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

作者： Yildiz, Mert Bahtiyar, Serif Istanbul Technical University Cyber Security and Privacy Research Lab SPFLab Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798331509736

The rise of decentralized identity systems has posed significant challenges in the secure and scalable management of keys, especially in large-scale national identity programs. In this paper, we propose a new secure and scalable framework for cryptographic keys management that may be applied in a national digital identity system. The proposed framework provides a hierarchical structure for efficient key generation and isolation, while hardware security modules provide a secure environment for key storage and operations. Key wrapping is implemented to enable secure external storage of large volumes of keys. In our work, we present a comprehensive security analysis. Our analysis demonstrates the resilience of the framework against various threat vectors and its ability to address key management challenges such as complexity, scalability, security isolation, recovery and secure delegation. The proposed framework provides a promising solution for security and scalability of national-level identity systems. © 2024 IEEE.

关键词： BIP32 decentralized identity hardware security modules key management key wrapping

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Generative Adversarial Networks for Synthetic Jamming Attacks on UAVs 9

Generative Adversarial Networks for Synthetic Jamming Attack...

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9th International Conference on computer Science and engineering, UBMK 2024

作者： Sarikaya, Burcu Sönmez Bahtiyar, Şerif Istanbul Technical University Cyber Security and Privacy Research Lab SPFLab Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350365887

Intrusion Detection Systems (IDS) have been significant for Unmanned Aerial Vehicles (UAVs) since high connectivity is essential for such vehicles. Recently, machine learning-based defense mechanisms have contributed to detecting intrusions. Data are crucial to better results with machine learning algorithms. However, security data is not freely distributed so there is a lack of security-related data about UAVs that can be processed with machine learning. In this research, we create synthetic security data for UAVs using Generative Adversarial Networks (GANs) that can be utilized to develop machine learning-based IDS systems. We propose employing Conditional Tabular Generative Adversarial Networks (CTGANs), Gaussian copulas, and Variational AutoEncoders (VAEs) to generate syn-thetic jamming attacks from the widely-used UAV attack dataset. We experimentally evaluated the performance of each model by using many metrics, such as Kullback-Leibler decomposition. Experimental results show that the distribution of CTGAN synthetic data is similar to the distribution of real data. Finally, we created a machine learning-based IDS and applied it to each dataset to validate the success rate of intrusion detection with newly generated synthetic data. It was observed that CTGAN-generated data decreased the IDS accuracy by approximately 25%, while Gaussian copula-generated data resulted in a 20% decrease, and VAE-generated data led to a 1% reduction, all relative to the accuracy achieved with real data. These findings suggest that synthetic data generation holds significant potential for enhancing the security frameworks of UAVs. The experimental results demonstrate that the CTGAN model is particularly effective in producing synthetic data that challenges traditional intrusion detection mechanisms, thereby contributing to the development of more robust IDS capabilities. © 2024 IEEE.

关键词： Generative adversarial networks

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LCEFL: A Lightweight Contribution Evaluation Approach for Federated Learning

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IEEE Transactions on Mobile Computing 2025年第07期24卷 6643-6657页

作者： Guo, Jingjing Li, Jiaxing Liu, Zhiquan Xiong, Yupeng Ma, Yong Vasilakos, Athanasios V. Li, Xinghua Ma, Jianfeng Xidian University School of Cyber Engineering Xi'an710071 China Jinan University College of Cyber Security Guangzhou510632 China Jiangxi Normal University Department of Computer Science and Technology Nanchang330022 China IAU Department of Networks and Communications College of Computer Science and Information Technology Dammam31441 Saudi Arabia Grimstad Norway

The prerequisite for implementing incentive mechanisms and reliable participant selection schemes in federated learning is to obtain the contribution of each participant. Available evaluation methods for participant contributions require the server to possess a test dataset, often impractical. Additionally, the excessively high complexity of these works is unacceptable when training complex models in large-scale federated learning system. To address these issues, we propose a lightweight contribution evaluation method for federated learning participants, named LCEFL, based on model projection theory, which does not require the server to provide a test dataset. In addition, a model compression method is designed to be used in LCEFL to reduce the computational complexity. Furthermore, a trusted aggregation method based on LCEFL is proposed, where the weight of each participant's local model is determined by its trust level, which can be calculated using its contribution evaluation result. Experimental results show that LCEFL can achieve nearly the same accuracy as schemes based on Shapley Value, while significantly reducing computational overhead by more than 50%. Compared to available aggregation methods, the proposed trusted aggregation scheme is able to accelerate the convergence speed of the global model and improve its accuracy by 2% to 45%. © 2002-2012 IEEE.

