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CBIDM: Controller Based Intelligent Detection and Mitigation Approach for Interest Flooding Attack in Named Data Networking

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SN computer science 2024年第8期5卷 1008页

作者： Kumar, Naveen Aleem, Abdul Department of Computer Science and Engineering Sardar Vallabhbhai National Institute of Technology Gujarat Surat 395001 India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida 201310 India

Named Data Networking (NDN) aims to fix the flaws of TCP/IP networks. NDN ensures provenance and integrity by requiring publishers to sign each content, which consumers can verify. This makes NDN more secure than TCP/IP. However, NDN is prone to Interest Flooding Attacks (IFA). In IFA, attackers fill the Pending Interest Tables (PITs) of NDN routers with fake entries by requesting non-existent content. Many methods have been proposed to counter IFA. Most of these methods rely on statistical thresholds, which reduce detection accuracy. Our previous work showed that using machine learning improves IFA detection accuracy. This paper introduces a Controller-Based Intelligent Detection and Mitigation (CBIDM) approach for online IFA detection. It deploys a trained Artificial Neural Network (ANN) detector on NDN routers. Additionally, a traceback-based mitigation method is applied using a central controller. The controller collects topology and attack data from each router. Routers then use this data for IFA mitigation. The proposed approach is more efficient than existing methods in terms of data packets received and satisfaction ratio. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： ANN IFA Interest Flooding Attack Named Data Networking NDN

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Diabetic Foot Ulcer Classification Using Deep Learning Approach 8th

Diabetic Foot Ulcer Classification Using Deep Learning Appro...

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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Sivakamasundari, P. Niranjana, G. Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Kattankulathur India

ISBN: (纸本)9783031736162

Podiatrist diagnosis and lesion localization are used as current testing approaches for Diabetic Foot ulcers (DFU). Current systems for automation concentrate on either categorization or division. One of the most common metabolic conditions, Diabetes (also called diabetes mellitus), is brought on by the pancreas incapacity to generate sufficient anti hyper-glycemic hormone to control glycemia levels. It may cause "Diabetic Peripheral Neuropathy," a collection of nerve diseases that can develop if the blood glucose level is left uncontrolled and unmanaged. Blood glucose levels may be efficiently maintained by recognising diabetes initially through constant surveillance and screenings. Using image processing methods such as data stretching with Deep Learning, image reduction and improvement, image division, and feature extraction, this paper offers a thorough approach for analysing thermal scans of the diabetic foot. By using these techniques, doctors may assess and track a patient's condition with fewer examinations. In this work, EfficientNetB3 architecture is compared with other Deep Learning models for accuracy in DFU identification. To create a powerful deep learning model, we gathered an extensive collection of 1,775 DFU photos. The basis of belief for this data gathering was created by two medical experts using annotator software to define the DFU region of interest. It took 48 ms to figure out a single image and 57.2 MB to validate the model. EfficientnetB3 got an average mean accuracy of 93%. This work demonstrates the effectiveness of Deep Learning for DFU localization in real-time, which could have improved with the use of a larger data set. © IFIP International Federation for Information Processing 2025.

关键词： Diagnosis

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The adoption of human resources analytics in construction projects in Jordan:antecedents and consequences

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Journal of Management Analytics 2025年第1期12卷 134-173页

作者： Ahmad Abujraiban Gabriel J.Assaf Aladeen Yousef Rashid Hmoud Department of Civil Engineering and Construction École de Technologie Supérieure(ÉTS)University of QuebecMontrealCanada Faculty of Information Technology and Computer Science Department of Information TechnologyYarmouk UniversityIrbidJordan

The construction industry is increasingly using analytics tools to enhance decision-making and streamline project ***,human resource analytics(HRA)adoption has been slow due to concerns about cost and *** studies investigating HRA adoption rely on conceptual models and are in their early *** address this gap,this study takes an empirical approach by examining the antecedents and impacts of HRA adoption on project performance in the Jordanian construction industry.A deductive conceptual framework based on technology-organisation-environment(TOE)and resource-based view(RBV)theories is developed,and 198 individuals are *** structural equation modelling(PLS-SEM),the study identifies eight factors that significantly impact HRA adoption and shows that adoption leads to significant project performance *** study provides valuable insights into HRA adoption in the construction industry,with implications for human resource management,project performance,and the industry as a whole.

