Pub Date : 2025-12-19DOI: 10.1016/j.eij.2025.100861
Wenyu Zhang , Yajing Li , Jiaxuan Hu , Ning Wang
With the continuous increase in vehicle ownership, the frequency of traffic accidents has risen significantly, and higher demands have consequently been placed on active vehicle safety technologies. To address the challenges of insufficient real-time performance and high model complexity in traditional object detection methods under complex traffic conditions, an improved front-vehicle collision warning system has been proposed by integrating YOLOv8 and DeepSort. In this approach, the original YOLOv8 backbone network is replaced by the lightweight MobileNet V4, and the Convolutional Block Attention Module (CBAM) is incorporated to enhance feature extraction capabilities. A comprehensive algorithmic framework has been constructed, integrating multi-object recognition, front-vehicle distance estimation, ego-vehicle speed calculation, and hierarchical warning level output. Experimental results on the KITTI dataset have demonstrated a detection accuracy of 95.5 % and a total detection time of 2.6 ms per frame. Additionally, a 2.6 % improvement in mAP50–95 has been observed, accompanied by only a 0.1 % decrease in the recall rate. These findings suggest that the proposed method provides effective technical support for front-vehicle collision warning in intelligent transportation environments.
随着机动车保有量的不断增加，交通事故的发生频率显著上升，对车辆主动安全技术提出了更高的要求。针对传统目标检测方法在复杂交通条件下实时性不足、模型复杂度高的问题，将YOLOv8与DeepSort相结合，提出了一种改进的前车碰撞预警系统。在这种方法中，原始的YOLOv8骨干网络被轻量级的MobileNet V4取代，并加入卷积块注意模块（CBAM）来增强特征提取能力。构建了集多目标识别、前车距离估计、自车速度计算、预警等级输出于一体的综合算法框架。在KITTI数据集上的实验结果表明，检测准确率为95.5%，总检测时间为2.6 ms /帧。此外，观察到mAP50-95有2.6%的改善，同时召回率仅下降0.1%。研究结果表明，该方法为智能交通环境下的前车碰撞预警提供了有效的技术支持。
{"title":"A study on front vehicle collision warning method based on lightweight YOLOv8 and DeepSort","authors":"Wenyu Zhang , Yajing Li , Jiaxuan Hu , Ning Wang","doi":"10.1016/j.eij.2025.100861","DOIUrl":"10.1016/j.eij.2025.100861","url":null,"abstract":"<div><div>With the continuous increase in vehicle ownership, the frequency of traffic accidents has risen significantly, and higher demands have consequently been placed on active vehicle safety technologies. To address the challenges of insufficient real-time performance and high model complexity in traditional object detection methods under complex traffic conditions, an improved front-vehicle collision warning system has been proposed by integrating YOLOv8 and DeepSort. In this approach, the original YOLOv8 backbone network is replaced by the lightweight MobileNet V4, and the Convolutional Block Attention Module (CBAM) is incorporated to enhance feature extraction capabilities. A comprehensive algorithmic framework has been constructed, integrating multi-object recognition, front-vehicle distance estimation, ego-vehicle speed calculation, and hierarchical warning level output. Experimental results on the KITTI dataset have demonstrated a detection accuracy of 95.5 % and a total detection time of 2.6 ms per frame. Additionally, a 2.6 % improvement in mAP50–95 has been observed, accompanied by only a 0.1 % decrease in the recall rate. These findings suggest that the proposed method provides effective technical support for front-vehicle collision warning in intelligent transportation environments.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100861"},"PeriodicalIF":4.3,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-19DOI: 10.1016/j.jnca.2025.104416
Zubaida Rehman , Iqbal Gondal , Hai Dong , Mengmeng Ge , Mark A. Gregory , Ikram ul Haq
Eclipse attacks, which isolate victim nodes by monopolizing their peer connections, remain a critical threat to Ethereum’s consensus mechanism. To address this, we present a principled framework for detecting Eclipse attacks in Ethereum peer-to-peer networks, grounded in a formal adversarial model. Existing defenses are either ad-hoc or lack provable guarantees, leaving open questions about their reliability under adaptive adversaries. Our work aims to bridge this gap by formally defining eclipse attack detection as a security property. We specify soundness, completeness, and robustness theorems under bounded adversarial drift, and derive formal guarantees within false positive and false negative bounds, resilience to adversarial manipulation, and multi-node compositional reliability. We then instantiate a lightweight detection framework that maps packet-level traffic features to predictions using ensemble classifiers (Random Forest, XGBoost). The system was validated using a controlled Ethereum testbed and extended with CTGAN-generated synthetic traces to emulate networks of up to 100 nodes. Empirical evaluation shows that our framework achieves up to 96% F1-score with sub-second inference latency, well within Ethereum’s 12-second Proof-of-Stake validator time slots. These findings demonstrate that lightweight statistical features, when coupled with formal analysis, enable accurate, efficient, and scalable detection of network-level partitioning attacks. Our work establishes a deployable and theoretically grounded defense foundation for securing modern blockchain systems against eclipse adversaries.
