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Understanding cybercrime: A Study of Malware Analysis, Phishing Attacks, and Sentiment Trends 3

Understanding Cybercrime: A Study of Malware Analysis, Phish...

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Kumaran, U. Sreeraag, G. Narendran, Advik Meena, V.P. Gupta, Sushil Kumar Appaji, Abhishek Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bangalore India National Institute of Technology Department of Electrical Engineering Jharkhand Jamshedpur831014 India Bms College of Engineering Department of Medical Electronics Engineering Bangalore India

ISBN: (纸本)9798331515911

With the rapid evolution of computer systems and technologies, individuals face an increasing threat of cybercrime. This research explores the changing landscape of cybercrime by focusing on three key areas: malware analysis, phishing attack analysis, and sentiment analysis. The study aims to identify malware strategies for evading detection and analyze the prevalence of spyware by examining diverse malware samples. Additionally, trend analysis highlights the rising complexity and frequency of phishing attacks, with a notable shift toward targeting reputable platforms. Sentiment analysis of media headlines surrounding data breaches uncovers a paradox: while concerns about cybersecurity remain, there is a growing confidence in the ability to manage cybersecurity crises. These findings provide insights into emerging threats and evolving public perceptions, offering a comprehensive understanding of the current cyber- crime landscape. © 2025 ieee.

关键词： Phishing

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A Comprehensive Review of Robotics Advancements Through Imitation Learning for Self-Learning systems

A Comprehensive Review of Robotics Advancements Through Imit...

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9th international conference on Mechanical Engineering and Robotics Research, ICMERR 2025

作者： Jadeja, Yagna Shafik, Mahmoud Wood, Paul Makkar, Aaisha College of Science and Engineering University of Derby Derby United Kingdom

ISBN: (纸本)9798331509262

In recent years, robotics and artificial intelligence (AI) have witnessed significant growth, particularly in self-learning systems. This paper examines the remarkable progress made in this area, with a particular focus on the utilisation of imitation learning. Self-learning robotics systems have demonstrated the autonomous acquisition of new skills, making them highly adaptable and versatile. Imitation learning is a crucial technique that allows robots to gain knowledge from human demonstrations. This paradigm allows machines to learn and replicate human actions, thus enhancing the capabilities of self-learning robotic technology. The primary objective of this research was to investigate the potential of imitation learning and evaluate its impact on the advancement of self-learning robotics. This paper provides a comprehensive overview of self-learning robotic systems using imitation learning, examining the foundational concepts, essential methodologies, and various applications in this intriguing area. Furthermore, we highlight recent developments, discuss current trends, and outline potential research initiatives to guide the continued development of self-learning robotic systems using imitation learning. This review aims to contribute to the evolving landscape of autonomous robotics by consolidating knowledge, identifying challenges, and fostering further innovation in the pursuit of intelligent self-learning machines. © 2025 ieee.

关键词： intelligent robots

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When Crowdsensing Meets Smart Cities: A Comprehensive Survey and New Perspectives

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ieee Communications Surveys and Tutorials 2025年第2期27卷 1101-1151页

作者： Wang, Zhenning Cao, Yue Jiang, Kai Zhou, Huan Kang, Jiawen Zhuang, Yuan Tian, Daxin Leung, Victor C. M. Wuhan University School of Cyber Science and Engineering Wuhan430072 China Northwestern Polytechnical University College of Computer Xi’an710129 China Guangdong University of Technology School of Automation Guangzhou510006 China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan430079 China Beihang University School of Transportation Science and Engineering Beijing100191 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518061 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Crowdsensing has received widespread attention in recent years. It is extensively employed in smart cities and intelligent transportation systems. This paper comprehensively surveys the latest research advancements in crowdsensing for smart cities from a novel perspective. Specifically, this paper is categorized according to sensing entities in smart cities, including human-oriented sensing, vehicle-oriented sensing, and infrastructure-oriented sensing. Meanwhile, the development of Unmanned Aerial Vehicle (UAV)-assisted sensing in recent years is also summarized, accompanied by a timeline of related research. To facilitate easy comprehension, we have positioned the reading flow into the corresponding architectures, resolved problems, existing technical solutions, and specific application scenarios for different sensing entities. In particular, the problems to be solved are further analyzed from four technical perspectives, namely mathematics and operational research, artificial intelligence and machine learning, incentive mechanisms, security and privacy protection. Based on the proposed taxonomy, recent studies are thoroughly investigated to illustrate the current state of research in crowdsensing. Furthermore, this paper highlights the emerging applications of human-oriented and vehicle-oriented sensing in smart cities, as well as the frameworks, platforms, simulators, and datasets involved in crowdsensing. Finally, this paper discusses research directions related to crowdsensing in smart cities, such as digital twins, metaverses, and artificial intelligence-generated content. The primary goal of this survey is to review and synthesize prior research, identify potential avenues for future research, and explore opportunities for collaboration with other relevant research domains. © 1998-2012 ieee.

关键词： Smart city

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Research on Medical Efficiency of Beijing-Tianjin-Hebei Based on SBM Model

Research on Medical Efficiency of Beijing-Tianjin-Hebei Base...

