Mechatronics Lab Faculty of Electrical Engineering Mathematics and Computer Science Enschede7500 AE Netherlands Tsinghua University Department of Computer Science and Technology China

Connecting multiple aerial vehicles to a rigid central platform through passive spherical joints holds the potential to construct a fully-actuated aerial platform. The integration of multiple vehicles enhances efficiency in tasks like mapping and object reconnaissance. This paper proposes a control and state estimation framework for the Integrated Aerial Platform (IAP), enabling it to perform versatile tasks like object reconnaissance and physical interactive tasks with only onboard sensors. In the framework, the 6D motion control serves as the low-level controller, while the high-level controller comprises a 6D admittance filter and a perception-aware attitude correction module. The 6D admittance filter, serving as the interaction controller, is adaptable for aerial interaction tasks. The perception-aware attitude correction algorithm is carefully designed by adopting a geometric Model Predictive Controller (MPC). This algorithm, incorporating both offline and online calculations, proves to be well-suited for the intricate dynamics of an IAP. A 6D direct wrench controller is also developed for the IAP. Notably, both the interaction controller and the direct wrench controller operate without reliance on force/torque sensors. Instead, a wrench observer algorithm is devised, considering external disturbances. Additionally, based on the kinematics constraints of the multiple aerials in the platform, a fusion algorithm for multiple Visual-Inertial Odometry (VIO) and kinematics constraints is developed, providing more accurate localization. A prototype of the IAP is constructed, and its capabilities are demonstrated through experiments including perception-aware object reconnaissance, aerial mapping, aerial peg-in-hole task, and 6D contact wrench generation. All experiments are conducted exclusively with onboard sensors. These tasks exemplify the merits of the proposed IAP and validate the effectiveness of the proposed control framework and fusion algorithm. © 2004-2012

关键词： State estimation

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Animating Sustainable Tourism: A Literature Review Analysis of technology's Role, Challenges, and Opportunities

Animating Sustainable Tourism: A Literature Review Analysis ...

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2023 International Conference for Technological engineering and its Applications in Sustainable Development, ICTEASD 2023

作者： Nasir, Suraya Md Ekram Alhafis Bin Hashim, Mohd Wan Mustafa, Wan Azani Hazlen Kamaruddin, Nor Alabdeli, Haider Hanafi, Hafizul Fahri Sultan Idris Educational University Faculty of Art Sustainability & Creative Industry Multimedia Creative Departmet Perak Malaysia Centre of Excellence Malaysia The Islamic University Faculty of Engineering Najaf Iraq Sultan Idris Educational University Faculty of Computing and Meta-Technology Department of Computer Science and Digital Technology Perak Malaysia

ISBN: (纸本)9798350336474

This research article explores the intricate nexus between animation technology and sustainable tourism animation, conducting an extensive literature review to scrutinize the role, hurdles, and prospects that technology presents in promoting sustainability within the realm of tourism. The outcomes enrich our comprehension of how animation technology intersects with and shapes sustainable practices in tourism, illuminating potential advancements and areas necessitating attention. By dissecting the key technologies employed in sustainable tourism animation, scrutinizing the challenges in their integration, identifying opportunities for their utilization, and assessing their impact on the sustainability of animation, each facet contributes to a comprehensive understanding of sustainable tourism animation's influence on contemporary artistic practices, technological innovations, and educational strategies. Drawing insights from diverse academic sources, peer-reviewed journals, and case studies, this study offers an integrated perspective on the transformative capacity of sustainable animation. It delves into fostering creative expression, encouraging interdisciplinary collaboration, and promoting critical engagement with emerging technologies in the digital age. © 2023 IEEE.

