Article
Computer Science, Information Systems
Firooz B. Saghezchi, Georgios Mantas, Manuel A. Violas, A. Manuel de Oliveira Duarte, Jonathan Rodriguez
Summary: This article investigates the problem of DDoS attack detection on Industry 4.0 CPPSs and adopts a machine learning approach. Through analysis of network traffic data from a real-world factory and extensive simulations, the study finds that supervised algorithms perform better in terms of detection performance.
Article
Computer Science, Theory & Methods
Luis Rosa, Tiago Cruz, Miguel Borges de Freitas, Pedro Quiterio, Joao Henriques, Filipe Caldeira, Edmundo Monteiro, Paulo Simoes
Summary: The next-generation of Industrial Automation and Control Systems (IACS) and Supervisory Control and Data Acquisition (SCADA) systems face challenges in cybersecurity monitoring due to the convergence of OT/IT networks and the need for comprehensive analysis enabled by the usage, combination and aggregation of outputs from multiple sources and techniques.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Green & Sustainable Science & Technology
Ana-Maria Mateoiu, Adrian Korodi, Anka Stoianovici, Radu Tira
Summary: The use of mobile devices for monitoring and controlling local processes has rapidly increased in industry. This paper presents an implementation of an easy-to-use SCADA system for the Android operating system, designed to meet industry 4.0 concepts. The system utilizes an OPC UA client-based architecture for interoperability, mobility, and security. It aims to improve visibility and response time for technical issues or faults, and is adaptable to both legacy and modern OPC UA specifications.
Article
Automation & Control Systems
Lianyong Qi, Yihong Yang, Xiaokang Zhou, Wajid Rafique, Jianhua Ma
Summary: This article discusses cyber attacks in the logistics network of Industry 4.0 and the proposed novel anomaly detection approach MDS_AD to address the challenges posed by these attacks. MDS_AD combines multiple techniques and performs well in experimental results.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Hardware & Architecture
Yafeng Wu, Yulai Xie, Xuelong Liao, Pan Zhou, Dan Feng, Lin Wu, Xuan Li, Avani Wildani, Darrell Long
Summary: This article presents Paradise, a real-time, generalized, and distributed provenance-based intrusion detection method. Paradise introduces a novel extract strategy to prune and extract process feature vectors from provenance dependencies at the system log level, and it stores them in high-efficiency memory databases. Paradise can negotiate all detection results from multiple detectors without extra communication overhead between detectors by calculating provenance-based dependencies independently during the detection phase.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2023)
Review
Computer Science, Theory & Methods
Salwa Alem, David Espes, Laurent Nana, Eric Martin, Florent De Lamotte
Summary: This paper addresses the limitations of industrial IDS and proposes a method to reduce the false positive rate and differentiate attacks from industrial failures using Neural Network and Decision Making System. It was tested in a real industrial environment and showed promising results with high accuracy and low false positive rate.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Automation & Control Systems
Amit K. Shukla, Shubham Srivastav, Sandeep Kumar, Pranab K. Muhuri
Summary: In an Industry 4.0 ecosystem, digital interconnectedness and automation integration increase productivity but also bring the risk of cyber-attacks. Efficient threat intelligence techniques or intrusion detection systems (IDSs) are needed to identify and monitor these attacks. This paper proposes a novel unsupervised IDS, UInDeSI4.0, which uses feature selection and isolation forest to detect network threats in an unsupervised manner. Experimental results show that UInDeSI4.0 provides better accuracy and minimal features compared to traditional IDSs.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Automation & Control Systems
Kaixiang Yang, Yifan Shi, Zhiwen Yu, Qinmin Yang, Arun Kumar Sangaiah, Huanqiang Zeng
Summary: With the development of Industry 4.0, industrial Big Data has become essential in the Industrial Internet of Things. Intelligent anomaly detection, which is challenging, is still a crucial issue in industrial cyber-physical systems. This article presents a new alternative solution for network intrusion detection in Industry 4.0 through the development of the one-class broad learning system (OCBLS) and the stacked OCBLS (ST-OCBLS) algorithms.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Industrial
Leonardo Moraes Aguiar Lima Dos Santos, Matheus Becker da Costa, Joao Victor Kothe, Guilherme Brittes Benitez, Jones Luis Schaefer, Ismael Cristofer Baierle, Elpidio Oscar Benitez Nara
Summary: This study identifies key and collaborative technologies related to Industry 4.0, and proposes five collaborative networks with distinct goals. This will help managers improve their focus on priorities regarding the implementation of Industry 4.0 technologies.
