International Journal of Critical Infrastructure Protection最新文献

Assessing earthquake risks to lifeline infrastructure systems in the United States

IF 4.1 3区工程技术

International Journal of Critical Infrastructure Protection Pub Date : 2025-03-24 DOI: 10.1016/j.ijcip.2025.100758

N. Simon Kwong , Kishor S. Jaiswal

{"title":"Assessing earthquake risks to lifeline infrastructure systems in the United States","authors":"N. Simon Kwong , Kishor S. Jaiswal","doi":"10.1016/j.ijcip.2025.100758","DOIUrl":"10.1016/j.ijcip.2025.100758","url":null,"abstract":"<div><div>The security and economic stability of the United States rely heavily on robust lifeline infrastructure systems and yet the risks to such systems are seldom quantified at the national scale. For example, while earthquake risks to buildings in the United States have been investigated at the national scale regularly, such risks to gas pipelines have rarely been investigated nationally. In this paper, we use examples from two critical infrastructure sectors to demonstrate (1) the nature of earthquake risks to lifeline infrastructure systems, (2) complexities involved in regional seismic risk assessments, and (3) how such risks change with time. We found that bridge risks can be underestimated by at least 64 % when viewed from repair costs instead of traffic demands and that regional risks can be underestimated by 19 % when spatial correlations of ground motion are ignored. Further, exceedance of traffic demand can be 50 times more likely to occur when viewed at the regional scale than when viewed at an individual bridge. Similarly, exceedance of repairs can be 180 times more likely to occur when viewed at the pipeline network level than at a segment-specific level. Finally, sensitivity analyses with the 2018 and 2023 USGS National Seismic Hazard Models indicate an increase in bridge risk of at least 24 % and an increase in exposed gas pipeline mileage of 43 %. The evolution of risks, complexities involved in assessments, and limited resources jointly underscore the need for more routine updates to nationwide seismic risk assessments of lifeline systems in the United States.</div></div>","PeriodicalId":49057,"journal":{"name":"International Journal of Critical Infrastructure Protection","volume":"49 ","pages":"Article 100758"},"PeriodicalIF":4.1,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Situation Awareness for Cyber Resilience: A review

IF 4.1 3区工程技术

International Journal of Critical Infrastructure Protection Pub Date : 2025-03-04 DOI: 10.1016/j.ijcip.2025.100755

Emanuele Bellini , Giuseppe D’Aniello , Francesco Flammini , Rosario Gaeta

引用次数: 0

Interdependencies and third parties

IF 4.1 3区工程技术

International Journal of Critical Infrastructure Protection Pub Date : 2025-03-01 DOI: 10.1016/S1874-5482(25)00011-3

Roberto Setola

引用次数: 0

Optimizing vehicle security: A multiclassification framework using deep transfer learning and metaheuristic-based genetic algorithm optimization

IF 4.1 3区工程技术

International Journal of Critical Infrastructure Protection Pub Date : 2025-02-24 DOI: 10.1016/j.ijcip.2025.100745

Hamad Naeem , Farhan Ullah , Ondrej Krejcar , Deguang Li , Danish Vasan

{"title":"Optimizing vehicle security: A multiclassification framework using deep transfer learning and metaheuristic-based genetic algorithm optimization","authors":"Hamad Naeem , Farhan Ullah , Ondrej Krejcar , Deguang Li , Danish Vasan","doi":"10.1016/j.ijcip.2025.100745","DOIUrl":"10.1016/j.ijcip.2025.100745","url":null,"abstract":"<div><div>An extension of the Internet of Things (IoT) paradigm, the Internet of Vehicles (IoV) makes it easier for smart cars to connect to the Internet and communicate with one another. Consumer interest in IoV technology has grown significantly as a result of the increased capabilities of smart vehicles. However, the rapid growth of IoV raises serious privacy and security issues that can lead to dangerous accidents. To detect intrusions into IoT networks, several academics have developed deep learning-based algorithms. Detecting malicious assaults inside vehicle networks and lowering the frequency of smart vehicle accidents are the goals of these models. The proposed approach makes use of an advanced three-layer design that combines ensemble approaches, Genetic Algorithms (GA), and Convolutional Neural Networks (CNNs). Three essential steps are used to execute this methodology: In order to perform CNN-based analysis, we first convert high-level IoV data into image format. The hyperparameters of each base learning model are then optimized via GA, which improves the performance and adaptability of the models. Lastly, we combine the outputs of the three CNN models using ensemble approaches, which greatly improves the intrusion detection system’s (IDS) long-term robustness. Two data sets were used for the evaluations: the CICEVSE dataset, which contains 22,086 samples from 12 distinct intrusion categories, and the publicly accessible Car Hacking dataset, which contains 29,228 samples from five different intrusion categories. According to the experimental findings, the proposed strategy obtained an optimal score of 100% on the Car Hacking images and 93% on the CICEVSE images, demonstrating excellent accuracy. The findings have substantial implications for the development of safe, effective, and flexible intrusion detection systems in the complicated environment of the Internet of Vehicles.</div></div>","PeriodicalId":49057,"journal":{"name":"International Journal of Critical Infrastructure Protection","volume":"49 ","pages":"Article 100745"},"PeriodicalIF":4.1,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advances in UAV detection: integrating multi-sensor systems and AI for enhanced accuracy and efficiency

