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Fortifying Smart Grids: A Holistic Assessment Strategy against cyber Attacks and Physical Threats for Intelligent Electronic Devices

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computers, Materials & Continua 2024年第8期80卷 2579-2609页

作者： Yangrong Chen June Li Yu Xia Ruiwen Zhang Lingling Li Xiaoyu Li Lin Ge School of Computer Science Zhengzhou University of AeronauticsZhengzhou450046China Henan Xin’an Century Technology Co. Ltd.Henan Data Security Engineering Technology Research CenterZhengzhou450046China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of EducationSchool of Cyber Science and EngineeringWuhan UniversityWuhan430072China

Intelligent electronic devices(IEDs)are interconnected via communication networks and play pivotal roles in transmitting grid-related operational data and executing control *** the context of the heightened security challenges within smart grids,IEDs pose significant risks due to inherent hardware and software vulner-abilities,as well as the openness and vulnerability of communication *** grid security,distinct from traditional internet security,mainly relies on monitoring network security events at the platform layer,lacking an effective assessment mechanism for ***,we incorporate considerations for both cyber-attacks and physical faults,presenting security assessment indicators and methods specifically tailored for ***,we outline the security monitoring technology for IEDs,considering the necessary data sources for their security ***,we classify IEDs and establish a comprehensive security monitoring index system,incorporating factors such as running states,network traffic,and abnormal *** index system contains 18 indicators in 3 ***,we elucidate quantitative methods for various indicators and propose a hybrid security assessment method known as GRCW-hybrid,combining grey relational analysis(GRA),analytic hierarchy process(AHP),and entropy weight method(EWM).According to the proposed assessment method,the security risk level of IEDs can be graded into 6 levels,namely 0,1,2,3,4,and *** higher the level,the greater the security ***,we assess and simulate 15 scenarios in 3 categories,which are based on monitoring indicators and real-world situations encountered by *** results show that calculated security risk level based on the proposed assessment method are consistent with actual ***,the reasonableness and effectiveness of the proposed index system and assessment method are validated.

关键词： Smart grid intelligent electronic device security assessment abnormal behaviors network traffic running states

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Credit Card Fraud Detection Using ML Techniques 1

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International Conference on Recent Developments in cyber security, ReDCySec 2023

作者： Bonkoungou, Samiratou Roy, Nihar Ranjan Ako, Nomel Haymes Axel-Elie Junior Mishra, Alpna Department of Computer Science and Engineering Sharda University Noida India Center of Cyber Security and Cryptology Sharda University Noida India

ISBN: (数字)9789819998111

ISBN: (纸本)9789819998104

With the increasing use of Internet, online banking transactions have also increased. Everyday more and more users are being added to the cyberspace, which has also attracted cybercriminals. These scammers use stolen credit card information for making online purchases. The revolution of online business or commerce has given an increased cybersurface for financial fraud targeting digital cards. By analyzing the cardholder’s transaction activity, using machine learning techniques, we can easily identify the fraudulent activity or transaction. In case of any abnormal behavior, the transaction is termed as fraudulent, and numerous techniques have been proposed by the researchers to prevent these, among these techniques machine learning-based techniques are the most popular. In this paper, we have surveyed the solution with respect to ML-based techniques, conducted experiments, and concluded our findings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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Blockchain-Assisted Fine-Grained Deduplication and Integrity Auditing for Outsourced Large-Scale Data in Cloud Storage

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IEEE Internet of Things Journal 2025年第12期12卷 21662-21678页

作者： Liu, Bingyun Zhang, Xiaojun Yang, Xingchun Zhang, Yuan Xue, Jingting Zhou, Rang Southwest Petroleum University School of Computer Science and Software Engineering Engineering Research Center for Intelligent Oil and Gas Exploration Chengdu610500 China Sichuan Police College Intelligent Policing Key Laboratory of SichuanProvince Luzhou646000 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu610054 China Chengdu University of Technology School of Computer Science and Cyber Security Chengdu610059 China

Cloud computing has emerged as a promising mode for storaging vast quantities of big data, which is vulnerable to potential security threats, making it urgent to ensure data confidentiality and integrity auditing. In addition, as a large number of duplicate data files exist in cloud storage, data deduplication is significant to improve storage efficiency. In this paper, we propose a blockchain-assisted fine-grained deduplication and integrity auditing scheme for outsourced large-scale data in cloud storage, achieving internal deduplication and cross-user external deduplication for ciphertexts and authentication tags. By constructing sparse summation ciphertext tree, the scheme implements proofs of ownership protocol through vector commitment, and guarantees the retrieval of distributed deduplication data blocks by designing the reconstruction matrix and bloom filter. The scheme exploits blockchain and smart contracts to ensure transparent integrity auditing without a third-party auditor (TPA), thereby avoiding malicious auditing biases. security analysis and performance evaluation demonstrate the feasibility of the scheme for deploying in cloud storage systems. © 2014 IEEE.