JOURNAL OF MANUFACTURING TECHNOLOGY MANAGEMENT
(2021)
Article
Chemistry, Analytical
Radhya Sahal, Saeed H. Alsamhi, John G. Breslin, Muhammad Intizar Ali
Summary: Forestry 4.0, inspired by Industry 4.0, introduces Internet of Forest Things (IoFT) to improve forest management efficiency and sustainability. By utilizing smart devices for data collection and monitoring, it addresses environmental challenges and ensures protection of forests from hazards.
Article
Automation & Control Systems
Motong Sun, Yingxu Lai, Yipeng Wang, Jing Liu, Beifeng Mao, Haoran Gu
Summary: In this study, a rule-based IDS called LU-IDS was proposed, which is able to understand industrial control logic and generate rules for detecting various attacks. Experimental results demonstrate that LU-IDS shows excellent performance in intrusion detection.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Review
Food Science & Technology
Abdo Hassoun, Abderrahmane Ait-Kaddour, Adnan M. Abu-Mahfouz, Nikheel Bhojraj Rathod, Farah Bader, Francisco J. Barba, Alessandra Biancolillo, Janna Cropotova, Charis M. Galanakis, Anet Rezek Jambrak, Jose M. Lorenzo, Ingrid Mage, Fatih Ozogul, Joe Regenstein
Summary: Climate change, population growth, food waste, and the risk of new disease outbreaks pose significant challenges to food sustainability and global security. Industry 4.0 technologies have emerged as a vital driver for sustainable development, transforming the food industry and offering solutions to these challenges. However, groundbreaking sustainable solutions can only be achieved by combining multiple technologies simultaneously.
CRITICAL REVIEWS IN FOOD SCIENCE AND NUTRITION
(2023)
Article
Computer Science, Information Systems
Wu Wang, Fouzi Harrou, Benamar Bouyeddou, Sidi-Mohammed Senouci, Ying Sun
Summary: This study introduces a stacked deep learning method to identify malicious attacks targeting SCADA systems, showing satisfactory detection performance and outperforming traditional algorithms. Additionally, the research uncovers the importance of features in the process of cyber-attacks detection, aiding in the design of more parsimonious models.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Alireza Dehlaghi-Ghadim, Mahshid Helali Moghadam, Ali Balador, Hans Hansson
Summary: Industrial Control Systems (ICS) have become increasingly vulnerable to cyberattacks due to their connection to the internet. Using Machine Learning (ML) for Intrusion Detection Systems (IDS) is a promising approach for ICS cyber protection, but the lack of suitable datasets for evaluating ML algorithms is a challenge. This paper introduces the 'ICS-Flow' dataset, which provides network data and process state variables logs for supervised and unsupervised ML-based IDS assessment. The dataset includes normal and anomalous network packets and flows captured from simulated ICS components and emulated networks, allowing for effective training of intrusion detection ML models.
Article
Computer Science, Artificial Intelligence
Adrien Becue, Isabel Praca, Joao Gama
Summary: This survey paper explores the opportunities and threats of using artificial intelligence (AI) in the manufacturing sector, focusing on intrusion detection techniques and the potential of AI technology in Industry 4.0.
ARTIFICIAL INTELLIGENCE REVIEW
(2021)
Editorial Material
Computer Science, Information Systems
Takeshi Takahashi, Rodrigo Roman Castro, Bilhanan Silverajan, Ryan K. L. Ko, Said Tabet
INTERNATIONAL JOURNAL OF INFORMATION SECURITY
(2020)
Editorial Material
Automation & Control Systems
Cristina Alcaraz, Yan Zhang, Alvaro Cardenas, Liehuang Zhu
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2020)
Article
Automation & Control Systems
Juan E. Rubio, Rodrigo Roman, Javier Lopez
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2020)
Editorial Material
Chemistry, Analytical
Dimitris A. Gritzalis, Grammati Pantziou, Rodrigo Roman-Castro
Editorial Material
Engineering, Civil
Yan Zhang, Celimuge Wu, Rodrigo Roman, Hong Liu
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Computer Science, Hardware & Architecture
Javier Lopez, Juan E. Rubio, Cristina Alcaraz
Summary: This article investigates the use of digital twins in smart grids, explores the role of AI technologies in managing information flows of future applications, and discusses how digital twins can enhance their context awareness and simulation technologies to predict faults and detect cybersecurity issues in real time, updating access control policies accordingly.
