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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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Anomaly-Based Detection of Log4j Attack in SDN

Anomaly-Based Detection of Log4j Attack in SDN

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Deshmukh, Pruthviraj Nitin Harish, R. Sangeetha, V. Praveen, K. TIFAC-CORE in Cyber Security Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Department of Computer Science and Information Systems BITS Pilani India

ISBN: (纸本)9789819613472

The widely used Java library Log4j logging framework has lately come under attack from a major vulnerability known as CVE-2021–44228, also known as Log4Shell. Through the use of malicious log messages, the vulnerability enables attackers to compromise vulnerable systems by executing remote code. A key component of SDN systems is the RYU controller, an open-source software-defined networking (SDN) controller that provides a wide range of functionalities. It can communicate and interact with SDN-capable switches and other network elements. It is used in SDN installations for managing, controlling, and supporting network automation and orchestration. This paper proposes a new anomaly-based Log4j detection approach in SDN networks. We have used multiple machine-learning classifiers to analyze and detect anomalies in network traffic generated by Log4j attacks to get the best achievable accuracy for real-time model development. The conducted experiment results that the proposed approach can give high accuracy up to 98% in detecting Log4j attack in SDN environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： E-learning

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Ensemble Method for Network IDS Using the B-DRF Algorithm 5

Ensemble Method for Network IDS Using the B-DRF Algorithm

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Vinitha, A. Dr.N. G.P Arts and Science College Department of Computer Science with Cyber Security Coimbatore India

ISBN: (纸本)9798350379945

Intrusion detection systems (IDS) are crucial for safeguarding against the increasing cyber threats. This research study proposes an ensemble-based machine learning approach to effectively detect anomalies in network traffic. By employing bootstrap aggregation, random forest, and randomization techniques on the LUflow dataset, the proposed model achieves a high accuracy of 99% in identifying intrusions. This robust IDS can provide timely alerts to administrators, enabling proactive security measures. © 2024 IEEE.

关键词： Network intrusion

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Literature Review on Packet Sniffing: Essential for cybersecurity & Network security 5

Literature Review on Packet Sniffing: Essential for Cybersec...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Shaw, Ranju Parveen, Suraiya Jamia Hamdard University Dept. of Computer Science Engineering with Specialization in Cyber Forensic Information Security Delhi India Faculty at Jamia Hamdard University Sest Department of Computer Science Engineering Delhi India

ISBN: (纸本)9798350385649

In the realm of cybersecurity and network security, the significance of monitoring and analyzing network traffic cannot be overstated Network packet sniffing, a technique employed to capture and inspect data packets traversing a network, has emerged as an imperative tool for understanding network behavior, identifying vulnerabilities, and enhancing overall security. This study analyzes packet sniffing by exploring its role in modern cybersecurity practices. By analyzing packet sniffing mechanisms, including its methodologies and potential applications, this res earch sheds light on its effectiveness as a proactive security solution. Furthermore, the study examines the ethical considerations surrounding packet sniffing, addressing concerns related to user privacy and data protection. It also investigates the legal aspects that govern the use of packet sniffing techniques, ensuring compliance with regulations while maximizing its benefits. Through case studies and real-world examples, this research illustrates how packet sniffing can be leveraged to detect network anomalies, thwart malicious activities, and fortify network infrastructures. By presenting a comprehensive overview of packet sniffing's capabilities and limitations, this study equips cybersecurity practitioners with the knowledge needed to make informed decisions about its integration into their defensive strategies. © 2024 IEEE.

关键词： cybersecurity

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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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Multi Disease Prediction Related to Pulmonary Area by Leveraging Deep Learning

Multi Disease Prediction Related to Pulmonary Area by Levera...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Tidke, Vedanti Zade, Sharayu Bhimarapu, Harshita Agrawal, Rahul Chavhan Morris, Nekita Dhule, Chetan Cyber Security G H Raisoni College of Engineering Department of Data Science IOT Nagpur440016 India

ISBN: (纸本)9798331512965

A large portion of the world's healthcare issues are related to conditions that impact the chest, heart, lungs, and respiratory system, which affect millions of people annually. To address this issue, we present the Deep learning-based model, which will detect multi diseases related to pulmonary condition. Multi pulmonary areas disease can be detected if the disease is present in the dataset of X- rays and CT Scans photos. This research shows the count of diseases out of the whole dataset taken. Its motive is to predict multi diseases related to the pulmonary area. This research will help the radiologists to make a quick diagnosis of pulmonary disease conditions. Ultimately, it will help the patients to have an earlier idea about disease to take treatment on time. © 2025 IEEE.

关键词： Lung cancer

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A Survey of Machine Learning Applications in Medical Imaging for Neurodegenerative Disease Diagnosis 2

A Survey of Machine Learning Applications in Medical Imaging...

