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Edge-based AI solution for enhancing urban safety: helmet compliance monitoring with YOLOv9 on Raspberry Pi

Discover Internet of Things 2025年第1期5卷 1-14页

作者： Tahilramani, Nikunj Ahir, Param Saxena, Shruti Talreja, Vandana P. Charanarur, Panem Adani Institute of Digital Technology Management Gandhinagar India School of Cyber Security and Digital Forensics National Forensic Sciences University Gandhinagar India Electrical Engineering Department R.C. Technical Institute Ahmedabad India Department of Cyber Security and Digital Forensics National Forensic Sciences University Tripura India

This paper presents an automated helmet detection system leveraging Raspberry Pi and advanced deep learning techniques, specifically the YOLOv9 object detection model that focuses on real-time object detection. The system captures real-time video frames of motorcyclists, processes these frames to detect helmet usage, and interfaces with the vehicle’s ignition control unit to inhibit engine start if a helmet is not detected. This edge computing solution addresses the inefficiencies of manual inspections by providing a scalable, cost-effective, and real-time enforcement mechanism for helmet compliance. The YOLOv9 model, trained on a robust dataset, ensures high detection accuracy and low latency, demonstrating effective performance in real-world scenarios. The YOLOv9 model achieved a precision of 0.894, recall of 0.943, mAP50 of 0.967, and an inference speed of 21.0 ms per image. This research highlights significant advancements in intelligent transportation systems and road safety, achieved through integrating convolutional neural networks and embedded hardware platforms. © The Author(s) 2025.

关键词： Convolutional neural networks

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In‑depth Correlation Power Analysis Attacks on a Hardware Implementation of CRYSTALS‑Dilithium

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cybersecurity 2025年第2期8卷 229-241页

作者： Huaxin Wang Yiwen Gao Yuejun Liu Qian Zhang Yongbin Zhou School of Cyber Science and Engineering Nanjing University of Science and TechnologyNanjingChina Institute of Information Engineering Chinese Academy of SciencesBeijingChina School of Cyber Security University of Chinese Academy of SciencesBeijingChina

During the standardisation process of post-quantum cryptography,NIST encourages research on side-channel analysis for candidate *** the recommended lattice signature scheme,CRYSTALS-Dilithium,when implemented on hardware,has seen limited research on side-channel analysis,and current attacks are incomplete or requires a substantial quantity of ***,we conducted a more complete analysis to investigate the leakage of an FPGA implementation of CRYSTALS-Dilithium using the Correlation Power Analysis(CPA)method,where with a minimum of 70,000 traces partial private key coefficients can be ***,we optimise the attack by extracting Point-of-Interests using known information due to parallelism(named CPA-PoI)and by iteratively utilising parallel leakages(named CPA-ITR).Our experimental results show that CPA-PoI reduces the number of traces by up to 16.67%,CPA-ITR by up to 25%,and both increase the number of recovered key coefficients by up to 55.17% and 93.10% using the same number of *** outperfom the CPA *** a result,it suggests that the FPGA implementation of CRYSTALS-Dilithium is more vulnerable than thought before to side-channel analysis.

关键词： CRYSTALS-Dilithium Post-Quantum Cryptography Correlation Power Analysis FPGA Side-Channel Attack

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Enhanced Deep Learning Approaches for Robust Darknet Traffic Classification 3

Enhanced Deep Learning Approaches for Robust Darknet Traffic...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Etyang, Felix Pavithran, Pramod Mwendwa, Gideon Mandela, Ngaira Hillary, Musiime School of Engineering Cochin University of Science & Technology Department of Computer Science Cochin India School of Cyber Security and Digital Forensics National Forensics Sciences University Gandhinagar India School of Cyber Security and Digital Forensics National Forensics Sciences University Jinja Uganda

ISBN: (纸本)9798331518592

The classification of darknet traffic remains a pivotal task in modern cybersecurity efforts, necessitating advanced analytical techniques to address malicious activities pre-emptively. This study introduces and compares the efficacy of several deep learning models for classifying darknet traffic, including a Feedforward Neural Network (FFNN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and a novel deep learning model designed to enhance classification performance. Utilizing the CIC-Darknet2020 dataset, we implement a comprehensive evaluation using accuracy, precision, recall, and F1 score metrics. The results reveal that the proposed model surpasses traditional architectures, significantly improving classification accuracy and reliability, thereby contributing valuable insights to cybersecurity. © 2024 IEEE.