关键词： Federated Learning Computational Modeling Accuracy Servers Training Data Models Machine Learning Mobile Computing Particle Measurements Convergence Federated Learning Contribution Evaluation Trust Evaluation Model Projection Theory Secure Aggregation Federated Learning Computational Complexity Test Dataset Global Model Selection Of Participants Model Projections Aggregation Method Shapley Value Speed Of The Model Contributions Of Participants Number Of Participants Accuracy Of Model Computational Cost Local Data Learning Task Layer Model Participation Outcomes Updated Model Value Of Participation Model Aggregation Server Aggregates Global Convergence Iterative Rounds Trust Value Local Updates MNIST Dataset Total Amount Of Data Local Training Aggregation Algorithm Complex Model Structure

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Exploring the landscape of network security: a comparative analysis of attack detection strategies

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Journal of Ambient Intelligence and Humanized Computing 2024年第8期15卷 3211-3228页

作者： Rajesh Kanna, P. Santhi, P. Department of Computer Science and Engineering Bannari Amman Institute of Technology Tamil Nadu Erode India TIFAC-CORE in Cyber Security Amrita School of Engineering Amrita Vishwa Vidyapeetham Tamil Nadu Coimbatore India

The field of computer networking is experiencing rapid growth, accompanied by the swift advancement of internet tools. As a result, people are becoming more aware of the importance of network security. One of the primary concerns in ensuring security is the authority over domains, and network owners are striving to establish a common language to exchange security information and respond quickly to emerging threats. Given the increasing prevalence of various types of attacks, network security has become a significant challenge in the realm of computing. To address this, a multi-level distributed approach incorporating vulnerability identification, dimensioning, and countermeasures based on attack graphs has been developed. Implementing reconfigurable virtual systems as countermeasures significantly improves attack detection and mitigates the impact of attacks. Password-based authentication, for instance, can be susceptible to password cracking techniques, social engineering attacks, or data breaches that expose user credentials. Similarly, ensuring privacy during data transmission through encryption helps protect data from unauthorized access, but it does not guarantee the prevention of other types of attacks such as malware infiltration or insider threats. This research explores various techniques to achieve effective attack detection. Multiple research methods have been utilized and evaluated to identify the most suitable approach for network security and attack detection in the context of cloud computing. The analysis and implementation of diverse research studies demonstrate that the based signature intrusion detection method outperforms others in terms of precision, recall, F-measure, accuracy, reliability, and time complexity. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Network security

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The Role of AI in Vehicle-to-Infrastructure Communication for Tolling Systems

The Role of AI in Vehicle-to-Infrastructure Communication fo...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Mahalakshmi, S. Baskaran, Nithiya Partheeban, Pachaivanna Krishnara, Kowsalyadevi Arjun, S. Chennai Institute of Technology Department of Computer Science and Engineering Kundrathur Chennai India Chennai Institute of Technology Department Cyber Security Kundrathur Chennai India Chennai Institute of Technology Department Civil EngDepartment Cyber Security Kundrathur Chennai India Sona Institute of Technology Department of Cyber Security Salem India

ISBN: (纸本)9798331535476

The number of vehicles people use is substantial;thus, surveillance must be accurate. License plates are detected and recognized by detecting static and real-time images captured by the camera using OCR technology, computer vision, and AI in the tool booth and other important roads. This model can be used for many things, such as automated services, increased security, determining whether a car is registered, and identifying and validating vehicles connected to traffic violations. Real-time investigations are part of the automated recognition of license plates and their application to other traffic control and highway surveillance aspects. To detect plates from the images and in real-time video, Use Easy OCR with the license plates to read the plate number, utilize tensor flow object detection to detect and recognize, and save the detected license plate for further analysis and search. © 2024 IEEE.