{"title":"A robust eclipse attack detection framework for Ethereum networks","authors":"Zubaida Rehman , Iqbal Gondal , Hai Dong , Mengmeng Ge , Mark A. Gregory , Ikram ul Haq","doi":"10.1016/j.jnca.2025.104416","DOIUrl":"10.1016/j.jnca.2025.104416","url":null,"abstract":"<div><div>Eclipse attacks, which isolate victim nodes by monopolizing their peer connections, remain a critical threat to Ethereum’s consensus mechanism. To address this, we present a principled framework for detecting Eclipse attacks in Ethereum peer-to-peer networks, grounded in a formal adversarial model. Existing defenses are either ad-hoc or lack provable guarantees, leaving open questions about their reliability under adaptive adversaries. Our work aims to bridge this gap by formally defining eclipse attack detection as a security property. We specify soundness, completeness, and robustness theorems under bounded adversarial drift, and derive formal guarantees within false positive and false negative bounds, resilience to adversarial manipulation, and multi-node compositional reliability. We then instantiate a lightweight detection framework that maps packet-level traffic features to predictions using ensemble classifiers (Random Forest, XGBoost). The system was validated using a controlled Ethereum testbed and extended with CTGAN-generated synthetic traces to emulate networks of up to 100 nodes. Empirical evaluation shows that our framework achieves up to 96% F1-score with sub-second inference latency, well within Ethereum’s 12-second Proof-of-Stake validator time slots. These findings demonstrate that lightweight statistical features, when coupled with formal analysis, enable accurate, efficient, and scalable detection of network-level partitioning attacks. Our work establishes a deployable and theoretically grounded defense foundation for securing modern blockchain systems against eclipse adversaries.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"246 ","pages":"Article 104416"},"PeriodicalIF":8.0,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-19DOI: 10.1016/j.automatica.2025.112795
Alexander Yu. Pogromsky, Alexey S. Matveev
{"title":"Remote robust state estimation for nonlinear systems","authors":"Alexander Yu. Pogromsky, Alexey S. Matveev","doi":"10.1016/j.automatica.2025.112795","DOIUrl":"https://doi.org/10.1016/j.automatica.2025.112795","url":null,"abstract":"","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"38 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As artificial intelligence and machine learning advance, increasing privacy concerns and regulatory constraints have limited cross-border data sharing for traditional model training. Federated Learning (FL) offers a privacy-preserving approach by enabling distributed training without exposing raw data. However, FL faces significant challenges, particularly when dealing with Non-Independent and Identically Distributed (Non-IID) data, which results in inconsistent model performance. Moreover, relying on a central server also raises reliability and scalability issues. Decentralized Federated Learning (DFL) eliminates the central server, thereby fostering more robust and scalable collaboration. Despite the growing interest in DFL, a comprehensive review focusing on Non-IID challenges remains scarce. This article presents a Systematic Literature Review (SLR) of existing research on DFL under Non-IID settings. Studies were retrieved from six major academic publishers and categorized into four pillars: architecture, topology, optimization, and security. The SLR review provides insights into current trends and systematically summarizes real-world applications, commonly used datasets, and neural network models. This article also examines prevalent methods for conducting Non-IID experiments and evaluating performance metrics. By providing a structured analysis of the literature, experimental setups, and evaluation practices, this survey highlights key trends, uncovers research gaps, and proposes future directions for advancing DFL in Non-IID environments.
{"title":"Decentralized Federated Learning with Non-IID Data: Challenges, Trends, and Future Opportunities","authors":"Wu-Chun Chung, Chao-Ai Lo, Yan-Hui Lin, Zhi-Hao Chen, Che-Lun Hung","doi":"10.1145/3785657","DOIUrl":"https://doi.org/10.1145/3785657","url":null,"abstract":"As artificial intelligence and machine learning advance, increasing privacy concerns and regulatory constraints have limited cross-border data sharing for traditional model training. Federated Learning (FL) offers a privacy-preserving approach by enabling distributed training without exposing raw data. However, FL faces significant challenges, particularly when dealing with Non-Independent and Identically Distributed (Non-IID) data, which results in inconsistent model performance. Moreover, relying on a central server also raises reliability and scalability issues. Decentralized Federated Learning (DFL) eliminates the central server, thereby fostering more robust and scalable collaboration. Despite the growing interest in DFL, a comprehensive review focusing on Non-IID challenges remains scarce. This article presents a Systematic Literature Review (SLR) of existing research on DFL under Non-IID settings. Studies were retrieved from six major academic publishers and categorized into four pillars: architecture, topology, optimization, and security. The SLR review provides insights into current trends and systematically summarizes real-world applications, commonly used datasets, and neural network models. This article also examines prevalent methods for conducting Non-IID experiments and evaluating performance metrics. By providing a structured analysis of the literature, experimental setups, and evaluation practices, this survey highlights key trends, uncovers research gaps, and proposes future directions for advancing DFL in Non-IID environments.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"56 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-19DOI: 10.1016/j.cosrev.2025.100869
Stefan Kratsch, Marcin Pilipczuk, Roohani Sharma, Magnus Wahlström
{"title":"Applications of flow-augmentation","authors":"Stefan Kratsch, Marcin Pilipczuk, Roohani Sharma, Magnus Wahlström","doi":"10.1016/j.cosrev.2025.100869","DOIUrl":"https://doi.org/10.1016/j.cosrev.2025.100869","url":null,"abstract":"","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"2 1","pages":""},"PeriodicalIF":12.9,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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