IF 4.1 3区工程技术

International Journal of Critical Infrastructure Protection Pub Date : 2025-02-11 DOI: 10.1016/j.ijcip.2025.100744

Vladislav Semenyuk , Ildar Kurmashev , Alberto Lupidi , Dmitriy Alyoshin , Liliya Kurmasheva , Alessandro Cantelli-Forti

引用次数: 0

Semi-asynchronous federated learning-based privacy-preserving intrusion detection for advanced metering infrastructure

IF 4.1 3区工程技术

International Journal of Critical Infrastructure Protection Pub Date : 2025-02-06 DOI: 10.1016/j.ijcip.2025.100742

Zhuoqun Xia , Hongmei Zhou , Zhenzhen Hu , Qisheng Jiang , Kaixin Zhou

{"title":"Semi-asynchronous federated learning-based privacy-preserving intrusion detection for advanced metering infrastructure","authors":"Zhuoqun Xia , Hongmei Zhou , Zhenzhen Hu , Qisheng Jiang , Kaixin Zhou","doi":"10.1016/j.ijcip.2025.100742","DOIUrl":"10.1016/j.ijcip.2025.100742","url":null,"abstract":"<div><div>The emergence of smart grid brings great convenience to users and power companies, but also brings many new problems, among which the most prominent one is network attack security. Although federated learning works well in dealing with smart grid network attacks, it suffers from gradient leakage, client node failure and a single type of training model. Therefore, this paper proposes a semi-asynchronous federated learning-based privacy-preserving intrusion detection for advanced metering infrastructure (AMI). First, we design a hierarchical federated learning framework based on chained secure multiparty computing, which allows concentrators to collaboratively train models to protect local gradients. Second, we adapt the framework to the AMI network structure characteristics, and design a semi-asynchronous model distribution protocol. Finally, we build an ensemble model based on temporal convolutional network and gated recurrent unit (TCN-GRU) to detect AMI network attacks. The experimental results show that the proposed method can achieve 99.23% accuracy than existing methods.</div></div>","PeriodicalId":49057,"journal":{"name":"International Journal of Critical Infrastructure Protection","volume":"49 ","pages":"Article 100742"},"PeriodicalIF":4.1,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling flood propagation and cascading failures in interdependent transportation and stormwater networks

IF 4.1 3区工程技术

International Journal of Critical Infrastructure Protection Pub Date : 2025-01-16 DOI: 10.1016/j.ijcip.2025.100741

H M Imran Kays, Arif Mohaimin Sadri, K.K. "Muralee" Muraleetharan, P. Scott Harvey, Gerald A. Miller

{"title":"Modeling flood propagation and cascading failures in interdependent transportation and stormwater networks","authors":"H M Imran Kays, Arif Mohaimin Sadri, K.K. \"Muralee\" Muraleetharan, P. Scott Harvey, Gerald A. Miller","doi":"10.1016/j.ijcip.2025.100741","DOIUrl":"10.1016/j.ijcip.2025.100741","url":null,"abstract":"<div><div>This study addresses the challenge of modeling flood propagation and cascading failures in geographically interdependent transportation and stormwater systems, filling a critical gap in the literature by effectively capturing the temporal progression and spatial distribution of failures in interdependent systems. We developed a contagion-based Susceptible-Exposed-Flooded-Recovered (SEFR) model to monitor flood propagation dynamics within these interconnected systems. We established a spatial interdependency threshold for transportation and stormwater systems using a multilayer network representation and incorporated the state-of-the-art Hydrologic Engineering Center's River Analysis System (HEC-RAS) to generate reliable flood data. The SEFR model combines the topological characteristics of the multilayer network with simulated flood data to accurately model the propagation of flood damage and cascading failures. Focusing on Norman, Oklahoma, we calibrated the SEFR model using the HEC-RAS 2D flood simulation data for a major precipitation event on July 27, 2021. Results demonstrate the SEFR model's ability to identify the spatiotemporal variations in flood propagation, highlighting critical infrastructure components at risk, including specific road segments and stormwater system elements vulnerable to cascading failures during flooding events. The findings provide new insights into interdependent system resilience and inform intervention strategies to mitigate adverse flooding impacts, enhancing the robustness of critical infrastructure against natural disasters.</div></div>","PeriodicalId":49057,"journal":{"name":"International Journal of Critical Infrastructure Protection","volume":"48 ","pages":"Article 100741"},"PeriodicalIF":4.1,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