关键词： Highway traffic control

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Electromagnetic Radiation Effects in Human and Animal Health 14

Electromagnetic Radiation Effects in Human and Animal Health

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Singh, Jagjot Kumar, Sanu Johnson, Derick Mathew Savita Department of Computer Science Engineering Spl. Digital Forensics and Cyber Security India

Electromagnetic (EM) radiations are the waves of electromagnetic and magnetic field which are generated from electromagnetic devices. These radiations are dangerous to humans as well as animal health. Talking medically, in the human and animal body, these bioelectric signals cause different processes to occur. Exposing to larger EMradiations can cause disrupt in the natural work of the human body. These harmful radiations harm the body at the cellular level. According to the World Health Organization (WHO), the EMF radiations released by mobile phones and otherdevices are hazardous to humans. The objective of this review paper is to carry out a survey to know about the usage of mobile phones and laptops by people and will aware about the harmful impact of them. © Grenze Scientific Society, 2023.

关键词： Cellular telephones

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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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Leveraging OpenStack-based Private Cloud for Intrusion Detection Using Deep Learning Techniques 1

Leveraging OpenStack-based Private Cloud for Intrusion Detec...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Roshan, Rahul G. Salanke, Vishal S Nagasundari, S. Research Center for Information Security Forensics and Cyber Resilience PES University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331518943

OpenStack is a cloud platform that offers infrastructure services, providing access to computing, storage, and network resources. However, due to its open-source nature, the cloud environment can be vulnerable to security threats. OpenStack security measures are generally applied to specific services, rather than being integrated comprehensively across the entire cloud environment. Despite offering flexibility and scalability, the widespread use of OpenStack calls for robust security solutions. Current intrusion detection systems often struggle to adapt to constantly evolving traffic patterns and attack vectors in the OpenStack environment. This study introduces a two-stage deep learning framework to create a robust Intrusion Detection System (IDS) for OpenStack-based private clouds. The proposed framework efficiently analyzes network traffic data in these clouds by implementing dimensionality reduction and anomaly detection techniques. It utilizes a network-based IDS, trained on two publicly available datasets, which enables a broader detection range of known attack patterns. The results indicate that the OpenStack IDS significantly enhances OpenStack's security, addressing the limitations of generic IDS solutions. © 2024 IEEE.

关键词： Network intrusion

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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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TU-IR Apple Image Dataset: Benchmarking, Challenges, and Asymmetric Characterization for Bruise Detection in Application of Automatic Harvesting

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第1期2卷 105-124页

作者： Das, Dipak Hrishi Roy, Sourav Dey Saha, Priya Bhowmik, Mrinal Kanti Government Degree College Department of Information Technology Santirbazar 799144 India Department of Computer Science and Engineering Suryamaninagar 799022 India Department of Cyber Security and Digital Forensics Agartala799001 India

With the blooming interest in computer vision-based technologies for future automation of food producers, there is a need for incorporating an automatic bruise detection module in robotic apple harvesting because of decreasing accessibility and growing labor costs. Although numerous studies have been published for automatic quality inspection of fruit and other agricultural products, there is a lack of publicly available image-based datasets for quality inspection/automatic detection of bruises. Toward the aim of developing a bruise detection system for apple harvesting, especially at night time, this article describes the designing issues (i.e., protocol) and creation of a new infrared imaging-based dataset titled 'TU-IR Apple Image Dataset,' which contains 1375 infrared images of apples defining four major categories of bruises (i.e., fresh, slight, moderate, and severe). Along with the infrared images, ground truths (in the form of binary masks) and measurements of suspicious bruised regions are defined. This study also investigates the efficiency of infrared imaging technology for automatic bruise detection in apples by performing an analysis of temperature-based, intensity-based, texture-based, shape-based, and deep convolutional neural network-based features. The classification performance was evaluated using eight different feature sets. Based on the experimental results, considering the most outer-performed classifier, deep convolutional neural networks as a fixed feature extraction method were found to provide the highest prediction performance for discriminating between fresh and three categories of bruises in apples with an average accuracy, specificity, and sensitivity of 93.87%, 80.57%, and 92.02%, respectively. © 2023 IEEE.

关键词： Feature extraction

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