关键词： HR Analytics HRM project performance construction industry

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AI Based Criminal Detection and Recognition System for Public Safety and Security using novel CriminalNet-228 4th

AI Based Criminal Detection and Recognition System for Publi...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Murthy, Jamuna S. Siddesh, G.M. Department of Computer Science and Engineering Ramiah Institute of Technology Bengaluru India Department of Artificial Intelligence and Data Science Ramiah Institute of Technology Bengaluru India

ISBN: (纸本)9789819726103

The recent surge in public space criminal activities underscores the need for an efficient system to promptly detect, recognize, and track criminals. Existing AI-based criminal detection literature, while insightful, has limitations such as the complexity to analyze video data, time, and speed of accuracy when it comes to training the algorithms that call for further advancements. Hence proposed AI-driven system addresses this demand, aiming to automate criminal identification, equipping law enforcement with a potent tool for proactive crime prevention and resolution. The system utilizes the innovative "CriminalNet-228" Convolutional Neural Network (CNN) architecture, meticulously trained on a vast criminal image dataset for enhanced detection accuracy. To bolster face detection, additional computational resources like parallel processing and distributed computing are employed, enabling real-time analysis of extensive CCTV footage. Notably, the system tracks identified criminals, providing law enforcement with real-time situational awareness. CriminalNet-228 achieves an impressive overall Mean Average Precision (mAP) of 0.65 and excels in detecting facial features as small as 4 × 4 pixels, demonstrating its detail-oriented recognition capabilities. When evaluating Proposed CriminalNet-228 in comparison to established state-of-the-art techniques like Fast-RCNN, Yolov7, and AlexNet, it surpassed them in terms of precision, recall, f-measure, and accuracy, achieving an impressive accuracy rate of 99.2%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Face recognition

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Shallow Tuned DenseNet: A Lightweight Convolutional Neural Network Approach for Enhanced Skin Lesion Recognition 2

Shallow Tuned DenseNet: A Lightweight Convolutional Neural N...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Ahmmed, Tomal Tahsin Faruk, Md. Farukuzzarnan Srizon, Azmain Yakin Mahedy Hasan, S.M. Khansur, Md. Nasif Efat, Anwar Hossain Hossain, Md. Rakib Al Mamun, Md. Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798331517984

Skin lesions are irregularities on the skin, often harmless, though some may progress to skin cancer. Detecting them early is crucial for successful treatment and prevention of cancer development. Traditional detection methods involve significant expenses and testing. This study introduces an auto-mated system for classifying skin lesions using a Shallow Tuned DenseNet (STDN), a compact Convolutional Neural Network (CNN) model. We employ Transfer Learning (TL) by fine-tuning a pretrained DenseNet-169 model with additional lightweight layers, allowing the STDN to adapt to the PH2 dataset's specific characteristics while remaining efficient. Initially, we utilize data augmentation techniques to address class imbalances within the dataset, improving model performance by reducing bias towards the majority class. The evaluation demonstrates a promising hundred percent accuracy with perfect F1-score and specificity. We attribute these results to effective transfer learning utilization, enhanced tuning, and partially to data augmentation, which mitigates overfitting and biases, facilitating robust feature extraction. Furthermore, we employ Grad-CAM (Gradient-weighted Class Activation Mapping) to visualize and interpret the model's predictions, enhancing trust and comprehension. The proposed STDN model makes a significant contribution by achieving high accuracy with a lightweight design, offering a potentially cost-effective and efficient solution for automated skin lesion detection, thereby aiding in early cancer prevention. © 2024 IEEE.