关键词： Adversarial machine learning

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Internet of Things Intrusion Detection System Based on Convolutional Neural Network

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Computers, Materials & Continua 2023年第4期75卷 2119-2135页

作者： Jie Yin Yuxuan Shi Wen Deng Chang Yin Tiannan Wang Yuchen Song Tianyao Li Yicheng Li Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjingChina Engineering Research Center of Electronic Data Forensics Analysis NanjingChina Key Laboratory of Digital Forensics Department of Public Security of Jiangsu ProvinceNanjingChina

In recent years, the Internet of Things (IoT) technology has developedby leaps and bounds. However, the large and heterogeneous networkstructure of IoT brings high management costs. In particular, the low costof IoT devices exposes them to more serious security concerns. First, aconvolutional neural network intrusion detection system for IoT devices isproposed. After cleaning and preprocessing the NSL-KDD dataset, this paperuses feature engineering methods to select appropriate features. Then, basedon the combination of DCNN and machine learning, this paper designs acloud-based loss function, which adopts a regularization method to preventoverfitting. The model consists of one input layer, two convolutional layers,two pooling layers and three fully connected layers and one output ***, a framework that can fully consider the user’s privacy protection isproposed. The framework can only exchange model parameters or intermediateresults without exchanging local individuals or sample data. This paperfurther builds a global model based on virtual fusion data, so as to achievea balance between data privacy protection and data sharing computing. Theperformance indicators such as accuracy, precision, recall, F1 score, and AUCof the model are verified by simulation. The results show that the model ishelpful in solving the problem that the IoT intrusion detection system cannotachieve high precision and low cost at the same time.

关键词： Internet of things intrusion detection system convolutional neural network federated learning

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Cross-Cloud Associated Multi-Replica Auditing for Lightweight Devices in the IoT

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IEEE Internet of Things Journal 2025年第13期12卷 23495-23509页

作者： Hou, Gaopan Liu, Zhiquan Guo, Jingjing Miao, Yinbin Ma, Jianfeng Gao, Dawei Liao, Guisheng Xidian University Hangzhou Institute of Technology Hangzhou311231 China Jinan University College of Cyber Security Guangzhou510632 China Xidian University School of Cyber Engineering Xi'an710071 China

Cloud storage has become prevalent in Internet of Things (IoT) systems, attributed to its robust storage capabilities and user convenience. However, the cloud-based storage model, which separates data ownership from management, introduces integrity challenges due to the vulnerability of data to tampering. To address the risk of data loss and ensure recoverability, the implementation of multiple replicas is a common strategy. Nonetheless, traditional multi-replica auditing schemes are not well-suited for IoT environments that employ lightweight devices with limited computational capabilities. In response to the aforementioned challenges, we propose a novel multi-replica auditing scheme named CCMR, aimed at alleviating the heavy computation cost on the device side. Our scheme leverages an efficient aggregated multi-signature algorithm, offloading computationally intensive tasks associated with data tags from lightweight devices to cloud service providers equipped with advanced computational power. The innovative cross-cloud multi-replica hash tree structure, named CC-MHT, facilitates the secure and efficient verification of data block structures and ensures consistency of replicas across multi-cloud environments. Furthermore, by utilizing blockchain as a public random source, a robust challenge-response protocol is established to guard against potential audit failures that could arise from collusion between the third-party auditor and cloud service providers. The experimental results indicate the high efficiency of the proposed scheme in terms of computation cost. © 2014 IEEE.

