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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Zhou, Ruiting Yu, Jieling Wang, Ruobei Li, Bo Jiang, Jiacheng Wu, Libing Wuhan University School of Cyber Science and Engineering China Southeast University School of Computer Science Engineering China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350334142

Federated Learning (FL) enables potentially a large number of clients to collaboratively train a global model with the coordination of a central cloud server without exposing client raw data. However, the FL model convergence performance, often measured by the job completion time, is hindered by two critical factors: non independent and identically distributed (non-IID) data across clients and the straggler effect. In this work, we propose a clustered FL framework, MCFL, to minimize the job completion time by mitigating the influence of non-IID data and the straggler effect while guaranteeing the FL model convergence performance. MCFL builds upon a two-stage operation: i) a clustering algorithm constructs clusters, each containing clients with similar computing and communications capabilities to combat the straggler effect within a cluster;ii) a deep reinforcement learning (DRL) algorithm based on soft actor-critic with discrete actions intelligently selects a subset of clients from each cluster to mitigate the impact of non-IID data, and derives the number of intra-cluster aggregation iterations for each cluster to reduce the straggler effect among clusters. Extensive testbed experiments are conducted under various configurations to verify the efficacy of MCFL. The results show that MCFL can reduce the job completion time by up to 70% compared with three state-of-the-art FL frameworks. © 2023 IEEE.

关键词： Clustering algorithms

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An Improved Wildfire Identification Method Based on Yolov7 and Attention Mechanism 3

An Improved Wildfire Identification Method Based on Yolov7 a...

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3rd International Conference on Artificial Intelligence, Virtual Reality, and Visualization, AIVRV 2023

作者： Yang, Qiang Yu, Fan Zhang, Gexiang Jin, Xiaozhao Guo, Dequan Wang, Ping Yao, Guangle Liang, Kai College of Computer Science and Cyber Security Chengdu University of Technology Chengdu China School of Automation Chengdu University of Information Technology Chengdu China School of Network & Communication Engineering Chengdu Technological University Chengdu China

ISBN: (纸本)9781510671485

Wildfires, also called forest fires, are a common natural disaster that often occur in forests and are difficult to control. Detecting and suppressing them at an early stage, primarily through monitoring smoke and fires, is crucial in reducing losses. Thanks to the efforts of researchers, wildfire detection technology has advanced significantly, from traditional manual monitoring to target detection, sensor detection, and infrared detection. However, the various detection methods still have some issues, including low accuracy, high costs, slow detection speeds and susceptibility to interference. This paper presents an improved approach to identifying wildfires based on YOLOv7 with CBAM. Our experimental results indicate that our approach achieves a mean Average Precision (mAP) of 95.77%, surpassing VGG-SSD with CBAM by 0.53%, MobileNetv2-SSD with CBAM by 0.37%, and Faster R-CNN with CBAM by 2.1%. Thus, our method offers a highly accurate approach to wildfire identification. © 2023 SPIE.

关键词： Smoke

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Machine-Learning-Based Spam Mail Detector

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SN computer Science 2023年第6期4卷 858页

作者： Charanarur, Panem Jain, Harsh Rao, G. Srinivasa Samanta, Debabrata Sengar, Sandeep Singh Hewage, Chaminda Thushara Department of Cyber Security and Digital Forensics National Forensic Sciences University Tripura campus Tripura Agartala India School of Cyber Security and Digital Forensics Goa Ponda India Department of Computer Science Government Degree College Sitaphalmandi Hyderabad India Department of Computing and Information Technologies Rochester Institute of Technology Kosovo Germia Campus Prishtina Kosovo 10000 Department of Computer Science Cardiff Metropolitan University Cardiff CF5 2YB United Kingdom