关键词： Convolutional neural networks

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Diabetic Prediction using Feature Selection based Random Forest and Fine Tuned K-Nearest Neighbor Classifier Algorithm-A Design Thinking Approach 4

Diabetic Prediction using Feature Selection based Random For...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Ramya, S. Vijayaraghavan, T. Kalaivani, D. Department of Computer Science India Sns College of Technology Department of Computer Science and Engineering India Dr.SNS Rajalakshmi College of Arts and Science Department of Computer Technology Coimbatore India

ISBN: (纸本)9798350300093

In low- and middle-income nations today, diabetes affects the majority of the population, according to a World Health organization (WHO) research. The WHO report suggested that 80% of the deaths would be due to the diabetes from 2016 to 2030. However, the current method continues to provide findings that are erroneous, which has a substantial negative impact on performance. To overcome the abovementioned issue, in this work, Random Forest (RF) algorithm and Fine tuned K-Nearest Neighbor (FKNN) classifier algorithm is proposed. Pre-processing, feature selection, and classification are the three primary stages of this project. Initially, preprocessing is performing for improving the final dataset results more accurately. Preprocessing is the process of cleaning the database into correct format. In order to choose more relevant and useful data from the dataset, the feature selection is then carried out utilizing the RF algorithm. It also minimizes the risk of over fitting with minimum features. Finally, diabetic prediction and classification is done by using FKNN classifier algorithm is used for categorizing items in the feature space based on training samples that are the most similar to the objects being classified. According to the experimental results, the suggested RF+FKNN method outperforms the current algorithms in accuracy, precision, recall, and f-measure. © 2023 IEEE.

关键词： Classification (of information)

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Efficient Liver and Tumor Segmentation from CT Images Using a Hybrid ResUNet Model 3

Efficient Liver and Tumor Segmentation from CT Images Using ...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Singh, Aditya Pratap Mishra, Santosh Kumar Nidhi Singh, Jagendra Agrawal, Madhup Joshi, Prakash Ajay Kumar Garg Engineering College Department of Information Technology Ghaziabad India Bennett University School of Computer Science Engineering & Technology Greater Noida India Raj Kumar Goel Institute of Technology Department of Computer Science Ghaziabad India

ISBN: (纸本)9798350364569

In this paper, we investigated deep learning models for automated segmentation of liver and tumor regions from CT images to improve the diagnostic accu-racy as well as treatment planning in case of liver cancer. The research used a dataset of 3200 CT scans, with about 70% serving as the testing set and 30% for training. Preprocessing steps like Auto Windowing HU (Hounsfield Units) and Histogram Equalization to retain better clarity of the images. Based on the metrics of performance such as accuracy, Dice coefficient, IoU(Intersection over Union), precision and recall were calculated used three types of CNN models (ResUNet, VGG16 and VGG19). Its hybrid architecture combining U-Net and ResNet, called as ResUNet indicated better results with an accuracy of 97.3%, against VGG16 at 93.5% and VGG19 at 91.5%. The model also demonstrated good data loss reduction from 0.35 to about 0.10 over the course of a total for around an epoch angle in enduring caching past phase. VGG16 and VGG19 outperformed the baseline performances with reduced data losses while higher accuracies, although their convergence rate was slightly slower than ResUNet. The results confirmed the high precision of liver/tumor segmentation that ResUNet provides, which would be useful for clinical practice to achieve sufficient diagnostic improvement. The architecture of ResUNet specifically designed for the segmentation tasks in medical imaging from this study, demonstrates to be a versatile framework favoring its potential integration into automated tools within oncological diagnostics and could thus radically transform liver cancer detection. © 2024 IEEE.

