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Special Issue: Cybersecurity in Manufacturing

Journal of Computing and Information science in engineering 2024年第7期24卷 070201页

作者： Ameta, Gaurav Bukkapatnam, Satish Li, Dan Tian, Wenmeng Yampolskiy, Mark Zhang, Fan Siemens Corporate Research PrincetonNJ08540 United States Department of Industrial and Systems Engineering Texas A&M University College StationTX77843 United States Department of Industrial Engineering Clemson University ClemsonSC29634 United States Department of Industrial and Systems Engineering Mississippi State University Mississippi StateMS39762 United States Department of Computer Science and Software Engineering Auburn University AuburnAL36849 United States School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30318 United States

The landscape of cybersecurity in manufacturing exhibits a dynamic interplay between evolving threats and vulnerabilities against innovative defense mechanisms. With the increasing adoption of smart and cloud-controlled technologies, there is a growing emphasis on securing manufacturing systems from cyberattacks. Future trends indicate a shift toward implementing more advanced technologies such as artificial intelligence and machine learning for threat identification, attack detection, and response. In addition, the adoption of secure-by-design principles in product development and the integration of blockchain technology for ensuring the integrity of supply chain data are expected to become more prevalent. As manufacturers continue to digitize and connect their operations, collaboration between industry stakeholders, government agencies, and cybersecurity experts will be crucial in developing robust defense strategies against evolving security threats. This Special Issue provided a platform for the research advancing understanding of and addressing these threats. The article by Rahman et al., “Taxonomy-Driven Graph-Theoretic Framework for Manufacturing Cybersecurity Risk Modeling and Assessment,” presents a taxonomy-driven graph-theoretic model and framework to represent the threat landscape of cybersecurity and identify vulnerable manufacturing assets. The model and framework contain three techniques to address identified research challenges. First, the proposed framework characterizes threat actors' techniques, tactics, and procedures using taxonomical classifications of manufacturing-specific threat attributes and integrates these attributes into cybersecurity risk modeling. Second, using the attack graph formalism, the proposed framework enables concurrent modeling and analysis of a wide variety of cybersecurity threats comprising varying attack vectors, locations, vulnerabilities, and consequences. Third, a quantitative risk assessment approach is presented to

关键词： computer security Manufacturing

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DeNC: Unleash Neural Codecs in Video Streaming with Diffusion Enhancement 39

DeNC: Unleash Neural Codecs in Video Streaming with Diffusio...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhou, Qihua Li, Ruibin Guo, Jingcai Huang, Yaodong Xu, Zhenda Cui, Laizhong Guo, Song College of Computer Science and Software Engineering Shenzhen University China Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)157735897X

Recent years have witnessed the rise of Neural-enhanced Video Streaming (NeVS), which integrates neural restoration models into video codecs for higher compression-restoration performance. Despite its benefit, existing work has not well explored the full potential of NeVS paradigm, due to: (1) post-streaming restoration by decoder while lacking the proactive collaboration of encoder, (2) end-to-end optimization based on conventional rate-distortion theory, which has been verified that low distortion is not always a synonym for high perceptual quality, and (3) coupled design for domain-specific tasks that cannot generalize to various video codecs. Observing these limitations, our objective is not to incrementally present an improved restoration model. Instead, we focus on the encoder-decoder synergy, i.e., the codec, which is non-trivial since it inherently strikes the rate-distortion-perception trade-off of NeVS. Aiming at this target, we propose the Diffusion-enhanced Neural Codec (DeNC), a plug- and-play module for current NeVS paradigm, to significantly reduce the required bitrates while preserving high perceptual quality of restored videos. Our key design is twofold. First, DeNC improves the encoder’s compression efficiency by simultaneously reducing the resolution and color bit-depth of frame referencing. Second, DeNC empowers the decoder with perception-oriented restoration capability by making its diffusion-based restoration process aware of the encoder’s compression conditions. Real-world evaluations show that DeNC improves compression ratios with nearly an order of magnitude and achieves much higher restoration quality (e.g., 93+ VMAF and 23% higher MOS) over the latest baselines. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Video streaming

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Digital Forensics Across Multiple Android Versions Using the MOBILEdit Forensic Tool

Digital Forensics Across Multiple Android Versions Using the...

