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Enhancing Cybersecurity in Medical Cyber-Physical Systems Using Blockchain and Deep Learning

Enhancing Cybersecurity in Medical Cyber-Physical Systems Us...

引用

2025 IEEE International Students' Conference on Electrical, Electronics and computer sci.nce, SCEECS 2025

作者： Pimple, Jagdish Sharma, Avinash Madhyanchal Professional University Department of Computer Sci. & Engineering Bhopal India

ISBN: (纸本)9798331529833

Medical Cyber-Physical Systems (MCPS) are essential in modern healthcare, enabling real-time patient monitoring, diagnosis, and treatment. However, their integration of digital and physical elements exposes them to cyber-attacks, threatening both patient safety and data integrity. This paper shows a novel cybersecurity framework that combines blockchain technology and deep learning-based intrusion detection to protect MCPS. The proposed model uses a deep DNN-based intrusion detection system (IDS) to categorize medical data as either normal or malicious. Blockchain technology is incorporated to ensure secure, tamper-proof, and decentralized storage of patient information. To improve detection accuracy and reduce false positives, feature fusion techniques, processed through convolutional neural networks (CNNs), are applied. Ethereum-based blockchain ensures that medical data remains confidential and resistant to unauthorized alterations. The system's effectiveness is validated using a healthcare dataset, showing a marked improvement in detection accuracy, precision, and recall over traditional methods. This integrated solution strengthens the safety and reliability of MCPS, protecting patient data while maintaining efficient data shared between medical devices and healthcare networks. Future research will focus on real-time applications and scaling the model for larger healthcare systems. © 2025 IEEE.

关键词： Cybersecurity

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Enhanced Reversible Data Hiding in Encrypted Images by Pixel Shifting 16

Enhanced Reversible Data Hiding in Encrypted Images by Pixel...

引用

2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Yang, Ching-Nung Lu, Yu-Quan Liu, Shu-Yu Xiong, Lizhi Dept. of Computer Sci. and Inf. Engineering National Dong Hwa University Hualien Taiwan School of Computer and Software Nanjing University of Inf. Sci. & Technology Nanjing China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Reversible data hiding (RDH) is one special type of data hiding, and is widely used for many intended applications. Moreover, to protect the cover image, RDH in encrypted image (RDHEI) is accordingly proposed. With the reversibility and hiding data in encrypted image, RDHEI has potential utility in fields such as military and medical image transmission, and forensic analysis. Recently, Panchikkil et al. propose a RDHEI by using pixel swapping (RDHE-PSW) to swap some pixels in a block. The pixel swapping does not change pixel values, and thus Panchikkil et al.’s RDHEI-PSW can retain entropy and histogram. The smoothness of a block is calculated to extract the hidden bits and meantime recover the cover image. Panchikkil et al.’s RDHEI-PSW only hides two secret bits in every block. Recently, Kim et al. use pixel shifting (PSH), a circular right shift on a block, instead of pixel swapping to propose a RDHEI-PSH. In this paper, we still adopt PSH approach but use it in a different way (a modified PSH) to propose an enhanced RDHEI-PSH with a higher embedding capacity than Kim et al.’s RDHEI-PSH. Our enhanced RDHEI-PSH double the patterns on a block, on which we can embed more secret bits. Experimental results demonstrate that our enhanced RDHEI-PSH surpasses Panchikkil et al.’s RDHEI-PSW and Kim et al.’s RDHEI-PSH. © 2025 SPIE.

关键词： Military photography

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Fake Review Detection using Random Forest Classifier

Fake Review Detection using Random Forest Classifier

引用

2025 IEEE International Students' Conference on Electrical, Electronics and computer sci.nce, SCEECS 2025

作者： Mane, Kishor Dongre, Snehlata Madankar, Mangala G H Raisoni College of Engineering Department of Computer Sci. & Engg. Nagpur India

ISBN: (纸本)9798331529833

In recent years, online review plays crucial role in the modern consumer experience to provide the valuable insights into the performance and quality of the products and services. Most of the consumers take a decision about purchase of online product based on the reviews. In the view of customer all the reviews are genuine. But nowadays many of the businesses manipulated the reviews of their products by inserting the fake reviews to misleads the consumer to buy wrong product. There is a need of the effective mechanism which can identify the fake reviews and increases the trustworthiness of the online reviews. Many researchers working on the different techniques available in different domains such as Machine learning, NLP, Opinion Mining, etc. In this paper the fake reviews are identified using the random forest classifier along with different challenges identified for improvement. The amazon reviews dataset has been tested for 90000 reviews to achieves accuracy up to 79.47%. © 2025 IEEE.

关键词： Adversarial machine learning

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Hybridization of DNA Computing and Chaotic System for Ensuring the Randomness and Integrity of Image Data 4

Hybridization of DNA Computing and Chaotic System for Ensuri...

