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35th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2024

作者： Huang, Bing Lam, Kwok-Yan Nanyang Technological University School of Computing and Data Science Singapore

ISBN: (纸本)9798350367041

Recent advances in IoT technologies have led to the rise of home automation (HA) systems, greatly improving users' daily life quality. With the growing use of smart devices in homes, event sensors play a key role by reporting physical events like doors opening or lights turning on, automatically or manually. However, these sensor reports can be unreliable due to faults or active attackers, leading to false events (event spoofing). This is critical, as smart homes can trigger event chains without user involvement. Therefore, verifying the authenticity of physical events is important in securing smart home automation systems. Despite increasing attention from academia and industry, no survey has yet summarized HA systems' security research on physical event verification. This paper provides a comprehensive review of physical event verification and future research directions. © 2024 IEEE.

关键词： Smart homes

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Answering reachability queries with ordered label constraints over labeled graphs

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Frontiers of Computer Science 2024年第1期18卷 105-117页

作者： Daoliang HE Pingpeng YUAN Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science&TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Reachability query plays a vital role in many graph analysis *** researches proposed many methods to efficiently answer reachability queries between vertex *** many real graphs are labeled graph,it highly demands Label-Constrained Reachability(LCR)query inwhich constraint includes a set of labels besides vertex *** researches proposed several methods for answering some LCR queries which require appearance of some labels specified in constraints in the *** that constraint may be a label set,query constraint may be ordered labels,namely OLCR(Ordered-Label-Constrained Reachability)queries which retrieve paths matching a sequence of ***,no solutions are available for ***,we propose DHL,a novel bloom filter based indexing technique for answering OLCR *** can be used to check reachability between vertex *** the answers are not no,then constrained DFS is ***,we employ DHL followed by performing constrained DFS to answer OLCR *** show that DHL has a bounded false positive rate,and it's powerful in saving indexing time and *** experiments on 10 real-life graphs and 12 synthetic graphs demonstrate that DHL achieves about 4.8-22.5 times smaller index space and 4.6-114 times less index construction time than two state-of-art techniques for LCR queries,while achieving comparable query response *** results also show that our algorithm can answer OLCR queries effectively.

关键词： graph ordered-label-constrained reachability partial index bloom filter query processing

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Fuzzy knowledge based intelligent decision support system for ground based air defence

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Journal of Ambient Intelligence and Humanized computing 2024年第4期15卷 2317-2340页

作者： Ahmad, Adnan Amjad, Rawan Basharat, Amna Farhan, Asma Ahmad Abbas, Ali Ezad Department of Computer Science School of Computing National University of Computer and Emerging Sciences Islamabad Pakistan Department of Data Science School of Computing National University of Computer and Emerging Sciences Lahore Pakistan Islamabad Pakistan

This research proposes an Intelligent Decision Support System for Ground-Based Air Defense (GBAD) environments, which consist of Defended Assets (DA) on the ground that require protection from enemy aerial threats. A Fire Control Officer is responsible for assessing threats and assigning the most appropriate weapon to neutralize them. However, the decision-making process can be prone to errors, risking resource wastage and endangering DA protection. To address this problem, this research proposes a hybrid approach that combines a knowledge-driven fuzzy inference system with machine learning models to optimize resource allocation while incorporating expert knowledge in the decision-making process. Since sensory data obtained from multiple radars may be incomplete or incorrect, a fuzzy knowledge graph-based system is used for data fusion and providing it to the connected modules. Feature selection is optimized by including the most important parameters, such as the vitality of defended assets and threat score, in the threat evaluation. The results from these subsystems are visualized using a Geographical Information System, allowing for real-time mapping of the GBAD environment and displaying the results in a user-friendly web interface. The proposed system has undergone rigorous testing and evaluation, resulting in an efficient and accurate weapon assignment model with a low RMSE value of 0.037. Overall, this Intelligent Decision Support System provides an effective solution for optimizing decision-making processes in GBAD environments and can significantly improve DA protection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Graphic methods

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Privacy Protection for Blockchain-Based Healthcare IoT Systems: A Survey

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1757-1776页

作者： Minfeng Qi Ziyuan Wang Qing-Long Han Jun Zhang Shiping Chen Yang Xiang School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthorn3122VictoriaAustralia Faculty of Data Science City University of MacaoMacaoChina CSIRO Data61 SydneyNew South WalesAustralia

To enable precision medicine and remote patient monitoring,internet of healthcare things(IoHT)has gained significant interest as a promising *** the widespread use of IoHT,nonetheless,privacy infringements such as IoHT data leakage have raised serious public *** the other side,blockchain and distributed ledger technologies have demonstrated great potential for enhancing trustworthiness and privacy protection for IoHT *** this survey,a holistic review of existing blockchain-based IoHT systems is conducted to indicate the feasibility of combining blockchain and IoHT in privacy *** addition,various types of privacy challenges in IoHT are identified by examining general data protection regulation(GDPR).More importantly,an associated study of cutting-edge privacy-preserving techniques for the identified IoHT privacy challenges is ***,several challenges in four promising research areas for blockchain-based IoHT systems are pointed out,with the intent of motivating researchers working in these fields to develop possible solutions.

