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Data Aegis Using Chebyshev Chaotic Map-Based Key Authentication Protocol 1

International Conference on Intelligent Manufacturing and Energy Sustainability, ICIMES 2022

作者： Lateef, Mohammed Abdul Atheeq, C. Rahman, Mohd Abdul Faizan, Mohammed Abdul Deccan College of Engineering and Technology Hyderabad India Computer Science Deccan College of Engineering and Technology Hyderabad India Hyderabad India Cyber Forensics and Information Security Jawaharlal Nehru Technological University Hyderabad Hyderabad India

ISBN: (数字)9789811984976

ISBN: (纸本)9789811984969

Networks with wireless connection between nodes are far more convenient than a wired connection and are under rapid development. One major factor that ensures a truly significant network is the security features it possesses. In traditional communication, authentication mechanisms based on ECC were used to additionally augment the trustworthiness of data security over the network. In order to enhance security, the primary focus remained on growing the key size but, this brings in the intricacy to many internal load operations that are performed in latter stages and to the workload of calculations being performed. In addition, the speed of the cryptographic processes could not be compromised. Hence, to make the process smoother and reliable, our C2MAP protocol is introduced. Particularly, Chebyshev chaotic maps are applied to relate association indulging in the mechanisms involved that are required to perform mutual authentication. The proposed protocol is based on one of the properties of the Chebyshev polynomial, i.e., the semi-group property;which differs from other traditional security strategies, by those implementing complex hash functions and corresponding cryptographic algorithms. In order to compete with the existing ECC mechanism;parameters like throughput, end-to-end delay (E2D), and packet delivery ratio are taken into account;whose corresponding calculated scrutiny would prove that the enhanced security in authentication for communication protocol would be a better choice;by visualizing them through graphical presentations. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Chebyshev polynomials

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Deep Learning based Intelligent System for Skin Disease Detection 10

Deep Learning based Intelligent System for Skin Disease Dete...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Dhanush, K.B. Arul Xavier, V.M. Shyni, S.S. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Tamilnadu Coimbatore India Karunya Institute of Technology and Sciences Division of Data Science and Cyber Security Tamilnadu Coimbatore India

ISBN: (纸本)9798350353068

Malignant and benign skin diseases significantly impact public health and personal well-being. Traditional machine learning models encounter difficulties in processing image datasets, which is crucial for early and accurate diagnosis. This work employed two pre-trained deep learning models, ResNet50 and VGG19, known for their precise and swift image processing capabilities. These models underwent training utilizing an extensive dataset of images from patients battling benign keratosis, Melanoma, and Melanocytic nevus (Mole). The models were then tasked to distinguish between these three conditions. The results were remarkable, with the models achieving accuracies of 91.3 % and 95.6 %, respectively. A comparative analysis revealed that the VGG19 model outperformed in accurately identifying these three diseases, surpassing traditional AI and machine learning methods. The trained VGG19 model was subsequently incorporated into an Android application to enhance user experience. © 2024 IEEE.

关键词： Diagnosis

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Task-level Distributionally Robust Optimization for Large Language Model-based Dense Retrieval 39

Task-level Distributionally Robust Optimization for Large La...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Ma, Guangyuan Ma, Yongliang Wu, Xing Su, Zhenpeng Zhou, Ming Hu, Songlin Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Langboat Technology Beijing China

ISBN: (纸本)157735897X

Large Language Model-based Dense Retrieval (LLM-DR) optimizes over numerous heterogeneous fine-tuning collections from different domains. However, the discussion about its training data distribution is still minimal. Previous studies rely on empirically assigned dataset choices or sampling ratios, which inevitably lead to sub-optimal retrieval performances. In this paper, we propose a new task-level Distributionally Robust Optimization (tDRO) algorithm for LLM-DR fine-tuning, targeted at improving the universal domain generalization ability by end-to-end reweighting the data distribution of each task. The tDRO parameterizes the domain weights and updates them with scaled domain gradients. The optimized weights are then transferred to the LLM-DR fine-tuning to train more robust retrievers. Experiments show optimal improvements in large-scale retrieval benchmarks and reduce up to 30% dataset usage after applying our optimization algorithm with a series of different-sized LLM-DR models. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Search engines

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Blockchain-Enabled DNS: Enhancing security and Mitigating Attacks in Domain Name Systems 6

Blockchain-Enabled DNS: Enhancing Security and Mitigating At...