引用

4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Dhakal, Madhav Shakya, Subarna Tribhuvan University Central Department of Computer Sci. & Information Technology Kathmandu Nepal Pulchowk Campus Tribhuvan University Department of Electronics and Computer Engineering Institute of Engineering Kathmandu Nepal

ISBN: (纸本)9798331523923

In this paper, the concept of DNA computing and 3-D chaotic system is implemented to maintain the security of grayscale image while transmitting through the publicly available network. Here, random selection technique for sequencing of DNA bases with secret key generated through chaotic sequence encryption technique is purposed. Generation of permutation box for diffusion of image pixels carried out on the basis of chaotic system, which create more randomness and difficult to retrieve the secret key and maintain confidentiality from the intruder. System parameter and initial values of chaotic system work as the private key. In this proposed concept, first, plain image is divided in to various block of 512 bit size and padding is necessary if the last block does not contain the block of 512 sizes and then data obtained from the blocks are XOR with the key value obtained through chaotic system. After that, results obtained from this operation are transformed in to the cipher image with using the concept of Central Dogma of Molecular Biology. Experimental result was thoroughly examined using various techniques including histogram analysis, chi-square test, information entropy assessment, differential attack and the result shows that the proposed concept is secure and maintain the randomness, confidentiality and integrity of image data. © 2025 IEEE.

关键词： Gene encoding

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Hindi Fake News Detection using Stacking Ensemble Method 14

Hindi Fake News Detection using Stacking Ensemble Method

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Nandgaonkar, Sushama S. Mane, Sunil B. Coep Technological University Pune Department of Computer Sci. & Engineering India

ISBN: (纸本)9798350335095

Day by day, usage of social media is increasing amongst the users giving rise to certain issues which need attention. Since information can be shared across social media very quickly, it has become a popular medium of information sharing. Users use these platforms to share information to individuals, groups and to communities. As information is spread across social media platforms very quickly, misinformation also gets spread very quickly. Detecting fake contents is very important at the initial stage so that spread of fake information can be prevented. In existing research most of the research carried out is for English news. Very few researchers have attempted to develop a model for other languages. We present a machine learning solution for fake information identification for Hindi News articles. We carried out experiments on three datasets using various machine learning approaches such as Support Vector Machine, Logistic Regression, Decision Tree, Multinomial NB and Random Forest, these learners are used individually, and their performance is evaluated. Then we use these learners in stacking ensemble method and evaluated the performance. To extract features from news TF/IDF is used. Results show that RF performed well over all datasets as individual learner. Ensemble learning resulted in significant improvement in the performance over individual learners in all performance evaluation metrics. In some cases, results of LR are closer to that of RF. © 2023 IEEE.

关键词： Decision trees

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Enhancing Ddos Attack Detection and Prevention in Mobile IP Networks Using HCIF 5

Enhancing Ddos Attack Detection and Prevention in Mobile IP ...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Shirke, Sonali Bharat Jaleel, Uruj Kadam, Sandeep Junnarkar, Aparna Atul Kalinga University Department of Computer Sci. and Engg India Keystone School of Engineering Department of Computer Engineering India Vishwakarma Institute of Information Technology Department of Information Technology India

ISBN: (纸本)9798350376685

This research suggests a Hybrid Cache-based Intelligent Framework (HCIF) to help mobile IP networks better identify and mitigate DDoS attacks. To improve network security and QoS, the HCIF technique integrates source monitoring, attack detection, and verification based on Turing tests. We evaluate HCIF and the state-of-the-art RBPBoost approach in NS-3 simulations, taking into account different mobility situations and attacker numbers. Throughput, packet delivery ratio, and packet loss ratio are all areas where HCIF excels compared to RBPBoost, while delay performance is still an issue. © 2024 IEEE.

关键词： Internet protocols

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software Product Line Testing based on Event Sequence Graphs with Feature Expressions 8

Software Product Line Testing based on Event Sequence Graphs...

引用

8th International Conference on computer sci.nce and engineering, UBMK 2023

作者： Kaya, Dilek Ozturk Tuglular, Tugkan Belli, Fevzi Izmir Institute of Technology Department of Computer Engineering Izmir Turkey Paderborn University Faculty of Computer Sci. Paderborn Germany

ISBN: (纸本)9798350340815

software Product Line testing is by its nature challenging, especially due to the exponential rise in the number of assets that need to be verified. Scalability and efficient verification, two challenges that model-based SPL testing must deal with, are discussed in this paper. An approach to automatically obtaining test suites for software product lines is proposed as a solution to these challenges. By exploiting Event Sequence Graphs with Feature Expressions, which concisely depict the software Product Line behavior, the proposed approach automatically generates test sequences for different product configurations. The presented approach is applied to the illustrative case studies from the literature. © 2023 IEEE.