关键词： Blockchain internet of healthcare things(IoHT) privacy-preserving techniques(PPTs).

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Delay-CJ:A novel cryptojacking covert attack method based on delayed strategy and its detection

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Digital Communications and Networks 2023年第5期9卷 1169-1179页

作者： Guangquan Xu Wenyu Dong Jun Xing Wenqing Lei Jian Liu Lixiao Gong Meiqi Feng Xi Zheng Shaoying Liu Big Data School Qingdao Huanghai UniversityQingdao266427China College of Intelligence and Computing Tianjin UniversityTianjin300350China School of Computing Macquarie UniversityNSW2109Australia Graduate School of Advanced Science and Engineering Hiroshima UniversityJapan

Cryptojacking is a type of resource embezzlement attack,wherein an attacker secretly executes the cryptocurrency mining program in the target host to gain *** has been common since 2017,and in fact,it once became the greatest threat to network *** better prove the attack ability the harm caused by cryptojacking,this paper proposes a new covert browser-based mining attack model named Delay-CJ,this model was deployed in a simulation environment for *** on the general framework of cryptojacking,Delay-CJ adds hybrid evasion detection techniques and applies the delayed execution strategy specifically for video websites in the prototype *** results show that the existing detection methods used for testing may become invalid as result of this *** view of this situation,to achieve a more general and robust detection scheme,we built a cryptojacking detection system named CJDetector,which is based on cryptojacking process ***,it identifies malicious mining by monitoring CPU usage and analyzing the function call *** system not only effectively detects the attack in our example but also has universal *** recognition accuracy of CJDetector reaches 99.33%.Finally,we tested the web pages in Alexa 50K websites to investigate cryptojacking activity in the real *** found that although cryptojacking is indeed on the decline,it remains a part of network security threats that cannot be ignored.

关键词： Cryptojacking Evasion techniques Delayed strategy Cryptocurrency mining Behavior-based detection

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Enhancing the Efficiency of Systems with Overlapping Coalition Formation 21st

Enhancing the Efficiency of Systems with Overlapping Coal...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Valizadeh, Jaber Zhang, Dongmo Mubin, Omar School of Computing Data and Mathematical Sciences Western Sydney University Sydney Australia

ISBN: (纸本)9789819601240

We studied the decision-making process of autonomous agents within the Electric Vehicle (EV) charging domain, where EV charging suppliers, acting as autonomous agents, interact to maximize their utilities in a shared environment. Initially, we modeled the suppliers’ utilities in an EV charging scenario as a non-cooperative game aimed at achieving Pareto-optimal outcomes. We then expanded this model to include two cooperative game approaches: Non-Overlapping Coalition Formation and Overlapping Coalition Formation. Our results indicate that allowing suppliers to form partial coalitions through the overlapping coalition formation model significantly enhances outcomes compared to both the non-overlapping coalition formation and non-cooperative models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Multi agent systems

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Deep Convolutional Neural Network for Enhancing Traffic Sign Recognition Developed on Yolo V5 2

Deep Convolutional Neural Network for Enhancing Traffic Sign...

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2nd International Conference on Advancements in Smart, Secure and Intelligent computing, ASSIC 2024

作者： Liu, Aofan Liu, Yutong Kifah, Saif School of Computing and Data Science Xiamen University Malaysia Sepang Malaysia

ISBN: (纸本)9798350370188

In today's era, deep learning neural networks with multiple hidden layers have been widely used in many fields. The deep learning method has more powerful features that enhance the method's performance by a learning process. With the development of the logistics industry and the prevalence of autonomous driving, traffic sign recognition has gained rising attention. This study uses a YOLO CNN to classify traffic signs. To improve model performance, we used MSRCR image augmentation during preprocessing. In the improvement phase, we used YOLOv5 to automate traffic sign categorization and improved training methods and network architecture. GTSRB and CCTSDB were used to assess the proposed technique. The experimental results show that the YOLOv5 model outperforms other methods. It has a 99.8% accuracy rate in the GTSRB dataset and 98.4% precision in the CCTSDB. © 2024 IEEE.