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IEEE International Conference on Multi Topic

作者： Kohal Deep Maheshwari Pardeep Kumar Najma Imtiaz Ali Imtiaz Ali Brohi Dua Agha Department of Computer Science & IT NED University Karachi Pakistan Department of Software Engineering QUEST Univesity Nawabshah Pakistan Institute of Mathematics and Computer Science University of Sindh Jamshoro Pakistan Department of Computer Science Government College University Hyderabad Pakistan Department of Software Engineering Mehran University of Engineering and Technology Jamshoro Pakistan

ISBN: (数字)9798331507213

ISBN: (纸本)9798331507220

Open-source Android smartphones present both opportunities and difficulties for forensic investigators as they become more widely used. Advancements in Android security, encryption, and storage features necessitate newer forensic methods. The performance of MOBILedit Forensic across several Android versions will be covered in this article, with a concentrate on those running the Android 4.x version series. Data extraction, file recovery from deletion, and examination of call logs, messages, multimedia, and social media activity were among the main forensic activities carried out using this program. The tool's performance was evaluated on data from locked or corrupted devices, including its ability to decrypt information. The findings indicate that while MOBILedit Forensic is compatible with many Android versions, the speed and effectiveness of data extraction depend on the particular security measures used on each version. Newer Android models with advanced encryption and secure boot processes require more complex recovery techniques compared to older models, which recover data more quickly due to fewer security mechanisms. MOBILedit Forensic is still helpful in mobile forensics despite its flaws, but only partially understanding of encrypted data. This study also provides a more thorough understanding of the tool's functionality across different Android versions, which can help forensic investigators make better decisions about the best evidence extraction methods. Regular updates to forensic tools are crucial to adapt to evolving Android security measures.

关键词： Social networking (online) Operating systems Forensics Digital forensics Feature extraction Data models Encryption Data mining Smart phones Secure storage

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Virtual and Augmented Reality for Mechatronics based Applications 4

Virtual and Augmented Reality for Mechatronics based Applica...

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4th International Conference on Applied Automation and Industrial Diagnostics, ICAAID 2022

作者： Alharbi, Hadeel Fahad Reddy, Nikunj Patel, Akshet Tsaramirsis, Kostandinos Randhawa, Princy University of ha'Il Hail College of Computer Science and Engineering Department of Computer Science and Software Engineering 1234 Saudi Arabia Manipal University Jaipur Mechatronics Department Jaipur India Infosuccess3d Athens Greece

ISBN: (纸本)9781728189390

Virtual reality (VR) is a rapidly growing medium of technology in its initial stages. Innumerable applications of its use are yet to be explored or properly implemented. One such forum is in the field of education. At the state of this pandemic implementation of virtual reality could prove to be a critical solution for students facing a creative gap of understanding or overcome the difficulties faced while maintaining an engaging environment over two dimensional screens. People of all ages loves to experiment and work with new technology and are more engrossed with it. VR thereby provides the users with a new medium to interact and investigate global phenomena or scenic environments with a completely different perspective within the safe confines of a classroom. This paper highlights the various applications and implementation of this technology along with the accompanying gear. The ease with which simple applications could be created for the purpose of VR/AR display over Unity-3D was practiced. Overall, the paper highlights how Augmented and Virtual reality can reinvent the field of education with the ingenuity it provides. © 2022 IEEE.

关键词： Augmented reality

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Influence of Key Nodes in Industrial Control Malicious Code Modeling 3

Influence of Key Nodes in Industrial Control Malicious Code ...

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3rd International Conference on Electronic Information engineering and computer science, EIECS 2023

作者： Yang, Wei Yan, Hongwei Xu, Chao Yao, Yu Zhou, Jinlei Wang, Baile Wang, Danni Northeastem University Software College Shenyang China Northeastem University School of Computer Science and Engineering Shenyang China State Grid Liaoning Information and Communication Company Department of Cybersecurity Monitoring Shenyang China

ISBN: (纸本)9798350316674

Key nodes in complex networks play an important role in the structure of the entire network. Different nodes in the network often have different degrees, and the node with the highest degree in the network is usually called a key node. However, the heterogeneity of nodes is mostly neglected in the research of malware propagation models by scholars at home and abroad. Therefore, based on the SUIQMR model proposed in the industrial control coupling network and considering the role of key nodes in the network, an improved malware suppression strategy for key nodes in the immune network is obtained. The simulation results verify the effectiveness of the improved malware suppression strategy, showing that the immune key nodes can effectively suppress the spread of malware. © 2023 IEEE.