The proliferation of spam emails, a predominant form of online harassment, has elevated the significance of email in daily life. As a consequence, a substantial portion of individuals remain vulnerable to fraudulent activities. Despite Gmail’s “spam mail filtration system,” its effectiveness is not absolute. It occasionally misclassifies legitimate messages, leading to their confinement in the spam folder, or overlooks potentially harmful spam emails. This results in the occurrence of false positives. This research scrutinizes the historical data, cookies, caches, Session Restores, flash artifacts, and super cookies of Internet Explorer, Firefox, and Chrome on the Windows 10 platform. Data was collected through Google, Firefox, and Internet Explorer, operating within the Windows 10 environment. It has been observed that browsers store user behavior data on the host computer’s hard drive. The implications of this study hold substantial value for computer forensics researchers, law enforcement professionals, and digital forensics experts. The study leverages Python, alongside pertinent libraries such as pandas, Numpy, Matplotlib, scikit-learn, and flask, to facilitate its investigation. The experiment result and analysis show KN and NB algorithms have the best accuracy and precision score compared to other Algorithms. © 2023, Crown.

关键词： Cookies E-mail Inbox Spam mail Trap Trash folder

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Performance Analysis of Three Spectrum Sensing Detection Techniques with Ambient Backscatter Communication in Cognitive Radio Networks

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computer Modeling in engineering & Sciences 2023年第10期137卷 813-825页

作者： Shayla Islam Anil Kumar Budati Mohammad Kamrul Hasan Saoucene Mahfoudh Syed Bilal Hussian Shah Institute of Computer Science&Digital Innovation ICSDIUCSI UniversityKuala Lumpur56000Malaysia Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)Selangor43600Malaysia School of Engineering Computing and InformaticsDar Al-Hekma UniversityJeddah22246Saudi Arabia

In wireless communications, the Ambient Backscatter Communication (AmBC) technique is a promisingapproach, detecting user presence accurately at low power levels. At low power or a low Signal-to-Noise Ratio(SNR), there is no dedicated power for the users. Instead, they can transmit information by reflecting the ambientRadio Frequency (RF) signals in the spectrum. Therefore, it is essential to detect user presence in the spectrum forthe transmission of data without loss or without collision at a specific time. In this paper, the authors proposed anovel Spectrum Sensing (SS) detection technique in the Cognitive Radio (CR) spectrum, by developing the *** Matched Filter Detection with Inverse covariance (MFDI), Cyclostationary Feature Detection with Inversecovariance (CFDI) and Hybrid Filter Detection with Inverse covariance (HFDI) approaches are used with AmBCto detect the presence of users at low power levels. The performance of the three detection techniques is measuredusing the parameters of Probability of Detection (PD), Probability of False Alarms (Pfa), Probability of MissedDetection (Pmd), sensing time and throughput at low power or low SNR. The results show that there is a significantimprovement via the HFDI technique for all the parameters.

关键词： Ambient backscatter communication cognitive radio MFDI CFDI HFDI

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Tjong:A transformer‐based Mahjong AI via hierarchical decision‐making and fan backward

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CAAI Transactions on Intelligence technology 2024年第4期9卷 982-995页

作者： Xiali Li Bo Liu Zhi Wei Zhaoqi Wang Licheng Wu School of Information and Engineering Minzu University of ChinaBeijingChina Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE Minzu University of ChinaBeijingChina Department of Computer Science New Jersey Institute of TechnologyNewarkNew JerseyUSA

Mahjong,a complex game with hidden information and sparse rewards,poses significant *** Mahjong AIs require substantial hardware resources and extensive datasets to enhance AI *** authors propose a transformer‐based Mahjong AI(Tjong)via hierarchical decision‐*** utilising self‐attention mechanisms,Tjong effectively captures tile patterns and game dynamics,and it decouples the decision pro-cess into two distinct stages:action decision and tile *** design reduces de-cision complexity ***,a fan backward technique is proposed to address the sparse rewards by allocating reversed rewards for actions based on winning *** consists of 15M parameters and is trained using approximately 0.5 M data over 7 days of supervised learning on a single server with 2 *** action decision achieved an accuracy of 94.63%,while the claim decision attained 98.55%and the discard decision reached 81.51%.In a tournament format,Tjong outperformed AIs(CNN,MLP,RNN,ResNet,VIT),achieving scores up to 230%higher than its ***-more,after 3 days of reinforcement learning training,it ranked within the top 1%on the leaderboard on the Botzone platform.