IEEE WIRELESS COMMUNICATIONS
(2021)
Article
Computer Science, Information Systems
Simone Fischer-Hubner, Cristina Alcaraz, Afonso Ferreira, Carmen Fernandez-Gago, Javier Lopez, Evangelos Markatos, Lejla Islami, Mahdi Akil
Summary: This article provides an overview and analysis of key cybersecurity issues, challenges, and requirements derived from interviews with 63 European stakeholders in security-critical sectors. Common themes across these sectors include trust-building, privacy and identity management, system resilience, standardization, design security and privacy, and data sharing compliance. The results also suggest cybersecurity trends and offer directions for future research and innovation activities in Europe.
JOURNAL OF INFORMATION SECURITY AND APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Ruben Rios, Jose A. Onieva, Rodrigo Roman, Javier Lopez
Summary: This privacy manager for IoT data, based on edge computing, enforces privacy before data leaves user control, providing a tool for users to express data-sharing preferences based on context-aware privacy language.
IEEE SECURITY & PRIVACY
(2022)
Article
Computer Science, Information Systems
Andrew D. Syrmakesis, Cristina Alcaraz, Nikos D. Hatziargyriou
Summary: This paper provides a classification and analysis of cyber resilience methods against cyber attacks in smart grids, highlighting the need for further research in scientific areas to enhance the cyber resilience of smart grids.
INTERNATIONAL JOURNAL OF INFORMATION SECURITY
(2022)
Article
Computer Science, Information Systems
Antonio Munoz, Ruben Rios, Rodrigo Roman, Javier Lopez
Summary: This paper provides a comprehensive analysis and categorization of existing vulnerabilities in TEEs, highlighting the design flaws that led to them. It also presents effective countermeasures to reduce the likelihood of new attacks and discusses appealing challenges and open issues in this field.
COMPUTERS & SECURITY
(2023)
Article
Computer Science, Information Systems
Cristina Alcaraz, Jesus Cumplido, Alicia Trivino
Summary: Undoubtedly, Industry 4.0 has increased the rate of electric vehicle manufacturing and the installation of grid-connected charging infrastructures. This study analyzes the security risks of the latest version of the open charge point protocol (OCPP-v2.0.1), especially when charging stations are connected to microgrids. The results suggest that despite the evolution of OCPP-v2.0.1, further protection measures are needed to address potential cyber and physical threats.
INTERNATIONAL JOURNAL OF INFORMATION SECURITY
(2023)
Article
Computer Science, Information Systems
Rodrigo Roman, Cristina Alcaraz, Javier Lopez, Kouichi Sakurai
Summary: Digitalization and industrial paradigms are reshaping critical infrastructures and supply chains, presenting opportunities to enhance trust and transparency through the deployment of emerging technologies. However, these advancements also bring vulnerabilities that need to be addressed.
IEEE SECURITY & PRIVACY
(2023)
Article
Computer Science, Information Systems
Cristina Alcaraz, Javier Lopez
Summary: Industry 4.0 is positively impacting the value chain through the use of digital twin technology, but it also brings security threats that need to be studied and addressed. This paper analyzes the current state of the digital twin paradigm, classifies potential threats, and provides security recommendations to ensure its appropriate and trustworthy use.