关键词： industrial control network key nodes Malware Propagation Model Suppression strategy

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Attention-guided CenterNet deep learning approach for lung cancer detection

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computers in Biology and Medicine 2025年 186卷 109613-109613页

作者： Dawood, Hussain Nawaz, Marriam Ilyas, Muhammad U. Nazir, Tahira Javed, Ali School of Computing Skyline University College Sharjah United Arab Emirates Department of Software Engineering University of Engineering and Technology-Taxila Punjab 47050 Pakistan School of Computer Science University of Birmingham Dubai United Arab Emirates Department of Software Engineering and Computer Science Riphah International University Gulberg Green Campus Islamabad Pakistan

Lung cancer remains a significant health concern worldwide, prompting ongoing research efforts to enhance early detection and diagnosis. Prior studies have identified key challenges in existing approaches, including limitations in feature extraction, interpretability, and computational efficiency. In response, this study introduces a novel deep learning (DL) framework, termed the Improved CenterNet approach, tailored specifically for lung cancer detection. The primary importance of this work lies in its innovative integration of ResNet-34 with an attention mechanism within the CenterNet architecture, addressing critical limitations identified in previous studies. By augmenting the base network with an attention mechanism, our framework offers improved feature extraction capabilities, enabling the model to learn relevant patterns associated with lung cancer amidst complex backgrounds and varying environmental conditions. This enhancement facilitates more accurate and interpretable predictions while reducing computational complexity and inference times. Through extensive experimental evaluations conducted on standard datasets, our proposed approach demonstrates promising results, highlighting its potential to advance the field of lung cancer detection and diagnosis. Specifically, we have acquired the precision, recall, and F1-Score of 99.89 %, 99.82 %, and 99.85 % on the LUNA-16 dataset, and 98.33 %, 98.02 %, and 98.17 % for the Kaggle data sample, respectively which is showing the efficacy of our approach. One limitation of the work is that it cannot effectively locate the samples with intense light variations. Therefore, future research work is focused on overcoming this challenge. © 2024 Elsevier Ltd

关键词： Lung cancer

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Efficient Models for Detecting Monkeypox using Skin Lesion Images 3

Efficient Models for Detecting Monkeypox using Skin Lesion I...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Sunda, Neelam Sharma, Divya Battu, Sujith Banoth, Rewanth Nayak Rahul Monika Kanoria PG Mahila Mahavidyalaya Department of Computer Science Jaipur India Kanoria PG Mahila Mahavidhyalaya Department of Computer Science jaipur India Delhi Technological University Dept. of Software Engineering New Delhi India Delhi Technological University Department of Applied Physics New Delhi India University of Delhi Shaheed Rajguru College of Applied Sciences for Women Department of Computer Science India

ISBN: (纸本)9798350343632

Monkeypox is predicted to be the next pandemic and is spreading over African countries like water under a mat. The study basically investigates three deep learning imaging models for the identification of monkeypox from skin lesion photos from the MSID dataset. The motto is to define a computer-based technique to identify monkeypox from just its image, making it cheaper and faster with accurate results. The study achieved the highest accuracy of 99.37% as compared to previous works related to Resnet 50. The limits and complexity of the procedures in real-world situations are also clearly addressed. © 2023 IEEE.