关键词： software testing

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Channel Attention HalfU-Net for Skin Lesion Segmentation 1

Channel Attention HalfU-Net for Skin Lesion Segmentation

引用

1st International Conference on Advanced engineering and Technologies, ICONNIC 2023

作者： Prasetyo, Heri Mahdy, Abid Ammar Nadhif, Abrar Dwi Fairuz Guo, Jing-Ming Hsia, Chih-Hsien Department of Informatics Surakarta Indonesia National Taiwan Univ. Sci. Technolgy Department of Electrical Engineering Taipei Taiwan National Ilan University Dept. Computer Sci. Infor. Engineering Yilan Taiwan

ISBN: (纸本)9798350306484

U-shaped architecture have gain popularity in the image segmentation task, especially for biomedical image. Regardless of its good performance, U-shaped architecture generally has huge number of parameters. Considering the resource constraint in actual medical environment, the needs lightweight model is essential. In order to overcome this problem, we propose Channel Attention HalfU-Net. The proposed method implement the existing medical image segmentation model named HalfU-Net. HalfU-Net can reduce the computational cost by simplify the decoder part of the UNet. Channel attention is assigned between ghost model for every stage in encoder part to capture more relevant feature. This leads to performance improvement in skin lesion segmentation problem. Despite that our model achieve state-ofthe-arts performance by surpassing U-Net in aspects of DSC and IoU metrics by 0.86% and 3.48% respectively, the number of parameter of our proposed method is more than 200 times less then U-Net. © 2023 IEEE.

关键词： Image segmentation

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Towards the Utilization of Parallel Programming to Speed Up RAM Forensics 10

Towards the Utilization of Parallel Programming to Speed Up ...

引用

10th International Conference on software Defined Systems, SDS 2023

作者： Al-Sharif, Ziad A. Alhamouri, Rahaf Jararweh, Yaser Lewis University Dept. of Engineering Computing and Mathematical Sciences Romeoville United States Jordan University of Sci. and Tech. Dept. of Software Engineering Irbid Jordan Jordan University of Sci. and Tech. Dept. of Computer Science Irbid Jordan

ISBN: (纸本)9798350382235

Memory forensics uses volatile digital artifacts as evidence about criminal activities. Analyzing captured memory dumps for volatile data requires time and effort. This paper studies the utilization of parallel programming to speed up RAM forensics. It presents a performance-based evaluation of parallel programming in the domain of memory forensics and compares sequential and parallel approaches to speed up the memory analysis process. First, it evaluates the sequential approach and uses it as a base case for further parallel approaches. Second, it evaluates two of the parallel approaches that can be performed on a typical user machine. Our experiments evaluate the use of two parallel programming paradigms: the in-process parallelization approach using OpenMP, and the inter-process parallelization approach using MPI. Our results compare the performance of the sequential approach, OpenMP thread-based approach, and MPI process-based approach. Experimentations compare the performance of three scenarios using six files of different sizes and various numbers of threads and/or processes. The results show that the use of MPI is slightly better than OpenMP approaches on the use of 2 and 4 processes/threads. However, when the number of processors/threads is increased to 8 and 16, OpenMP slightly outperforms the MPI approach. Additionally, the parallelization approach using OpenMP and MPI provides 3X to 5X speed up over the traditional sequential approach. Moreover, it is worth mentioning that this speed-up is achieved on traditional user machines without the use of HPC computers. © 2023 IEEE.

关键词： Parallel programming

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Utilizing RL and Web-Enhanced Commuting for Traffic Congestion Mitigation and Public Transportation Enhancement 7

Utilizing RL and Web-Enhanced Commuting for Traffic Congesti...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Jenefa, A. Jerusha Miraclin Dulcie, B. Carolin Joanna Sheryl, K. Esswari, S. Sandra Jeslena, W. Kuriakose, Bessy M. Karunya Institute of Tech. and Sci. Division of CSE Coimbatore India Malla Reddy Engineering College for Women Department of Computer Science and Engineering India

ISBN: (纸本)9798350340600

In the face of escalating urban traffic congestion and inadequate public transportation efficiency, this study introduces a pioneering solution by merging Reinforcement Learning (RL) with web-enhanced commuting strategies. The central goal is to create a real-time adaptive system capable of optimizing traffic control and enhancing public transit management. The urgency of the problem lies in the elongated commute times and ecological ramifications caused by congested roads, underscoring the necessity for innovative and sustainable urban mobility solutions. Through the innovative combination of RL techniques and web-enhanced approaches, the study proposes a dynamic framework that can potentially alleviate congestion and augment public transportation efficiency through on-the-fly adjustments. Preliminary experimental results highlight encouraging advancements in both traffic flow management and public transportation efficiency, affirming the efficacy of the RL-augmented system. This integration of cutting-edge technology and commuter-focused strategies presents an avenue for reshaping urban mobility paradigms, mitigating traffic congestion, and optimizing public transportation systems, thereby paving the way for a more sustainable urban future. © 2023 IEEE.

关键词： Reinforcement learning

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