OptAML: Optimized adversarial machine learning on water treatment and distribution systems

IF 4.1 3区工程技术

International Journal of Critical Infrastructure Protection Pub Date : 2025-01-13 DOI: 10.1016/j.ijcip.2025.100740

Mustafa Sinasi Ayas , Enis Kara , Selen Ayas , Ali Kivanc Sahin

引用次数: 0

Artificial immunity-based energy theft detection for advanced metering infrastructures

IF 4.1 3区工程技术

International Journal of Critical Infrastructure Protection Pub Date : 2025-01-09 DOI: 10.1016/j.ijcip.2025.100739

Jie Fu , Chengxi Yang , Yuxuan Liu , Kunsan Zhang , Jiaqi Li , Beibei Li

{"title":"Artificial immunity-based energy theft detection for advanced metering infrastructures","authors":"Jie Fu , Chengxi Yang , Yuxuan Liu , Kunsan Zhang , Jiaqi Li , Beibei Li","doi":"10.1016/j.ijcip.2025.100739","DOIUrl":"10.1016/j.ijcip.2025.100739","url":null,"abstract":"<div><div>Advanced Metering Infrastructure (AMI) is envisioned to enable smart energy management and consumption while ensuring the integrity of real energy consumption data. However, existing smart meters, gateways, and communication channels are usually weakly protected, often opening a huge door for data eavesdroppers who may be easily to further construct energy thefts. Although some energy theft detection schemes have already been reported in the literature, they often fail to take into account the dense data distribution characteristics of energy consumption data, resulting in compromised detection performance. To this end, we in this paper propose a novel ar<strong>T</strong>ificial <strong>IM</strong>mune based <strong>E</strong>nergy theft <strong>D</strong>etection (TIMED) scheme, which can effectively identify five types of energy thefts. Specifically, we first develop an energy consumption data pre-processing method, which can effectively reduce the dimensionality of raw energy consumption data to facilitate the data analyzing efficiency. Second, we design a center-distance-based energy theft detector generation method to create high-quality detectors with low elimination rates. Last, we devise a nonself-based hole repair method for energy theft detectors, which can further reduce the false negative alarms. Extensive experiments on a real public AMI dataset demonstrate that the proposed TIMED scheme is highly effective in identifying pulse attacks, scaling attacks, ramping attacks, random attacks, and smooth-curve attacks. The results show that TIMED outperforms many existing machine learning and traditional artificial immunity-based energy theft detection methods.</div></div>","PeriodicalId":49057,"journal":{"name":"International Journal of Critical Infrastructure Protection","volume":"48 ","pages":"Article 100739"},"PeriodicalIF":4.1,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An efficient convolutional neural network based attack detection for smart grid in 5G-IOT

IF 4.1 3区工程技术

International Journal of Critical Infrastructure Protection Pub Date : 2025-01-03 DOI: 10.1016/j.ijcip.2024.100738

Sheeja Rani S , Mostafa F. Shaaban , Abdelfatah Ali

{"title":"An efficient convolutional neural network based attack detection for smart grid in 5G-IOT","authors":"Sheeja Rani S , Mostafa F. Shaaban , Abdelfatah Ali","doi":"10.1016/j.ijcip.2024.100738","DOIUrl":"10.1016/j.ijcip.2024.100738","url":null,"abstract":"<div><div>The deployment of 5G networks and IoT devices in smart grid applications provides electricity-generated, distributed, and managed bidirectional transmission of real-time information between utility providers and consumers. However, this increased transmission and confidence in IoT devices also present novel security challenges, since they are vulnerable to malicious attacks. Ensuring robust attack detection mechanisms in 5G-IoT smart grid systems for reliable and efficient power distribution, and early accurate identification of attacks addressed. To solve these concerns, a novel technique called Target Projection Regressed Gradient Convolutional Neural Network (TPRGCNN) is introduced to improve the accuracy of attack detection during data transmission in a 5G-IoT smart grid environment. The TPRGCNN method is combined with feature selection and classification for improving secure data transmission by detecting attacks in 5G-IoT smart grid networks. In the feature selection process, TPRGCNN utilizes the Ruzicka coefficient Dichotonic projection regression method and aims to enhance the accuracy of attack detection while minimizing time complexity. Then selected significant features are fed into Jaspen’s correlative stochastic gradient convolutional neural learning classifier for attack detection. Classification indicates whether transmission is normal or an attack in the 5G-IoT smart grid network. The implementation results demonstrate that the proposed TPRGCNN method achieve a 5% of improved attack detection accuracy and 2% improvement in precision, recall, F-score while reducing time complexity and space complexity by 13% and 23% compared to conventional methods.</div></div>","PeriodicalId":49057,"journal":{"name":"International Journal of Critical Infrastructure Protection","volume":"48 ","pages":"Article 100738"},"PeriodicalIF":4.1,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0