关键词： decision making deep learning deep neural networks

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Frequency Attention Module for Speaker Recognition 5

Frequency Attention Module for Speaker Recognition

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5th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2022

作者： Wu, Yujiao Fang, Zulin Dong, Jianping Zhang, Gexiang Chengdu University of Technology College of Computer Science and Cyber Security Chengdu China Chengdu University of Technology School of Nuclear Technology and Automation Engineering Chengdu China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

Speaker recognition is a task of identifying user's individuality from their speech utterances. Most of the text-independent speaker recognition models available directly input the spectrogram into the deep neural network. However, this does not take full advantage of the spectrogram features. In this paper, we propose a frequency attention module (FAM) based on the fact that the frequency axis in the spectrogram is more representative for speaker feature extraction. It aims to assigning higher weights to important frequency bins, and making the network to extract more features to improve speaker recognition performance. Furthermore, FAM is applied on the convolution output in ResBlock of ResNet34 to capture richer features of the speaker. The experimental results under self-attention pooling (SAP) and attentive-statistics pooling (ASP) show that FAM is feasible and effective. Moreover, the extended experimental results on the Free ST Chinese Mandarin Corpus dataset indicate that the proposed FAM (equal error rate (EER) is 1.59%) outperforms the no-attention ResNet34 (EER is 1.97 %) and spatial-CBAM ResNet34 (EER is 1.88%). © 2022 IEEE.

关键词： Deep neural networks

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Traceable Attribute-Based Keyword Search Scheme for Distributed Data Storage under Fog Computing-Based Social Internet of Vehicles

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Gu, Ke Qiu, Juan Peng, Xin Li, Xiong Changsha University of Science and Technology School of Computer and Communication Engineering Changsha410114 China University of Electronic Science and Technology of China Institute for Cyber Security School of Computer Science and Engineering Chengdu611731 China

In Social Internet of Vehicles (SIoVs), vehicles frequently need to obtain their required data, thus many secure data acquisition schemes were proposed in cloud computing-based SIoVs. However, the distributed data storage method under fog computing-based SIoVs can bring about more security issues, such as potential data leakage, unauthorized access and integrity concerns. To address these challenges, we propose a traceable attribute-based keyword search scheme for distributed data storage based on SIoVs under fog computing in this paper. According to the Shamir's secret-sharing scheme, our proposed scheme provides a distributed data block verification participated by multiple fog servers to check the effectiveness of distributed data storage. In our scheme, a legal data-owned vehicle can divide its encrypted data into many data blocks, and securely upload these data blocks and related keywords to multiple fog servers;a data-required vehicle can generate a trapdoor on the keywords of required data, and then decrypt the data blocks received from related fog servers. Further, the key generation center can trace the identity of a suspicious data-owned vehicle according to related data when it is confirmed that there is false traffic data uploaded by the suspicious data-owned vehicle. Under the proposed security models, our proposed scheme is proved to secure stored data and keyword search in fog computing-based SIoVs. Additionally, experimental results show that our scheme is effective. © 2015 IEEE.

关键词： Information leakage

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An Efficient Intrusion Detection Framework for Industrial Internet of Things security

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computer Systems Science & engineering 2023年第7期46卷 819-834页

作者： Samah Alshathri Ayman El-Sayed Walid El-Shafai Ezz El-Din Hemdan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Recently,the Internet of Things(IoT)has been used in various applications such as manufacturing,transportation,agriculture,and healthcare that can enhance efficiency and productivity via an intelligent management console *** the increased use of Industrial IoT(IIoT)applications,the risk of brutal cyber-attacks also *** leads researchers worldwide to work on developing effective Intrusion Detection Systems(IDS)for IoT infrastructure against any malicious ***,this paper provides effective IDS to detect and classify unpredicted and unpredictable severe attacks in contradiction to the IoT infrastructure.A comprehensive evaluation examined on a new available benchmark TON_IoT dataset is *** data-driven IoT/IIoT dataset incorporates a label feature indicating classes of normal and attack-targeting IoT/IIoT ***,this data involves IoT/IIoT services-based telemetry data that involves operating systems logs and IoT-based traffic networks collected from a realistic medium-scale IoT *** is to classify and recognize the intrusion activity and provide the intrusion detection objectives in IoT environments in an efficient ***,several machine learning algorithms such as Logistic Regression(LR),Linear Discriminant Analysis(LDA),K-Nearest Neighbors(KNN),Gaussian Naive Bayes(NB),Classification and Regression Tree(CART),Random Forest(RF),and AdaBoost(AB)are used for the detection intent on thirteen different intrusion *** performance metrics like accuracy,precision,recall,and F1-score are used to estimate the proposed *** experimental results show that the CART surpasses the other algorithms with the highest accuracy values like 0.97,1.00,0.99,0.99,1.00,1.00,and 1.00 for effectively detecting the intrusion activities on the IoT/IIoT infrastructure on most of the employed *** addition,the proposed work accomplishes high performance compared to other recent rela