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS
(2022)
Proceedings Paper
Computer Science, Information Systems
Aristeidis Farao, Juan Enrique Rubio, Cristina Alcaraz, Christoforos Ntantogian, Christos Xenakis, Javier Lopez
CRITICAL INFORMATION INFRASTRUCTURES SECURITY (CRITIS 2019)
(2020)
Article
Computer Science, Information Systems
Kashan Ahmed, Syed Khaldoon Khurshid, Sadaf Hina
Summary: This paper mainly introduces the construction of the cyber threat intelligence knowledge graph and the information extraction technique. By using joint extraction technique, it solves the problem of traditional techniques becoming ineffective due to the increasing size of CTI data. Experimental results show that this technique outperforms state-of-the-art models in knowledge triple extraction on CTI data and improves the F1 score.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Xinlong He, Yang Xu, Sicong Zhang, Weida Xu, Jiale Yan
Summary: This paper proposes a new membership inference attack method in federated learning, which utilizes data poisoning and sequence prediction confidence. The attack is effective and results in minimal overall model performance degradation.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Tieming Chen, Huan Zeng, Mingqi Lv, Tiantian Zhu
Summary: In this paper, the authors propose a deep learning based dynamic malware detection method called CTIMD, which integrates threat knowledge from CTIs into the learning process of API call sequences with runtime parameters. Experimental results show that CTIMD outperforms existing methods in terms of performance.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Wonwoo Choi, Minjae Seo, Seongman Lee, Brent Byunghoon Kang
Summary: This paper proposes SUM, a backward-edge control flow protection scheme for ARM Cortex-M processors. It combines MPU and the overlooked hardware feature FaultMask to achieve efficient and robust protection. The empirical evaluation shows minimal runtime overhead for the proposed solution.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Liliana Ribeiro, Ines Sousa Guedes, Carla Sofia Cardoso
Summary: Phishing susceptibility is influenced by individual and contextual factors. The study found that individuals who perceive themselves as capable of detecting phishing and those who use online services more frequently are more susceptible to phishing. However, technology competencies and other individual variables do not predict phishing susceptibility.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Wenjie Wang, Yuanhai Shao, Yiju Wang
Summary: In this paper, we investigate the adversarial perturbations of twin support vector machines (TWSVMs) and propose an optimization framework, which provides explicit solutions to increase the interpretability of the conclusion and convenience for calculation.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Snofy D. Dunston, V. Mary Anita Rajam
Summary: This paper proposes a novel adversarial attack technique that can synthesize adversarial images to mislead deep learning models, and also studies interpretability plots. The research findings show that the proposed attack technique influences the interpretability plots, regardless of the success of the attack.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Junchen Li, Guang Cheng, Zongyao Chen, Peng Zhao
Summary: Protocol Reverse Engineering (PRE) is a direct approach for analyzing unknown traffic. This paper proposes a method for clustering unknown traffic based on private protocol labels, and the experimental results demonstrate its advantages on real-world network traffic.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Rafal Kozik, Massimo Ficco, Aleksandra Pawlicka, Marek Pawlicki, Francesco Palmieri, Michal Choras
Summary: The inclusion of Explainability of Artificial Intelligence (xAI) has become a mandatory requirement for designing and implementing reliable, interpretable, and ethical AI solutions. However, it has been shown that xAI can enable successful adversarial attacks in the domain of fake news detection, leading to a decrease in AI security. This paper presents an attack scheme that uses an explainable solution to reshape the structure of the original message, allowing the adversary to manipulate the model's prediction while keeping the message's meaning intact.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Benyuan Yang, Lili Luo, Zhimeng Wang
Summary: Interoperation is widely used in practical industrial applications, but merging local access control policies may lead to security violations. Dealing with these issues in a multidomain environment is critical, but finding the maximum secure interoperation among individual systems poses a challenge due to the large number of entities and access involved.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Binghui Zou, Chunjie Cao, Longjuan Wang, Sizheng Fu, Tonghua Qiao, Jingzhang Sun
Summary: The ongoing struggle between security researchers and malware has led to the exploration of using convolutional neural networks and capsule networks for classification and identification of malware. However, training these networks requires a significant amount of data and parameters, and the research on capsule networks is still in its early stages, posing challenges.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Hongsong Chen, Xingyu Li, Wenmao Liu
Summary: Multivariate time-series anomaly detection is crucial for maintaining normal operation of physical equipment. Recent advances have been made in this field, but two challenges have limited the model's ability to generalize. To address these challenges, a multivariate time-series anomaly detection model consisting of a characterization network and a forecasting network is proposed. Experimental results demonstrate that this method outperforms baseline methods in terms of detection performance and robustness.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Roberto Doriguzzi-Corin, Domenico Siracusa
Summary: This paper discusses the application of federated learning in the field of cybersecurity and proposes an adaptive mechanism-based federated learning solution for DDoS attack detection in dynamic cybersecurity scenarios. Through experiments, it is demonstrated that the proposed solution outperforms state-of-the-art federated learning algorithms in terms of convergence time and accuracy.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Antonio Giovanni Schiavone
Summary: The usage of HTTPS protocol is crucial for secure communication with websites, ensuring the confidentiality, integrity, and authenticity of online data transmissions. The Municipality2HTTPS research project analyzed the implementation of HTTPS in Italian municipalities' websites and identified areas for improvement.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Domna Bilika, Nikoletta Michopoulou, Efthimios Alepis, Constantinos Patsakis
Summary: Voice Assistants (VAs) are widely used in smart devices, but are vulnerable to attacks, as shown by experiments with popular VAs revealing successful attack rates exceeding 30% and statistical variations among vendors, calling for additional countermeasures to protect user information.
COMPUTERS & SECURITY
(2024)