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2024 international conference on Mechatronics and intelligent control, ICMIC 2024

作者： Bai, Yanxin Dong, Yaru Ding, Haozhe Zhao, Chengyu Yang, Zixi Liu, Shuo Zhang, Xinpeng Song, Weiyou School of Economics and Management Hebei University of Technology 300131 China School of Artificial Intelligence and Data Science Hebei University of Technology 300131 China School of Mathematics and Statistics Hebei University of Economics and Trade 050064 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Based on the data of Beijing-Tianjin-Hebei region from 2011 to 2019,this paper measures the medical efficiency of Beijing-Tianjin-Hebei region in recent ten years by using the super efficiency SBM model, and uses the ML index decomposition method to conduct a dynamic analysis of medical efficiency, and further discusses the calculation results through convergence analysis, The medical efficiency in Beijing-Tianjin-Hebei region was studied. The study found that the overall medical efficiency in Beijing-Tianjin-Hebei region showed an increasing trend, but the increase was not large, and there was greater room for progress;The convergence of ML index changes is also high, indicating the overall development of Beijing-Tianjin-Hebei region. © 2025 SPIE.

关键词： Medical research Data modeling Analytical research Data acquisition Medical equipment Diseases and disorders systems modeling Biomedical applications Education and training Energy efficiency

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Computer-Assisted Knowledge Points and Question Difficulty Classification Based on BERT

Computer-Assisted Knowledge Points and Question Difficulty C...

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2024 international conference on Mechatronics and intelligent control, ICMIC 2024

作者： Zhu, Jia Jun Yang, Zichuan Hong, Binjie Song, Jiacheng Wang, Jiwei Chen, Tianhao Yang, Shuilan Lan, Zixun Ma, Fei Department of Information and Computing Science Xi'an Jiaotong-Liverpool University Suzhou China School of Engineering Suzhou Centennial College Suzhou China Department of Applied Mathematics Xi'an Jiaotong-Liverpool University Suzhou China GROW Inc. Suzhou China EasyStack Inc. Nanjing China TAL Education Group Beijing China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

In recent years, with the increasing use of educational technology and online learning platforms, there has been a growing interest in developing intelligent systems that can automatically predict the knowledge points associated with educational questions. This paper presents a novel approach for knowledge point prediction in middle school mathematics questions. The dataset used in this study consists of a large collection of 591,379 middle school mathematics questions. To leverage the power of natural language processing techniques, the questions are preprocessed using a tokenizer and encoded into word embeddings using BERT (Bidirectional Encoder Representations from Transformers). Matrix dot production is then employed to calculate the similarity between each test question and the training set, and the top-n most similar vectors are selected for each test question. A voting mechanism is introduced to eliminate duplicate knowledge points and rank them based on their total scores in descending order, resulting in k candidate solutions. The performance of the proposed approach is evaluated using top-k accuracy as the evaluation metric and a lowest common ancestor (LCA) algorithm is used to calculate accuracy at different levels of the knowledge point tree. The results demonstrate the effectiveness of the proposed approach in predicting knowledge points for middle school mathematics questions and its potential for application in intelligent tutoring systems and educational assessment tools. © 2025 SPIE.

关键词： Natural language processing systems

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Distributed Event-Triggering Secondary Frequency control of Islanded Microgrids Under DoS Attacks 8th

Distributed Event-Triggering Secondary Frequency Control of...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Zhou, Lihua Zhang, Changda Du, Dajun Fei, Minrui Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronic Engineering and Automation Shanghai University Shanghai China School of Electrical Engineering Hebei University of Science and Technology Hebei China

ISBN: (纸本)9789819602247

High reliability and efficiency of microgrids are being threatened by cyber attacks. This paper investigates a Secondary Frequency control Scheme (SFCS) for an islanded microgrid with Event-Triggering Mechanism (ETM) under Denial of Service (DoS) attacks. Firstly, the microgrid dynamic model is reviewed and control objectives are formulated. Secondly, a cooperative frequency control scheme combined with distributed event-triggering mechanism is presented to achieve the desired frequency control under DoS attacks. Thirdly, the stability and Zeno Behaviour of the closed-loop system are analysed by Lyapunov theory. Finally, simulation results of the microgrid are explored to verify the effectiveness of the proposed frequency control scheme. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： cyber attacks

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Construction and Analysis of Wooden Pallets Recycling Model Based on Closed-Loop Supply Chain

Construction and Analysis of Wooden Pallets Recycling Model ...

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2024 international conference on Mechatronics and intelligent control, ICMIC 2024

作者： Zha, Meng Zhang, Meizhou Zhang, Hanting Zhou, Min Xima Steel Logistics Co. Ltd. Wuhan China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

As an important tool for material stacking, handling and transportation in enterprises, wooden pallets have many problems such as large loss, low recovery rate and high procurement cost. Therefore, improving the recycled rate and recycling utilization rate of wooden pallet, and reducing its failure rate, is an effective means to reduce the operation cost of enterprises. Starting from the actual situation, this paper first establishes the closed-loop supply chain model of Company J, then concretizes and generalizes the entire supply chain model by constructing the closed-loop supply chain mathematical model. Finally, through analyzing and comparing the data related to wooden pallets of the company in two years, it proves that wooden pallets adopt the closed-loop supply chain recycling mechanism can greatly reduce the operating costs of enterprises, and also has important reference significance for realizing resource protection and green production. © 2025 SPIE.