关键词： Cloud storage

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Label-Aware Chinese Event Detection with Heterogeneous Graph Attention Network

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Journal of Computer Science & technology 2024年第1期39卷 227-242页

作者：崔诗尧郁博文从鑫柳厅文谭庆丰时金桥 Institute of Information Engineering Chinese Academy of SciencesBeijing 100190China School of Cyber Security University of Chinese Academy of SciencesBeijing 100049China Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou 510006China School of Cyber Security Beijing University of Posts and TelecommunicationsBeijing 100088China

Event detection(ED)seeks to recognize event triggers and classify them into the predefined event *** ED is formulated as a character-level task owing to the uncertain word *** methods try to incorpo-rate word-level information into characters to enhance their ***,they experience two ***,they fail to incorporate word-level information into each character the word encompasses,causing the insufficient word-charac-ter interaction ***,they struggle to distinguish events of similar types with limited annotated instances,which is called the event confusing *** paper proposes a novel model named Label-Aware Heterogeneous Graph Attention Network(L-HGAT)to address these two ***,we first build a heterogeneous graph of two node types and three edge types to maximally preserve word-character interactions,and then deploy a heterogeneous graph attention network to enhance the semantic propagation between characters and ***,we design a pushing-away game to enlarge the predicting gap between the ground-truth event type and its confusing counterpart for each *** results show that our L-HGAT model consistently achieves superior performance over prior competitive methods.

关键词： Chinese event detection heterogeneous graph attention network(HGAT) label embedding

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An Early Warning Model of Telecommunication Network Fraud Based on User Portrait

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Computers, Materials & Continua 2023年第4期75卷 1561-1576页

作者： Wen Deng Guangjun Liang Chenfei Yu Kefan Yao Chengrui Wang Xuan Zhang Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjingChina Engineering Research Center of Electronic Data Forensics Analysis NanjingChina Department of Public Security of Jiangsu Province Key Laboratory of Digital ForensicsNanjingChina

With the frequent occurrence of telecommunications and networkfraud crimes in recent years, new frauds have emerged one after another whichhas caused huge losses to the people. However, due to the lack of an effectivepreventive mechanism, the police are often in a passive position. Usingtechnologies such as web crawlers, feature engineering, deep learning, andartificial intelligence, this paper proposes a user portrait fraudwarning schemebased on Weibo public data. First, we perform preliminary screening andcleaning based on the keyword “defrauded” to obtain valid fraudulent userIdentity Documents (IDs). The basic information and account information ofthese users is user-labeled to achieve the purpose of distinguishing the typesof fraud. Secondly, through feature engineering technologies such as avatarrecognition, Artificial Intelligence (AI) sentiment analysis, data screening,and follower blogger type analysis, these pictures and texts will be abstractedinto user preferences and personality characteristics which integrate multidimensionalinformation to build user portraits. Third, deep neural networktraining is performed on the cube. 80% percent of the data is predicted basedon the N-way K-shot problem and used to train the model, and the remaining20% is used for model accuracy evaluation. Experiments have shown thatFew-short learning has higher accuracy compared with Long Short TermMemory (LSTM), Recurrent Neural Networks (RNN) and ConvolutionalNeural Network (CNN). On this basis, this paper develops a WeChat smallprogram for early warning of telecommunications network fraud based onuser portraits. When the user enters some personal information on the frontend, the back-end database can perform correlation analysis by itself, so as tomatch the most likely fraud types and give relevant early warning *** fraud warning model is highly scaleable. The data of other Applications(APPs) can be extended to further improve the efficiency of anti-fraud whichhas e

关键词： Crawler user portrait feature engineering deep learning small program development

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Research on Metaverse security and forensics

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Computers, Materials & Continua 2023年第10期77卷 799-825页

作者： Guangjun Liang Jianfang Xin Qun Wang Xueli Ni Xiangmin Guo Pu Chen Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjingChina Engineering Research Center of Electronic Data Forensics Analysis NanjingChina Key Laboratory of Digital Forensics Department of Public Security of Jiangsu ProvinceNanjingChina School of Intelligent Engineering Nanjing Institute of Railway TechnologyNanjingChina

As a subversive concept,the metaverse has recently attracted widespread attention around the world and has set off a wave of enthusiasm in academic,industrial,and investment ***,while the metaverse brings unprecedented opportunities for transformation to human society,it also contains related *** is a digital living space with information infrastructure,interoperability system,content production system,and value settlement system as the underlying structure in which the inner core is to connect real residents through applications and *** social incentives and governance rules,the metaverse reflects the digital migration of human *** article will conduct an in-depth analysis of the metaverse from the perspective of electronic data ***,from the perspective of Internet development,the background and development process of the metaverse is *** systematically elaborating on the concept and connotation of the metaverse,this paper summarizes the different views of current practitioners,experts,and scholars on the ***,from the perspective of metaverse security,the social risk and crime risks of the metaverse are *** the importance of metaverse forensics is ***,from the perspective of blockchain,smart wearable devices,and virtual reality devices,the objects and characteristics of metaverse forensics have been studied in *** smart wearable devices as an example,this paper gives the relevant experimental process of smart bracelet ***,many challenges faced by metaverse forensics are summarized by us which provide readers with some exploratory guidance.