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6th International Conference on Signal Processing and Information security, ICSPIS 2023

作者： Shah, Kaushal Padhya, Mukti Sharma, Sachin Pandit DeenDayal Energy University School of Technology Department of Computer Science and Engineering Gandhinagar India National Forensic Sciences University School of Cyber Security and Digital Forensics Gandhinagar India

ISBN: (纸本)9798350329599

The importance of Internet security is increasing as businesses expand through digitalization. Like in the physical world, data transmitted over the Internet must be safeguarded to ensure confidentiality, integrity, and availability. Among the various security threats, Distributed Denial-of-Service (DDoS) attacks are widely recognized as they can potentially compromise all three aspects of data security. Despite several attempts to address these attacks, the traditional Domain Name System (DNS) has struggled to counter them effectively. In this research, we propose the concept of a Blockchain-based DNS as a solution to mitigate DNS-related threats, particularly DDoS attacks. The suggested architecture encompasses all levels of the DNS, including registrars, intermediaries (if any), and clients. By leveraging the decentralized nature of blockchain technology, this approach provides a fundamental defense against DDoS attacks, as it eliminates the vulnerability of a single point of failure. Furthermore, this work explores the appropriate type of blockchain to be used, estimates the cost of implementing such a service, and offers flexibility for organizations to adopt this solution at their discretion. © 2023 IEEE.

关键词： Blockchain

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Oral Cancer Using Deep Learning and Auto-Fluorescence Image Analysis

Oral Cancer Using Deep Learning and Auto-Fluorescence Image ...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Muhammed Yaseer, P. Arul Xavier, V.M. Shyni, S.S. Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Tamilnadu Coimbatore India Karunya Institute Of Technology And Sciences Division Of Data Science And Cyber Security Tamilnadu Coimbatore India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371567

Poor treatment outcomes result from the fact that oral cancer is frequently identified at an advanced stage, making it a serious and sometimes lethal disease. Thus, it is essential to develop quick and accurate procedures for detecting oral cancer in its early stages. In this work, we present a unique non-invasive detection technique for evaluating auto-fluorescence signals in oral tissues utilizing convolutional neural networks ResNet50 architecture. Biological tissues naturally produce auto-fluorescence signals, and variations in these signals may be a marker of the existence of malignant cells. Our approach seeks to precisely identify malignant and normal oral tissues by capturing these minute variations in auto-fluorescence patterns. Using a sizable dataset of pictures of oral tissue, we trained and evaluated our RestNet50 model, and the results showed impressive accuracy rates of 89%. The better performance of our detection method can be attributed to the effective capture of high-level features made possible by the ResNet50 architecture. Furthermore, the suggested approach demonstrated quick processing speeds, which allowed it to be used for real-time clinical applications. This approach may greatly enhance patient prognosis and treatment results by identifying oral cancer in its early stages with high accuracy of 89% when compared to VGG16 and VGG19. Although more validation research is necessary before the suggested approach can be used in clinical settings, it shows promise in facilitating early diagnosis and intervention to enhance patient outcomes. © 2024 IEEE.

关键词： Fluorescence

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forensics Analysis of TOR Browser

Forensics Analysis of TOR Browser

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International Conference on Information security, Privacy, and Digital forensics, 2022

作者： Kumar, Adarsh Sondarva, Kumar Gohil, Bhavesh N. Patel, Sankita J. Shah, Ramya Rajvansh, Sarang Sanghvi, H.P. Computer Engineering Department Sardar Vallabhbhai National Institute of Technology Gujarat Surat395-007 India School of Cyber Security and Digital Forensics National Forensic Sciences University Gujarat Gandhinagar382007 India Directorate of Forensic Science Gujarat Gandhinagar India

ISBN: (纸本)9789819950904

The Onion Router is a web browser that uses the Tor network to anonymize web traffic by making it simple to conceal one’s identity on social media. It uses the onion routing technology to access the multiple-level encrypted, Internet-impossible private mode. These features are being abused to engage in a variety of criminal activities, including cyber terrorism and the black market. The TOR erases all browsing history and other network traces, making it impossible for investigators to gather evidence. This study extracts and examines any potential artifacts that the TOR browser may have produced in local system files and memory dumps. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Routing protocols

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Detecting compromised email accounts via login behavior characterization

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cybersecurity 2024年第1期7卷 16-36页

作者： Jianjun Zhao Can Yang Di Wu Yaqin Cao Yuling Liu Xiang Cui Qixu Liu Institute of Information Engineering Chinese Academy of SciencesBeijing100085China School of Cyber Security University of Chinese Academy of SciencesBeijing100049China China Cybersecurity Review Technology and Certification Center Beijing100013China Zhongguancun Laboratory Beijing100089China

The illegal use of compromised email accounts by adversaries can have severe consequences for enterprises and *** compromised email accounts is more challenging than in the social network field,where email accounts have only a few interaction events(sending and receiving).To address the issue of insufficient features,we propose a novel approach to detecting compromised accounts by combining time zone differences and alternate logins to identify abnormal *** on this approach,we propose a compromised email account detection framework that relies on widely available and less sensitive login logs and does not require *** framework characterizes login behaviors to identify logins that do not belong to the account owner and outputs a list of account-subnet pairs ranked by their likelihood of having abnormal login *** approach reduces the number of account-subnet pairs that need to be investigated and provides a reference for investigation *** evaluation demonstrates that our method can detect most email accounts that have been accessed by disclosed malicious IP addresses and outperforms similar ***,our framework has the capability to uncover undisclosed malicious IP addresses.