关键词： Packaging Mathematical modeling Manufacturing Transportation Engineering Metals systems modeling Failure analysis Inspection Stochastic processes

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LANet: Enhancing Plant Disease Recognition Through Transfer Learning and Layer Attention 6

LANet: Enhancing Plant Disease Recognition Through Transfer ...

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6th ieee international conference on Image Processing, Applications and systems, IPAS 2025

作者： Lei, Xin Behera, Ardhendu Li, Fuzhong Zhang, Wuping Sun, Yang Liu, Yonghuai Edge Hill University Department of Computer Science Ormskirk United Kingdom Shanxi Agricultural University School of Software Jinzhong China Hebei University School of Cyber Security and Computer Baoding China

ISBN: (纸本)9798331506520

Accurate recognition of plant diseases plays a critical role in maintaining agricultural productivity and food security. This study proposes LANet, an innovative network architecture aimed at improving the precision of identifying and categorizing plant diseases from images. LANet consists of two primary components. Firstly, Layer Attention applies self-attention across feature layers at different scales to capture the weights between cross-scale features, addressing the data loss caused by downsampling and enhancing plant species recognition. Secondly, we introduce a transfer learning method, where a plant species classification network is initially trained, its parameters are then frozen, and several convolutional layers are added to train the disease classification network. This allows the model to leverage the learned classification information for more accurate disease recognition. Our model demonstrates an accuracy of 85.62%, outperforming nine other models significantly. Comparative analyses using the public PlantVillage dataset highlight the superior performance of our method in disease recognition. © 2025 ieee.

关键词： Deep learning Image processing Layer attention Plant disease recognition Transfer learning

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Detection of Anomaly in Internet of Things using Gated Recurrent Unit with Artificial Bee Colony 1

Detection of Anomaly in Internet of Things using Gated Recur...

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1st international conference on intelligent systems and Computational Networks, ICISCN 2025

作者： Karthik, M. Ganesh Divya, N. Sree Shnaeen, Amaar Haamid Alagarraja, K. Praveena, S. Gitam University Department of Computer Science and Engineering Bengaluru India Mahatma Gandhi Institute of Technology Department of Information Technology Hyderabad India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Electronics and Communication Engineering Mamallapuram India

ISBN: (纸本)9798331529246

Now a days, development in Internet of Things (IoT) networking generates large attack surface for cybercriminals, that impact the industry has resulted with exponential increase in cyber-attacks. To develop a robust anomaly detection system, a Gated Recurrent Unit Anomaly Detection (GRUAD) is proposed in this research. Initially, the data is collected from the Toyama IoT Network Traffic Dataset (TON-IoT) dataset which includes samples of packet capture data (PCAP files) and network logs (text files). Then, the collected data is pre-processed using data cleaning technique which removes noisy and irrelevant data, then label encoding and data normalization assist to transform data into a suitable format while data splitting. Further, features are selected using Mutual Information (MI) in which features with high mutual information with the target variable are selected. Then, Pearson Coefficient Correlation (PCC) identifies features with strong linear correlations. At the end, K-Best selects the top-ranked features. Finally, the Multistep-Prediction is performed using GRUAD by hyperparameter tuning the Artificial Bee Colony (ABC). The proposed model gained better results in terms of precision with value 98.54%, f1 score of 98.74%, Accuracy of 98.62%, recall of 98.77% comparing with existing method Logistic Regression (LR) respectively. © 2025 ieee.

关键词： Logistic regression

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An Improved DeeplabV3+ Tile Image Positioning Method

An Improved DeeplabV3+ Tile Image Positioning Method

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2024 international conference Optoelectronic Information and Optical Engineering, OIOE 2024

作者： Li, Xiaoyang Wang, Rui Tong, Xing Niu, Lianqiang Shenyang University of Technology Shenyang China Key Laboratory of Network Control Systems Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

In order to improve the detection accuracy and efficiency of surface defects on ceramic tiles, a detection model based on DeepLabV3+and combined with MobileNetv2 for feature extraction was constructed. Using ECA attention mechanism and multi branch receptive field cascade fusion structure to expand the sampling range of global and local element features for the empty space pyramid pooling module ASPP of the original model. Meanwhile, the model decoder uses depthwise separable convolutions instead of the original standard convolutions. Experimental results have shown that the improved model has an MPA value of 96.12%, a MIou value of 97.29%, an F1 value of 90.03%, and an Accuracy value of 99.89%. Compared with the previous model, the improved model's indicators have increased by 4.07%, 4.44%, 2.04%, and 1.02%, respectively. The segmentation accuracy and detection speed have been significantly improved. The experimental results fully demonstrate that the algorithm proposed in this article can effectively complete the task of detecting surface defects on ceramic tiles. © 2025 SPIE.

关键词： Image segmentation

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