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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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In Silico Scenario of Bare Metal Stent and Drug-Eluting Stent Performance modeling in Patient-Specific Arteries: A Comparative Analysis 46

In Silico Scenario of Bare Metal Stent and Drug-Eluting Sten...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Karanasiou, Georgia S. Loukas, Vasileios S. Tachos, Nikolaos S. Kyriakidis, Savvas Sakellarios, Antonis I. Kotoulas, Grigorios Geris, Liesbet Semertzioglou, Arsen Katsouras, Christos Michalis, Lambros K. Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Department of Materials Science and Engineering IoanninaGR 45110 Greece University of Liège Ku Department of Biomechanics and Computational Tissue Engineering Leuven Belgium Rontis Corporation S.A. ZugCH6300 Switzerland University of Ioannina Medical School Department of Cardiology Ioannina Greece Research Institute Forth Department of Biomedical Ioannina Greece

ISBN: (纸本)9798350371499

Atherosclerosis, a chronic inflammatory disease linked to heart attacks and strokes, stimulates the formation of atherosclerotic plaques within arterial vessels, leading to reduced blood flow to the heart. Drug-Eluting Stents (DES) aim to expand the arterial stenosis and restore the blood flow while mitigating neo-intimal thickening through controlled drug release. In silico modeling has been widely used as a reliable means to predict and evaluate stent performance accurately. This in silico study investigates the efficacy of two stent types (Bare Metal Stent- BMS, DES) within a patient-specific coronary artery, examining the impact of stent coating. In both models, most arterial stresses lie within 0-0.5 MPa. Model A has 97.4% within this range, with the remaining 2.6% split between 0.5-0.2 MPa, while Model B has 2.6% between 0.5-0.18 MPa. Maximum von Mises arterial stresses peak at 0.20926 MPa in Model A and 0.18103 MPa in Model B. Peak stress occurs at 719.86 MPa for BMS in Model A and slightly higher at 725.54 MPa for DES in Model B. Our results have shown that there are minor differences between the performance of the BMS and the DES, with stent coating insignificantly altering deployment outcomes and scaffold stresses. © 2024 IEEE.

关键词： Heart

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Semantic Template-based Convolutional Neural Network for Text Classification

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ACM Transactions on Asian and Low-Resource Language information Processing 2023年第11期22卷 1-21页

作者： Chang, Yung-Chun Ng, Siu Hin Chen, Jung-Peng Liang, Yu-Chi Hsu, Wen-Lian Graduate Institute of Data Science Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan Snhcc Tigp Academia Sinica National Cheng Chi University Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering College of Information and Electrical Engineering Asia University Taichung Taiwan Pervasive Ai Research Labs Ministry of Science and Technology Hsinchu City Taiwan

We propose a semantic template-based distributed representation for the convolutional neural network called Semantic Template-based Convolutional Neural Network (STCNN) for text categorization that imitates the perceptual behavior of human comprehension. STCNN is a highly automatic approach that learns semantic templates that characterize a domain from raw text and recognizes categories of documents using a semantic-infused convolutional neural network that allows a template to be partially matched through a statistical scoring system. Our experiment results show that STCNN effectively classifies documents in about 140,000 Chinese news articles into predefined categories by capturing the most prominent and expressive patterns and achieves the best performance among all compared methods for Chinese topic classification. Finally, the same knowledge can be directly used to perform a semantic analysis task. © 2023 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Semantics

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Camouflaged Object Segmentation with Transformer 6th

Camouflaged Object Segmentation with Transformer

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6th International Conference on Cognitive systems and Signal Processing, ICCSIP 2021

作者： Wang, Haiwen Wang, Xinzhou Sun, Fuchun Song, Yixu State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing China School of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (纸本)9789811692468

The Vision Transformer (ViT) [6] directly applies a Transformer architecture to image classification and achieves an impressive result compared with convolutional networks. This paper presents a new ViT-base camouflaged object segmentation method, called COS Transformer, which aims to identify and segment objects concealed in a complex environment. The high intrinsic similarities between object and surrounding makes the task challenging than salient object detection. Most recent camouflaged object segmentation methods(e.g., EGNet [29], PraNet [10] and SINet [9]) adopt convolutional network with an encoder-decoder architecture and focused on increasing the receptive field, which is limited by the depth of the network. In camouflaged object segmentation (COS) task, the camouflage is mainly relied on contrast of the whole surrounding instead of the local information. We introduce transformer with global context awareness in this paper, for self-attention allowing COS Transformer to aggregate features globally even in the lowest layers. Specifically, the architecture is composed of a transformer-based encoder and a multi-layers feature aggregation refinement module. After training on the COD10K [9] dataset, COS Transformer attains excellent results compared to state-of-the-art convolutional networks, e.g. 11.7% improvement of E [8] on the COD10K contrasted to SINet. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Convolution