关键词： Animation

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Fairness Under Cover: Evaluating the Impact of Occlusions on Demographic Bias in Facial Recognition

arXiv

引用

arXiv 2024年

作者： Mamede, Rafael M. Neto, Pedro C. Sequeira, Ana F. Faculty of Engineering The University of Porto Porto Portugal Institute for Systems and Computer Engineering Technology and Science Porto Portugal

This study investigates the effects of occlusions on the fairness of face recognition systems, particularly focusing on demographic biases. Using the Racial Faces in the Wild (RFW) dataset and synthetically added realistic occlusions, we evaluate their effect on the performance of face recognition models trained on the BUPT-Balanced and BUPT-GlobalFace datasets. We note increases in the dispersion of FMR, FNMR, and accuracy alongside decreases in fairness according to Equilized Odds, Demographic Parity, STD of Accuracy, and Fairness Discrepancy Rate. Additionally, we utilize a pixel attribution method to understand the importance of occlusions in model predictions, proposing a new metric, Face Occlusion Impact Ratio (FOIR), that quantifies the extent to which occlusions affect model performance across different demographic groups. Our results indicate that occlusions exacerbate existing demographic biases, with models placing higher importance on occlusions in an unequal fashion, particularly affecting African individuals more severely. Copyright © 2024, The Authors. All rights reserved.

关键词： Face recognition

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LiDAR Spoofing Attack Detection in Autonomous Vehicles

LiDAR Spoofing Attack Detection in Autonomous Vehicles

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2022 IEEE International Conference on Consumer Electronics, ICCE 2022

作者： Alheeti, Khattab M. Ali Alzahrani, Abdulkareem Al Dosary, Duaa College of Computer and Information Technology University of Anbar Computer Networking Systems Dept. Ramadi Iraq Albaha University Faculty of Computer Science and Information Technology Computer Engineering and Science Department Albaha Saudi Arabia College of Computer Sciences and Information Technology University of Anbar Ramadi Iraq

ISBN: (纸本)9781665441544

Detecting 3D objects is a core task for autonomous vehicles (AVs), as it allows them to drive safely and responsibly. To detect objects quickly and accurately, AVs have LiDAR sensors, which can capture 3D data from 360° in various conditions, including harsh weather. However, LiDAR sensors may face a spoofing attack via laser satirizing. This type of attack does not require physical contact with the sensors nor does it need to change them. It can mislead AVs by providing them with false information, which can then put passengers, pedestrians, and other vehicles in danger. Therefore, this paper proposes a model utilizing machine learning (i.e., decision trees) to protect AVs from such attacks. The performance of the proposed model indicates that it can detect LiDAR spoofing attacks. © 2022 IEEE.

关键词： Autonomous vehicles

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A computational supervised neural network procedure for the fractional SIQ mathematical model

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European Physical Journal: Special Topics 2023年第5期232卷 535-546页

作者： Mukdasai, Kanit Sabir, Zulqurnain Raja, Muhammad Asif Zahoor Singkibud, Peerapongpat Sadat, R. Ali, Mohamed R. Department of Mathematics Faculty of Science Khon Kaen University Khon Kaen 40002 Thailand Department of Mathematics and Statistics Hazara University Mansehra Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan Department of Applied Mathematics and Statistics Faculty of Science and Liberal Arts Rajamangala University of Technology Isan Nakhon Ratchasima 30000 Thailand Department of Mathematics Faculty of Engineering Zagazig University Zagazig Egypt Faculty of Engineering and Technology Future University in Egypt New Cairo 11835 Egypt Basic Engineering Science Department Benha Faculty of Engineering Benha University Benha Egypt Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon

The purpose of the current work is to provide the numerical solutions of the fractional mathematical system of the susceptible, infected and quarantine (SIQ) system based on the lockdown effects of the coronavirus disease. These investigations provide more accurateness by using the fractional SIQ system. The investigations based on the nonlinear, integer and mathematical form of the SIQ model together with the effects of lockdown are also presented in this work. The impact of the lockdown is classified into the susceptible/infection/quarantine categories, which is based on the system of differential models. The fractional study is provided to find the accurate as well as realistic solutions of the SIQ model using the artificial intelligence (AI) performances along with the scale conjugate gradient (SCG) design, i.e., AI-SCG. The fractional-order derivatives have been used to solve three different cases of the nonlinear SIQ differential model. The statics to perform the numerical results of the fractional SIQ dynamical system are 7% for validation, 82% for training and 11% for testing. To observe the exactness of the AI-SCG procedure, the comparison of the numerical attained performances of the results is presented with the reference Adam solutions. For the validation, authentication, aptitude, consistency and validity of the AI-SCG solver, the computing numerical results have been provided based on the error histograms, state transition measures, correlation/regression values and mean square error. © 2022, The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature.

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