关键词： Convolutional Neural Network Image preprocessing Monkeypox Performance Evaluation Metrics Transfer learning

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A Survey on Anomaly Detection with Few-Shot Learning 8th

A Survey on Anomaly Detection with Few-Shot Learning

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8th International Conference on Cognitive Computing, ICCC 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Chen, Junyang Wang, Changbo Hong, Yifan Mi, Rui Zhang, Liang-Jie Wu, Yirui Wang, Huan Zhou, Yue College of Computer Science and Software Engineering Shenzhen University Shenzhen China Center of Health Administration and Development Studies Hubei University of Medicine Shiyan China College of Informatics Huazhong Agricultural University Wuhan China Hohai University Nanjing China Department of Obstetrics and Gynecology Tianyou Hospital Affiliated to Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783031779534

The primary objective of anomaly detection is to identify abnormal or unusual patterns within a dataset, where the number of normal samples typically exceeds that of abnormal samples. Due to the scarcity of labeled abnormal samples, traditional methods face challenges when dealing with anomaly detection. To overcome these limitations, few-shot learning has emerged as a promising solution. By leveraging a limited number of labeled anomaly samples, few-shot learning enables the construction of models that enhance anomaly detection performance and generalization. This paper provides a comprehensive investigation of anomaly detection, covering its definition, fundamental principles, methods, and challenges. Furthermore, it introduces few-shot learning as a solution and explores its principles, applications, and technical categorization, including meta-learning, transfer learning, generative models, prototypical learning, and siamese networks. The paper explores the utilization of few-shot learning in anomaly detection across diverse data types This paper delves into significance across different domains. Additionally, it addresses the challenges faced by few-shot learning in the field of anomaly detection and proposes future directions for development. This comprehensive analysis aims to provide profound insights and guidance for prospective research and application in anomaly detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

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Development of Classification Model based on Arabic Textual Analysis to Detect Fake News: Case Studies

Development of Classification Model based on Arabic Textual ...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Hanen Tarik Himdi Fatmah Yousef Assiri Computer Science and Artificial Intelligence Department College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Software Engineering Department College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

The ease of communication that has been made possible by chat messaging platforms, such as WhatsApp and Line, and their increased use and ubiquity in society, have motivated purveyors of fake news to create and present their news as legitimate. Though different organisations continued their efforts to resolve this problem, many of the developed solutions rely on verifying the associated metadata, which are not always available. Several studies have attempted to identify fake news by analysing textual content; however, there is a lack of studies on Arabic language sources. Our work filled this gap; we proposed a machine learning model that classifies Arabic fake news based on textual analysis. This paper shows the feasibility of our approach to classify Arabic text in social media applications.

关键词： Analytical models Technological innovation Freeware Social networking (online) Smart cities Machine learning Metadata

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Detection and Replay of Distributed Denial of Service Attacks in Smart Cities Using a Hybrid Deep Learning Approach 2

Detection and Replay of Distributed Denial of Service Attack...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Iqbal, Muhammad Waseem Issa, Ghassan F. Yousif, Muhammad Atif, Muhammad Superior University Department of Software Engineering Lahore Pakistan Skyline University College School of Information Technology Sharjah United Arab Emirates Department of Computer Science NCBAE Lahore Pakistan Applied Science Research Center Applied Science Private University Amman11937 Jordan

ISBN: (纸本)9798350335644

Infrastructure for smart cities is presently being built, mostly because of the (IoT) platform, which connects a wide variety of things by using the website's underlying infrastructure. As a result, it is possible to employ a uniform platform to automate the services that are delivered. Smart city infrastructure, on the other hand, is susceptible to cyberattacks owing to security flaws in IoT networks. Distributing denial of service (DDoS) and replay assaults, for example, violate the requirements for certification in smart cities. The lack of citations in this part backs up many of the statements it makes. Who needs a citation? Both assaults have the potential to substantially damage smart city infrastructure, which might also cost money and even result in human fatalities. This paper covers the creation of a blended deep- learning algorithm for replay and DDoS intrusion prevention on a growth-oriented smart city platform. This is made possible by combining proven machine-learning approaches with more recent advances in artificial intelligence. We simulate scattered denial of service and replay attacks on three datasets acquired from real-world smart cities to assess the efficacy of the proposed hybrid strategy on environmental, smart river, and smart soil dataset. The suggested model has shown excellent rates of accuracy. The ecosystem collection has an accuracy level of 98.37%, the smart riverbed dataset must have an accuracy of 98.13%, and the smart mud dataset must have an accuracy of 99.51%. The findings demonstrated that the proposed model outperformed past instances of evolutionary computation and machine learning techniques employed in the corpus of academic literature. © 2023 IEEE.

关键词： Internet of things

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