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6D Movable Antenna Enhanced Interference Mitigation for Cellular-Connected UAV Communications

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IEEE Wireless Communications Letters 2025年第6期14卷 1618-1622页

作者： Ren, Tianshi Zhang, Xianchao Zhu, Lipeng Ma, Wenyan Gao, Xiaozheng Zhang, Rui The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore The Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems China The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing314001 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of HongKong Guangdong Shenzhen518172 China

Cellular-connected unmanned aerial vehicle (UAV) communications is an enabling technology to transmit control signaling or payload data for UAVs through cellular networks. Due to the line-of-sight dominant air-to-ground channels, efficient interference mitigation is crucial to UAV communications, while the conventional fixed-position antenna (FPA) arrays have limited degrees of freedom to suppress the interference between the UAV and its non-associated co-channel base stations (BSs). To address this challenge, we propose in this letter a new approach by utilizing the six-dimensional movable antenna (6DMA) arrays to enhance the interference mitigation for the UAV. Specifically, we propose an efficient block coordinate descent algorithm to iteratively optimize the antenna position vector, array rotation vector, receive beamforming vector, and associated BS of the UAV to maximize its signal-to-interference-plus-noise ratio. Numerical results show that the proposed 6DMA enhanced cellular-connected UAV communication can significantly outperform that with the traditional FPA arrays and other benchmark schemes in terms of interference mitigation. © 2025 IEEE. All rights reserved.

关键词： Aircraft communication

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VisioSLR: A Vision Data-Driven Framework for Sign Language Video Recognition and Performance Evaluation on Fine-Tuned YOLO Models

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Procedia computer science 2025年 257卷 85-92页

作者： Leila Ismail Nada Shahin Henok Tesfaye Alain Hennebelle Intelligent Distributed Computing and Systems (INDUCE) Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Independent Researcher

With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It is an essential component of a sign-to-text translation system to support the deaf and hard-of-hearing population. This paper presents a computer VISIOn data-driven deep learning framework for Sign Language video Recognition (VisoSLR). VisioSLR provides a precise measurement of translating signs for developing an end-to-end computational translation system. Considering the scarcity of sign language datasets, which hinders the development of an accurate recognition model, we evaluate the performance of our framework by fine-tuning the very well-known YOLO models, which are built from a signs-unrelated collection of images and videos, using a small-sized sign language dataset. Gathering a sign language dataset for signs training would involve an enormous amount of time to collect and annotate videos in different environmental setups and multiple signers, in addition to the training time of a model. Numerical evaluations of VisioSLR show that our framework recognizes signs with a mean average precision of 97.4%, 97.1%, and 95.5% and 11, 12, and 12 milliseconds of recognition time on YOLOv8m, YOLOv9m, and YOLOv11m, respectively.

关键词： Artificial intelligence computer vision Deep learning Fine-tuning Machine translation Neural machine translation Sign language Sign language recognition Transfer learning YOLO

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The Iterative Compromise Ranking Analysis (ICRA) - The New Approach to Make Reliable Decisions

The Iterative Compromise Ranking Analysis (ICRA) - The New A...

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Special Sessions in the Advances in information systems and Technologies Track, AIST 2022 and 17th Conference on information systems Management, ISM 2022 held as part of the Federated Conference on computer science and information systems, FedCSIS 2022

作者： Paradowski, Bartosz Kizielewicz, Bartlomiej Shekhovtsov, Andrii Salabun, Wojciech Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin Poland National Institute of Telecommunications Szachowa 1 Warsaw04-894 Poland

ISBN: (纸本)9783031295690

In the field of multi-criteria decision-making, compromise is often sought because it is highly desirable for decision-making. However, over the years, many methods have been developed for decision-making, between which discrepancies in the final rankings are often present. For this reason, it is worth noting the possibility of a compromise between different multi-criteria decision-making methods. One such solution is the Iterative Compromise Ranking Analysis (ICRA), which, by means of an iterative evaluation of the preferences of alternatives, leads to a compromise between the methods under consideration. This work presents an example of a solution to a theoretical decision problem, for which five methods were used: TOPSIS, VIKOR, MARCOS, MABAC and EDAS. In addition, an empirical analysis of the compromise solution was carried out to check the effect of parameters on the number of iterations needed to reach a compromise and the differences between the rankings proposed by the methods and the compromise ranking. The work showed that this is an interesting tool that can find its use in the field of multi-criteria decision-making as well as can be used to analyze the behaviour of multi-criteria decision-making methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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A Sustainable Approach for Determining Compromise Ranking Based on Intuitonistic Fuzzy Score Functions

A Sustainable Approach for Determining Compromise Ranking B...