关键词： Metaverse forensics blockchain smart wear virtual reality

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GuardGrid: A Queriable and Privacy-Preserving Aggregation Scheme for Smart Grid via Function Encryption

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IEEE Internet of Things Journal 2025年第11期12卷 17622-17633页

作者： Yu, Pengfei Huang, Weicong Zhang, Ran Qian, Xinyuan Li, Hongwei Chen, Hanxiao China Electric Power Research Institute State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology Nanjing210003 China University of Electronic Science and Technology of China School of Computer Science and Engineering School of Cyber Security Chengdu611731 China

Smart grids have revolutionized electricity management by leveraging real-time consumption data, enabling more efficient power control through advanced algorithms. However, this transformation raises significant privacy and security concerns due to the extensive collection of user data. Current solutions face challenges, such as aggregator gateway misbehavior, lack of support for function queries, and the need to balance privacy with efficiency. In this article, we propose FEHH, a novel scheme that ensures both privacy preservation and verifiable aggregation. It allows multiple aggregators to perform inner-product computations on encrypted data while safeguarding the aggregated results from the aggregator. Additionally, it supports verification of aggregated data’s correctness using Linear Homomorphic Hash. Building on FEHH, we introduce GuardGrid, a privacy-preserving aggregation scheme for smart grids that inherits FEHH’s core features and adds support for essential arithmetic operations necessary for function queries. This allows cloud servers to respond to queries from either the control center or users without compromising data confidentiality. Experimental results show that the encryption overhead of GuardGrid is only 7% of that of the PPDA scheme, and its communication overhead is 123x less. These results demonstrate that GuardGrid significantly reduces computation and communication costs, providing a more sustainable and cost-effective smart grid solution. © 2014 IEEE.

关键词： Differential privacy

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Combating Terrorism: Detecting and Analyzing Extremist Content on X (Formerly Twitter) 3

Combating Terrorism: Detecting and Analyzing Extremist Conte...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Mwendwa, Gideon Chouhan, Lokesh Etyang, Felix Mandela, Ngaira Gift, Uwineza School of Cyber Security and Digital Forensics National Forensic Sciences University Goa India Cochin University of Science and Technology School of Engineering Department of Computer Science Cochin India School of Cyber Security and Digital Forensics National Forensic Sciences University Gandhinagar India School of Cyber Security and Digital Forensics National Forensic Sciences University Jinja Uganda

ISBN: (纸本)9798331518592

This study investigates the efficacy of machine learning algorithms in detecting and evaluating extremist content on social media platforms, and we employ X, formerly known as Twitter. We therefore utilize Kaggle's dataset that contains 83,000 entries from both extremist and non-extremist posts to compare the performance of four machine learning models, namely Support Vector Machine (SVM), Random Forest, Gradient Boosting, and Naive Bayes. Moreover, we examine a Stacking Classifier that leverages the advantages of all four models. The models were assessed using accuracy, precision, recall, F1 Score, ROC-AUC Score, training duration, and prediction time. The findings of our study revealed that the Support Vector Machine (SVM) algorithm was the most accurate and resilient, whereas Naive Bayes showcased remarkable computing efficiency, rendering it well-suited for real-time detection. The Random Forest and Gradient Boosting algorithms offered a well-balanced approach. Notably, the novel Stacking Classifier outperformed individual models, demonstrating the strength of a hybrid methodology. Hence our study presents a thorough structure for improving the identification of extremist material on social media, thereby providing essential knowledge for creating efficient, immediate detection systems to combat online extremism successfully. © 2024 IEEE.

关键词： Tweets

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