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2nd International Conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Nandhashree, K.R. Senthil Kumar, M. Susee, S.K. Department of Cyber Security SRM Valliammai Engineering College India Department of Computer Science and Engineering SRM Valliammai Engineering College India Department of Electronics and Communication Mohamed Sathak A J College of Engineering India

ISBN: (数字)9781837242535

ISBN: (纸本)9781837242672

The increasing prevalence of brain degenerative disorders particularly Alzheimer's and Parkinson's, have incurred a dire need for effective diagnostic methodologies. With advancements in medical imaging techniques and Artificial Intelligence (AI), transformative potential exists to enhance early detection and disease classification. This review examines the recent progress made in the use of diagnostic medical imaging and artificial intelligence (AI) techniques for the early detection of Alzheimer's disease and Parkinson's disease. Despite significant progress, a comprehensive overview is lacking. The review aims to summarize and synthesize existing methods, critically evaluate their strengths and limitations, and identify emerging trends. Multimodal imaging data, combining information from different sources like MRI, PET, and SPECT, has shown progress in improving diagnostic accuracy and providing insights into disease progression. Advanced AI techniques, such as transfer learning and generative adversarial networks, have further enhanced the performance of diagnostic models. Previous studies have predominantly focused on using standalone techniques, often leaving the integration of multi-modal imaging and deep learning methodologies that could lead to more robust diagnostic frameworks. This literature review addresses this gap by synthesizing current research on medical imaging modalities, AI applications, and their implications in diagnosing Alzheimer's and Parkinson's diseases. This review seeks to connect technological innovations with clinical applications to establish more robust and dependable diagnostic methods. © The Institution of engineering & Technology 2024.

关键词： Generative adversarial networks

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Survey on digital twins for Internet of Vehicles:Fundamentals,challenges,and opportunities

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Digital Communications and Networks 2024年第2期10卷 237-247页

作者： Jiajie Guo Muhammad Bilal Yuying Qiu Cheng Qian Xiaolong Xu Kim-Kwang Raymond Choo School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Department of Computer Engineering Hankuk University of Foreign StudiesGyeonggi-do17035South Korea Jiangsu Hydraulic Research Institute Nanjing210044China Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

As autonomous vehicles and the other supporting infrastructures(e.g.,smart cities and intelligent transportation systems)become more commonplace,the Internet of Vehicles(IoV)is getting increasingly *** have been attempts to utilize Digital Twins(DTs)to facilitate the design,evaluation,and deployment of IoV-based systems,for example by supporting high-fidelity modeling,real-time monitoring,and advanced predictive ***,the literature review undertaken in this paper suggests that integrating DTs into IoV-based system design and deployment remains an understudied *** addition,this paper explains how DTs can benefit IoV system designers and implementers,as well as describes several challenges and opportunities for future researchers.

关键词： Internet of vehicles Digital twin Simulation Traffic systems

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Smart Water-Powered Renewable Energy System with IoT Integration

Smart Water-Powered Renewable Energy System with IoT Integra...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Ibraheem, Ahmed Salman Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq Department of Computer Technical Engineering Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

An advanced hybrid renewable energy collecting and storage system prototype that utilizes water flow as its primary power source. Additionally, the system incorporates an energy monitoring system based on the Internet of Things (IoT) technology. The device effectively harnesses energy from the movement of water, storing it for future use in low-voltage applications. It demonstrates a maximum daily energy capture capacity of about 100Wh. The energy monitoring system, based on the Internet of Things (IoT), enables the real-time and remote monitoring of energy harvesting and usage. We thoroughly assessed the energy harvesting component across different situations to identify the crucial parameters that impact energy output. The results of our study demonstrate that several elements, including the spatial separation 'between the energy harvester and the water source, Water flow' speed, sensor orientation, and sensor density, all play significant roles in determining the quantity of energy collected. This research shows that it is possible to integrate IoT technology with hydropower renewable energy systems. It provides significant perspectives on enhancing energy extraction efficiency in practical scenarios. © 2024 IEEE.

关键词： harvesting renewable energy IoT storage energy water flow energy

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Improving VANET Localization Performance with Data Verification: Challenges and Solutions

Improving VANET Localization Performance with Data Verificat...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Kadhim, Aqeel Kamil Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisa Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

In VANETs, the important and effective applications of vehicle localization include safety and communication applications. Thus, it is difficult to get very accurate localization in the dynamic and often very fluctuating environment of VANET. This research responds to this challenge with a keen thrust on improving vehicle localization using the Mobility Data Verification process. The primary goal of this work is to achieve high accuracy of vehicle positioning within VANET by checking the coherence of mobility data of moving vehicles. Hence, we present a concept that builds upon Mobility Data Verification to provide real-time location details from vehicles. Both GPS and onboard sensors feed this method, which uses a multi-level verification to flag out or eliminate the errors of deliberately spoofed data points. We use simulations to evaluate and validate the effectiveness of the proposed verification approach. As the proposed system, our previously developed and fine-tuned weighted localization (OWL) strategy does so. The performance evaluation results suggest that our proposed solution is feasible to distinguish automobiles that are not trustworthy and improve the performance of the localization algorithm. © 2024 IEEE.

关键词： Vane pumps

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