关键词： Data integrity

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Research on Lightweight Sensing Technology Based on Single-Antenna Multicarrier

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IEEE Internet of Things Journal 2025年第12期12卷 20678-20694页

作者： Jiang, Yu Zhu, Di Wang, Jiadong Hu, Aiqun Southeast University School of Cyber Scienc and Engineering Purple Mountain Laboratories Nanjing210096 China Key Laboratory of Computer Network Technology of Jiangsu Province Nanjing210096 China Southeast University Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Southeast University School of Cyber Science and Engineering Nanjing210096 China Southeast University School of Cyber Science and Engineering School of Information Science and Engineering Nanjing210096 China Southeast University State Key Laboratory of Mobile Communication Nanjing210096 China

Channel State Information (CSI) serves as a critical indicator of wireless signal conditions and is widely regarded by researchers for its sensitivity in detecting changes within the channel. However, traditional sensing technologies often require substantial data and intricate learning algorithms, highlighting an urgent need for advancements in lightweight sensing technologies. These technologies should leverage simpler terminal devices, reduced data volumes, and more straightforward classification algorithms to achieve sensing capability that are comparable to those offered by more complex and established methods. This paper concentrates on the lightweight application of wireless sensing and encompasses the following key contributions: 1) The development of a lightweight sensing model utilizing a single-antenna multicarrier system, which introduces a CSI ratio model that adapts multi-antenna techniques for single-antenna settings;2) The enhancement of feature stability through the introduction of a complex-plane fitting method using artificial vector, alongside a dual receiver based method for cross-scene feature generation aimed at producing stable and high-quality auxiliary features. Experimental results show that the feature extraction capability of the single-antenna multicarrier CSI ratio model is close to that of traditional multi-antenna scheme. On the gait dataset, when the enhanced CSI ratio is used as a feature, the accuracy is nearly 100%, surpassing the 93% accuracy of the original amplitude feature. On the gesture dataset, the combination of the enhanced CSI ratio and PEI features achieves an accuracy of 96%, which is superior to using the original CSI amplitude feature alone. An analysis of resource consumption shows that the lightweight SVM model incurs very low computational overhead during decision-making, validating the potential of this scheme in terms of efficiency and practical application. © 2014 IEEE.

关键词： Communication channels (information theory)

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An intelligent hybrid MAC protocol for a sensor-based personalized healthcare system

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Digital Communications and Networks 2022年第2期8卷 174-185页

作者： Linbo Deng Jinsong Gui Tian Wang Jiawei Tan Xiong Li School of Computer Science and Engineering Central South UniversityChangSha410083China College of Computer Science and Technology Huaqiao UniversityXiamen361021China Institute for Cyber Security School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China

Sensors based on personalized healthcare systems have been widely used in the medical ***,energy limitations have greatly hindered the further development of medical *** the traditional Medium Access Control(MAC)protocol,the duration of low-power listening is fixed because it ignores that the available energy of sensors is different in some situations,which leads to a high delay and low energy *** this paper,a Maximum Listening Length MAC(MLL-MAC)protocol is proposed to fully utilize the energy in the sensor-based *** MLL-MAC protocol is an improvement of the Receiver-Initiated(RI)MAC *** main advance is that the sensor node performs the following additional operations:(1)The sender sends a beacon when it wakes up and sends data,thus establishing a communication link with the receiver in the listening state;(2)The receiver keeps listening as long as possible to reduce the delay when it wakes up and listens to the channel,which is different from the previous strategy in which the node turns into a sleep state immediately without receiving ***,the sensor node can dynamically determine whether to send beacons and prolong listening duration according to its available energy *** MLL-MAC protocol is evaluated through theoretical analysis and experimental *** results show that,compared with the RI-MAC protocol,the MLL-MAC protocol can reduce the average end-to-end delay by 41.4%and improve the energy utilization by 15.1%.