关键词： Attacks intrusion detection machine learning deep learning industrial IoT TON_IoT dataset

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Design and Implementation of a BERT Based Weibo Public Opinion Monitoring System 14th

Design and Implementation of a BERT Based Weibo Public Opini...

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14th International Conference on computer engineering and Networks, CENet 2024

作者： Chen, Shun-Chao Zhu, Tao Pang, Zi-An Xu, Chen Department of Computer Information and Cyber Security Key Laboratory of Digital Forensics Department of Public Security of Jiangsu Province Jiangsu Police Institute Engineering Research Center of Electronic Data Forensics Analysis Jiangsu Province NAN Jing 210031 China

ISBN: (纸本)9789819642281

With the rapid development of online We Media, it is not easy to monitor and guide massive public opinion information. The massive forwarding, emotional polarization, and mixed rumors of blog posts are highly likely to cause online public opinion events. A Weibo public opinion monitoring system based on bidirectional encoder representation from transformers (BERT) is proposed to support the full lifecycle guidance for public opinion. After Weibo text data is crawled from blog posts of important informants according to the lifecycle stages, and preprocessed for data cleaning and text vectorization, an emotional tendency classification and rumor detection model with the way of ensemble learning is proposed with the latent Dirichlet allocation (LDA) model and three BERT-based combining model. For extracting emotional tendency information efficiently, the LDA model is applied to extract topics on emotional polarity in blog posts. In order to address the issue of long text data caused by the lifting of restrictions on short posts on Weibo, three BERT-based model are ensembled together to achieve better performance. The experiment test demonstrates that the evolution of online public opinion can be described efficiently by this system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： BERT LDA public opinion monitoring We Media

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cybersecurity Revolution via Large Language Models and Explainable AI 17

Cybersecurity Revolution via Large Language Models and Expla...

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17th International Conference on security of Information and Networks, SIN 2024

作者： Ghazal, Taher M. Janjua, Jamshaid Iqbal Abushiba, Walid Ahmad, Munir Ihsan, Anuam Al-Dmour, Nidal A. College of Arts & Science Applied Science University Manama Bahrain Center for Cyber Security Faculty of Information Science and Technology Selangor Malaysia Lahore Pakistan College of Engineering Applied Science University Bahrain College of Informatics Korea University Seoul02841 Korea Republic of College of Engineering Mutah University Department of Computer Engineering Jordan

ISBN: (纸本)9798331509736

Integrating Groundbreaking advancements in AI, like language models, interpretable AI, and machine learning, opens up a world of exciting new possibilities. The Evolving face of cybersecurity and Modern cyber threats are complex and well crafted;hence, conventional cybersecurity mechanisms show difficulty in staying relevant. LLMs, especially based on Transformer architecture will noticeably increase the accuracy and speed of detecting threats. Transparency and trust are increased by XAI approaches like SHAP and LIME, which offer facts about ML model predictions. This paper explores the literature that demonstrates the integration between XAI and LLMs in cybersecurity, exemplifying how this trinity of models has the potential to help attenuate errors producing reduced false positives and improve how we detect threats. Thinking about the possibilities the challenges including performance Explainability trade-offs, the need for common evaluation metrics, and the black-box nature of AI Models, remain in place. Solving these will help to enhance AI-driven solutions in cybersecurity. © 2024 IEEE.

关键词： cybersecurity Explainable AI (XAI) Large Language Models (LLMs) Machine Learning (ML) Reliability Threat Detection Transformer Architecture Transparency

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