关键词： Object recognition

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Enhancing AI Security: A Review of Blockchain-Integrated Solutions 4

Enhancing AI Security: A Review of Blockchain-Integrated Sol...

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4th International Conference on Computer Science and Blockchain, CCSB 2024

作者： Wei, Cai School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia

ISBN: (纸本)9798350356502

Artificial Intelligence (AI) technology is advancing rapidly, driving innovation across various industries while also presenting significant security challenges. These challenges include protecting model ownership, ensuring data privacy, and defending against malicious attacks. Blockchain technology, recognized for its decentralized, transparent, and tamper-proof nature, offers a promising solution to address these issues. This review article explores three key research questions: How can blockchain technology protect AI model ownership? In what ways does blockchain safeguard data privacy within AI systems? What other security-related concerns arise with blockchain-driven AI? Through exploring these questions, the article addresses discussions on ownership, data privacy, attacks, scalability, quantum threats, and ethical security challenges, Through a comprehensive analysis of the existing literature, this article identifies the benefits and limitations of combining blockchain with AI, highlights technical and practical challenges, and proposes directions for future research to enhance the security, effectiveness, and ethical governance of blockchain-integrated AI systems. © 2024 IEEE.

关键词： Federated learning

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Soft Real-time Emotion-aware System through Human Physiological Signals using Pervasive Devices 13

Soft Real-time Emotion-aware System through Human Physiologi...

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13th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2024

作者： Setiawan, Feri Khowaja, Sunder Ali Bina Nusantara University School of Computing and Creative Arts Jakarta Indonesia Technological University Dublin School of Computing Faculty of Computing Digital and Data Dublin Ireland

ISBN: (纸本)9798350368086

The concept of connected things that involves emotional aspects has been raised as a new research issue known as 'emotional IoT'. One of the challenges is to recognize the emotion in real-time without interfering the user's task or activity. However, most of the existing works on emotion recognition deal with high sampling rate devices and thus need complex arrangement. In this study, we present a framework to recognize four emotions based on human physiological signals using pervasive devices with low sampling rate. The employment of low sampling rate devices may degrade the classification accuracy due to the data offsets or calibration errors. Our study proposes a set of energy deviation features to overcome the accuracy degradation caused by low sampling rate devices. The recursive feature elimination method is applied to avoid overfitting by selecting the most important features and reduce the computational overhead in real-world implementation. Experimental results show that the proposed approach achieves 92.01% and 91.75% accuracy in terms of valence and arousal, respectively. The realization of the proposed approach is done in an end-to-end soft real-time emotion recognition system using smartphone and smartwatch devices. The performance of our system was evaluated under laboratory setup, resulting in 46.3 milliseconds of response time on the average for recognizing emotions. © 2024 IEEE.

关键词： Emotion Recognition

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Plant Disease Identification under Imbalanced dataset using Hybrid Deep Learning Method

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Journal of The Institution of Engineers (India): Series A 2025年第1期106卷 19-29页

作者： Zhou, Changjian Zhang, Xin Department of Data and Computing Northeast Agricultural University Harbin150030 China School of Electrical and Information Northeast Agricultural University Harbin150030 China

Recent studies suggest that plant disease identification via computational approaches is vital for agricultural production. However, there is still a large gap that needs to be bridged while the training data is imbalanced when the number of samples in different categories of the dataset varies greatly. To solve this limitation, a hybrid deep learning method combining deep residual network, dense network, and deep convolution generative adversarial network (DCGAN) is proposed for plant disease identification in this work, which takes advantage of these three models. Including 34,501 original images with 33 categories are collected, where one category contains 4442 samples and another contains 74, causing the phenomenon of data imbalance. Importantly, the imbalanced dataset has a negative impact on training performance. To address this issue, the DCGAN is introduced for data augmentation to make up for the limit of training data in this research. In addition, the residual and dense network are combined as a novel deep learning method to improve prediction ability. Together, the original and generated images are integrated as a mixed dataset for training, and only the original images were utilized for testing. Experimental results indicated that the presented approach achieved 0.977 F1-score and 0.987 test accuracy, outperformed the existing state-of-the-art models. These findings indicate that the hybrid deep learning approach, through the ingenious integration of the strengths of three sub-networks, significantly enhances the generalization capability of the model. This methodology not only optimizes overall performance but also underscores the profound potential in tackling these complex problems. Furthermore, a smartphone-based point-to-point identification system was designed to provide convenience for users in practical application. © The Institution of Engineers (India) 2024.

关键词： Generative adversarial networks

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