关键词： Sensor based system Delay Energy consumption Network lifetime Adaptive listening length

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Robust steganographic approach using generative adversarial network and compressive autoencoder

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Multimedia Tools and Applications 2024年 1-38页

作者： Qasaimeh, Malik Qtaish, Alaa Abu Aljawarneh, Shadi Department of Computer Information Systems Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan Department of Software Engineering co-joint with the Department of Cyber Security Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

Nowadays, social media applications and websites have become a crucial part of people’s lives;for sharing their moments, contacting their families and friends, or even for their jobs. However, the fact that these valuable data are transferred via the Internet and open channels, which are vulnerable to attacks and espionage, requires using defense methods to improve the security of data transmission. Recently, the progress of Deep Learning (DL) inspired researchers to use it with security methods such as steganography, the art of hiding secret data in unrelated content. DL and steganography combined enhanced the hiding properties, especially in the Coverless Image Steganography (CIS) methods. In this paper, we propose a Compressive Coverless Image Steganography (CCIS) model, which is a generated-based CIS, to improve the capacity and robustness of Steganography. This model uses the compressive autoencoder to compress the message, thus increasing the capacity, and uses the Generative Adversarial Network (GAN) to generate a stego image from the compressed vector, thus enhancing concealment ability, besides using a regression model and optimization method to extract the data. Another version was designed to enable sending binary messages by replacing the compressive autoencoder network with a robust mapping rule. Experiments show that the proposed model could improve the characteristics of Steganography. Furthermore, the proposed model could extract binary messages with 256 bits from attacked stego images with 99% recovery accuracy. Thus, capacity and robustness were enhanced using our model with both images and text as secret messages. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Detecting anomalies in blockchain transactions using machine learning classifiers and explainability analysis

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Blockchain(Research and Applications) 2024年第3期5卷 106-122页

作者： Mohammad Hasan Mohammad Shahriar Rahman Helge Janicke Iqbal H.Sarker Department of Computer Science and Engineering Premier University4000 ChitagongBangladesh Department of Computer Science and Engineering United International University1212 DhakaBangladesh Cyber Security Cooperative Research Centre 6027 PerthAustralia Centre for Securing Digital Futures Edith Cowan University6027 PerthAustralia

As the use of blockchain for digital payments continues to rise,it becomes susceptible to various malicious *** detecting anomalies within blockchain transactions is essential for bolstering trust in digital ***,the task of anomaly detection in blockchain transaction data is challenging due to the infrequent occurrence of illicit *** several studies have been conducted in the field,a limitation persists:the lack of explanations for the model’s *** study seeks to overcome this limitation by integrating explainable artificial intelligence(XAI)techniques and anomaly rules into tree-based ensemble classifiers for detecting anomalous Bitcoin *** shapley additive explanation(SHAP)method is employed to measure the contribution of each feature,and it is compatible with ensemble ***,we present rules for interpreting whether a Bitcoin transaction is anomalous or ***,we introduce an under-sampling algorithm named XGBCLUS,designed to balance anomalous and non-anomalous transaction *** algorithm is compared against other commonly used under-sampling and over-sampling ***,the outcomes of various tree-based single classifiers are compared with those of stacking and voting ensemble *** experimental results demonstrate that:(i)XGBCLUS enhances true positive rate(TPR)and receiver operating characteristic-area under curve(ROC-AUC)scores compared to state-of-the-art under-sampling and over-sampling techniques,and(ii)our proposed ensemble classifiers outperform traditional single tree-based machine learning classifiers in terms of accuracy,TPR,and false positive rate(FPR)scores.

关键词： Anomaly detection Blockchain Bitcoin transactions Data imbalance Data sampling Explainable AI Machine learning Decision tree Anomaly rules

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ZoomViT: an observation behavior-based fine-grained recognition scheme

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Neural Computing and Applications 2024年第21期36卷 12775-12789页

作者： Ma, Zhipeng Yang, Yongquan Wang, Haicheng Huang, Lei Wei, Zhiqiang Faculty of Information Science and Engineering Ocean University of China Songling Road Shandong Qingdao266000 China College of Computer and Cyber Security Fujian Normal University Xuefu South Road Fuzhou350117 China