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作者： Paradowski, Bartosz Kizielewicz, Bartlomiej Więckowski, Jakub Salabun, Wojciech Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin Poland National Institute of Telecommunications Szachowa 1 Warsaw04-894 Poland

ISBN: (纸本)9783031295690

Many real-world decision-making problems require some degree of uncertainty to be taken into account. For purpose of representing such problems, intuitionistic fuzzy sets are used, however, most well-known multi-criteria decision-making methods operate in a crisp environment. In this paper, we present an assessment of score functions that are used to convert fuzzy numbers into crisp ones. Five score functions were selected to assess their usefulness and effectiveness. Those functions were used to transform the theoretical fuzzy decision matrix problems into a crisp environment for evaluating alternatives using the Measurement Alternatives and Ranking according to COmpromise Solution (MARCOS) method. In addition, the compromise between score functions is presented and compared with the other results. The research showed that score functions are useful tools when dealing with problems in an uncertain environment and might prove helpful for decision-makers. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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Early Diagnosis of Carotid Artery Disease based on non-imaging data

Early Diagnosis of Carotid Artery Disease based on non-imagi...

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2023 IEEE EMBS Special Topic Conference on Data science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Kigka, Vassiliki I. Sakellarios, Antonis I. Tsakanikas, Vassilis D. Potsika, Vassiliki T. Koncar, Igor Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Department of Materials Science and Engineering Ioannina Greece Clinic Center of Serbia Department of Vascular and Endovascular Surgery Belgrade11000 Serbia University Campus of Ioannina Institute of Molecular Biology and Biotechnology-FORTH Department of Biomedical Research Ioannina45110 Greece

ISBN: (纸本)9798350383386

Carotid artery disease refers to the build up of atherosclerotic plaques into the carotid arteries. This pathological condition is responsible for cerebral events and stroke. The diagnosis of the disease is based on imaging techniques (carotid ultrasound, computed tomography angiography and magnetic resonance angiography) and the disease is either symptomatic or asymptomatic. The aim of the presented study is to early identify individuals of high risk for carotid artery disease through utilizing data driven techniques. More specifically, we utilize a dataset of 135 individuals with carotid artery disease and healthy ones, as it is defined with the use of carotid ultrasound. The dataset includes both typical medical records (demographics, risk factors) and biomarkers (biochemical, hematological) and is trained into a gradient boosting, aiming to detect the healthy participants and the individuals of high risk. The pipeline includes the pre-processing of the dataset, the handling of the class imbalance, the implementation of feature ranking techniques and finally the evaluation of the proposed pipeline. The model achieved an accuracy of 0.82 and a sensitivity and specificity of 0.68 and 0.92, respectively. © 2023 IEEE.

关键词： Pipelines

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Optimization of Grayscale Image Security through the use of Hybrid Cryptographic Techniques based on Sine and Logistics Maps 4

Optimization of Grayscale Image Security through the use of ...

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4th International Conference on Innovative Research in Applied science, Engineering and technology, IRASET 2024

作者： Koulouh, Fatima Safae, Amine Es-Sabry, Mohammed Mostafa, Merras Elakkad, Nabile Sidi Mohamed Ben Abdellah University Department Of Computer Science Morocco Abdelmalek Essaadi University Laboratory Of Information Security Intelligent Systems And Applications Tetouan Morocco High School Of Technology Moulay Ismail University Image Laboratory Morocco Sidi Mohamed Ben Abdellah University Laboratory Of Engineering Systems And Applications Morocco

ISBN: (纸本)9798350309508

This paper presents an innovative method for enhancing security improve the security of grayscale images by integrating hybrid cryptographic techniques, specifically taking advantage of the sine map and a dual application of the logistic map. The synergy of these chaotic systems reinforces the encryption process, the sinusoidal card generating pseudo-random sequences essential for the generation of cryptographic keys, and the double logistic card adding complexity and unpredictability. Hybridization exploits their complementary characteristics, contributing to the overall robustness of the encryptions system. The theoretical foundations of the sinusoidal map and the double logistic map are examined, highlighting their suitability for cryptographic applications. A comparative examination with established techniques showcases the effectiveness of the proposed method, both in terms of security and computational efficiency, experimental results showing resilience against various attacks, providing information for practical integration into image security frameworks. © 2024 IEEE.

关键词： Chaotic systems

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