关键词： Compromised account detection Mixture model Login log analysis Attribution and forensic

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Bridging the Gap: Aligning Language Model Generation with Structured Information Extraction via Controllable State Transition 25

Bridging the Gap: Aligning Language Model Generation with St...

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34th ACM Web Conference, WWW 2025

作者： Li, Hao Ren, Yubing Cao, Yanan Li, Yingjie Fang, Fang Lin, Zheng Wang, Shi Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400712746

Large language models (LLMs) achieve superior performance in generative tasks. However, due to the natural gap between language model generation and structured information extraction in three dimensions: task type, output format, and modeling granularity, they often fall short in structured information extraction, a crucial capability for effective data utilization on the web. In this paper, we define the generation process of the language model as the controllable state transition, aligning the generation and extraction processes to ensure the integrity of the output structure and adapt to the goals of the information extraction task. Furthermore, we propose the Structure2Text decider to help the language model understand the fine-grained extraction information, which converts the structured output into natural language and makes state decisions, thereby focusing on the task-specific information kernels, and alleviating language model hallucinations and incorrect content generation. We conduct extensive experiments and detailed analyses on myriad information extraction tasks, including named entity recognition, relation extraction, and event argument extraction. Our method not only achieves significant performance improvements but also considerably enhances the model’s capability to generate precise and relevant content, making the extracted content easy to parse. © 2025 Copyright held by the owner/author(s).

关键词： Image retrieval

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Breast Cancer Detection using Domain-Adversarial Training (DANN) with Invariant Risk Minimization (IRM) Hybrid Approach 3

Breast Cancer Detection using Domain-Adversarial Training (D...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Akash, K.V. Daniel, Esther Seetha, S. Durga, S. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Cmr Institute of Technology Department of Information Science & Engineering India Amrita Vishwa Vidyapeetham Tifac Core in Cyber Security Amrita School of Engineering India

ISBN: (纸本)9798331509675

The breast cancer detection performs a key function in the health care network. The precise and early detection of cancer in the breast could aid to save life of the sufferer. The traditional machine learning methods struggle due to the data used to train is different from data used later this is known as Domain shifts. This project uses a hybrid model known as Domain-Adversarial Training of Neural Networks (DANN) with Invariant Risk Minimization (IRM) Hybrid Method to identification of the tumor in the breast. Through integrating the (DANN) and IRM works perfectly among the various type of data and also with the various image from the various sources. This model is more precise and reliable than any other older *** that this model act as an effective tool for detecting the breast cancer in the early stages. © 2025 IEEE.

关键词： Adversarial machine learning

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Reinforcement Learning-based security Enhancement for Controlled Optimization of Phases in Intelligent Traffic Signal System

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial cyber-Physical Systems 2024年 2卷 575-587页

作者： Qiao, Ziyan Xiang, Yingxiao Baker, Thar Li, Gang Wu, Yalun Tong, Endong Peng, Shuanghe Zhu, Ye Xu, Dongwei Niu, Wenjia Beijing Jiaotong University Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing China Chinese Academy of Sciences Institute of Information Engineering Beijing China University of Brighton School of Architecture Technology and Engineering Brighton United Kingdom Deakin University Centre for Cyber Security Research and Innovation Geelong Australia Deakin University Australia Tangshan Research Institute of Beijing Jiaotong University Tangshan063000 China Deakin University Centre for Cyber Resilience and Trust Burwood3125 Australia Zhejiang University of Technology College of Information Engineering Institute of Cyberspace Security Hangzhou311121 China

With the rise of intelligent devices within Industrial cyber-Physical Systems (ICPS), encompassing applications in traffic signal control, the vulnerability of devices such as On-Board Units and Roadside Units to attacks of data spoofing becomes a critical issue. Congestion attacks pose a significant threat to traffic security, capable of causing traffic jams by manipulating traffic signal control methods on the Internet of Vehicles. One such method is the Controlled Optimization of Phases algorithm, which is susceptible to congestion attacks. In this paper, we propose a security enhancement approach integrating advantage actor-critic reinforcement learning with a long short-term memory network. Our work extends the application of security enhancement methodologies to the context of intelligent traffic systems, that synchronously detects and disposes of attacks, ensuring the physical safety and reliable operation of ICPS. The experimental results and analysis exhibit the efficiency of our approach in terms of processing time and effectiveness. © 2023 IEEE.

关键词： Traffic congestion

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