Fine-grained image recognition aims to distinguish many images with subtle differences and identify the sub-categories to which they belong. Recently, vision transformer (ViT) has achieved promising results in many computer vision tasks. In this paper, we introduce human observation behavior into ViT and propose a novel transformer-based network, named ZoomViT. We divide the fine-grained recognition into two steps "look closer" and "contrast." Firstly, looking closer is to observe finer local regions and multi-scale features, and avoid the adverse effect of background on recognition. We design the zoom-in module to track the attention flow by integrating the attention weights to zoom in the discriminative foreground regions. Subsequently, the straight image splitting like ViT may harm recognition adversely. Therefore, we design the zoom-out module combining overlapping cutting and downsampling to maintain the integrity of local neighboring structures and the running efficiency of the model in recognition. Finally, we propose to contrast the features of known sub-categories to supervise the model to learn subtle differences among different sub-categories. The consistency of features extracted from different batches increases over time;for this reason, we proposed a variable-length queue to store features from different batches to efficiently and fully conduct contrastive learning. We experimentally demonstrate the state-of-the-art performance of our model on four popular fine-grained benchmarks: CUB-200-2011, Stanford Dogs, NABirds, and iNat2017. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image recognition

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Resource investment for DDoS attack resistant SDN: a practical assessment

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science China(Information sciences) 2023年第7期66卷 112-129页

作者： Bin YUAN Fan ZHANG Jun WAN Huan ZHAO Shui YU Deqing ZOU Qiangsheng HUA Hai JIN School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Shenzhen Huazhong University of Science and Technology Research Institute School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab School of Computer Science University of Technology Sydney

Software-defined networks(SDNs) present a novel network architecture that is widely used in various datacenters. However, SDNs also suffer from many types of security threats, among which a distributed denial of service(DDoS) attack, which aims to drain the resources of SDN switches and controllers,is one of the most common. Once the switch or controller is damaged, the network services can be *** defense schemes against DDoS attacks have been proposed from the perspective of attack detection;however, such defense schemes are known to suffer from a time consuming and unpromising accuracy, which could result in an unavailable network service before specific countermeasures are taken. To address this issue through a systematic investigation, we propose an elaborate resource-management mechanism against DDoS attacks in an SDN. Specifically, by considering the SDN topology, we leverage the M/M/c queuing model to measure the resistance of an SDN to DDoS attacks. Network administrators can therefore invest a reasonable number of resources into SDN switches and SDN controllers to defend against DDoS attacks while guaranteeing the quality of service(QoS). Comprehensive analyses and empirical data-based experiments demonstrate the effectiveness of the proposed approach.

关键词： SDN security DDoS attacks resource management queueing theory QoS

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Data Analytic for Healthcare cyber Physical System

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IEEE Transactions on Network science and engineering 2023年第5期10卷 2490-2502页

作者： Gan, Wensheng Hu, Kaixia Huang, Gengsen Chien, Wei-Che Chao, Han-Chieh Meng, Weizhi Jinan University College of Cyber Security Guangzhou510632 China Pazhou Lab Guangzhou510330 China National Dong Hwa University Department of Computer Science and Information Engineering Hualien97401 Taiwan National Dong Hwa University Department of Electrical Engineering Hualien97401 Taiwan UCSI University Institute of Computer Science and Innovation Kuala Lumpur Malaysia Department of Applied Mathematics and Computer Science Cyber Security Section Lyngby2800 Denmark

Nowadays, a large number of AI-powered healthcare cyber-physical systems (CPSs) have been used in healthcare services. In order to provide better care, AI-powered healthcare CPSs analyze the data they collect using a variety of techniques. Data analysis for artificial intelligence (AI)-driven healthcare CPS is one of these approaches. However, none of the techniques in data analysis can provide a good representation of contiguous and negative information. Therefore, we are the first to introduce the problem of contiguous negative sequential pattern mining. A novel algorithm called Contiguous Negative Sequential Pattern Miner (CNSPM) is proposed to discover and analyze contiguous negative sequential patterns (CNSPs) from the data collected by healthcare CPSs. Finally, we select some real medical and non-medical datasets to conduct numerous experiments. We further analyze the discovered patterns and show how healthcare services can use meaningful patterns for medical decision-making. The performance results on these datasets demonstrate that the proposed algorithm can discover more valuable patterns efficiently and effectively from the collected and transformed medical data. © 2013 IEEE.

关键词： Data mining

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