关键词： computerized tomography

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Road Damage Detection Using Real Time Video 8th

Road Damage Detection Using Real Time Video

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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Gomathi, G. Niranjana, G. Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Kattankulathur India

ISBN: (纸本)9783031736162

This study presents a completely unique deep-learning- powered real-time road damage detection system, paving the way for more secure roads and smarter infrastructure. With the help of convolutional neural networks (CNNs) and an easy to use interface, the system can automatically and clearly identify different types of road damage, such as surface irregularities, detailed fractures, and dangerous potholes. This innovative method surpasses the drawbacks of laborious manual inspections and ushers in a brand new age of advanced road safety and efficient maintenance. A strong CNN model that has been painstakingly trained on an intensive collection of annotated pictures and videos forms the basis of the system. It, as it should be, detects different sorts of damage with an accuracy close to 89%, offering users useful visible information via highlighting particular places on output pictures. However, the creativity doesn’t stop there. The system has a clean-to-use Tkinter graphical consumer interface (GUI) that facilitates engagement. It also has real-time alarm triggers that, without delay, inform the appropriate authorities, ensuring timely restoration and decreasing any safety threats. To make the system work better, it is put through a radical testing process using common evaluation standards along with F1-rating, accuracy, precision, and recall. The findings are clear-cut, which is revolutionary pressure in addition to a useful tool for finding road degradation. Beyond the instantaneous safety advantages, it offers better infrastructure management and optimized useful resource allocation, opening the door to a future in which technology protects our lives and creates more secure, more convenient travel. © IFIP International Federation for Information Processing 2025.

关键词： Resource allocation

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Electronic health records protection strategy by using blockchain approach

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Multimedia Tools and Applications 2024年第39期83卷 86883-86894页

作者： Kathole, Atul B. Patil, Sonali D. Kumbhare, Savita Vhatkar, Kapil Kshirsagar, Aniruddha P. Kimbahune, Vinod V. Computer Engineering Dr. D. Y. Patil Institute of Technology Pune India IT Dept. Pimpri Chinchwad College of Engineering Pune India Computer Science and Engineering Department K.B.P College of Engineering Satara Satara India

Electronic health records (EHRs) provide significant challenges for medical organizations due to the digitalization of traditional medical information. When using EHRs, both doctors and patients are accustomed to spending considerable time conducting the necessary queries;nevertheless, the acquired data is not always related to the sought one, and access may be revoked if this is the case. The exchange and maintenance of medical health records are among the most crucial tasks in the healthcare system. The integrity of health records is crucial, as is maintaining patient anonymity, and either would have devastating effects in the event of a breach. Therefore, it is one of the most important reasons to keep electronic health records safe. Modern healthcare infrastructure is notoriously difficult to use, wasteful, and fraught with problems related to data integrity, security, and privacy. Better EHR management and monitoring, however, has the potential to lessen the severity of the problems with security and complexity. The blockchain's potential, with its decentralized and trustworthy nature in the healthcare industry, makes this a reality. Problems with data management and the way information is validated and disseminated are at the heart of the healthcare delivery system's inherent difficulties. Improved access to medication tracking, hospital assets, drug systems, patient information, and so on is the primary benefit of employing blockchain technology in healthcare data management. Because of how important it is to have access to a patient's medical history when prescribing medication, blockchain technology has the potential to significantly enhance the current healthcare delivery system. Therefore, it is critical to develop a blockchain-based system for verifying and protecting medical records. The primary objective of this article is to create a permissioned blockchain-based Ciphertext Policy-Attribute-based encryption system to protect privacy and regulate acces

关键词： Blockchain

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Revolutionizing Machine Learning Security: The Role of Quantum-Enhanced Federated Learning 2

Revolutionizing Machine Learning Security: The Role of Quant...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Kannan, E. Ravikumar, S. Carmel Mary Belinda, M.J. Vijay, K. Vathani, Anstey Kannan, Sriram Vel Tech Rangarajan Dr. Sagunthala RD Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical And Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala RD Institute of Science and Technology Department of Civil Engineering India Fidelity Investments Data Engineering Chennai India

ISBN: (纸本)9798331534950

In today's age of collaborative machine learning where data sharing happens across organizations and privacy becomes a real issue. Old-school methods of securing systems work, but tend to lack against popular new attacks. The research uses a novel approach that uses quantum computing to reimagine what privacy means. An article which deals with the combination of quantum technologies and federated learning systems, specifically discussing how it may set a new standard for data privacy. The plan is to use quantum key distribution and entanglement-based protocols in order to fortify the defences of collaborative machine learning systems against adversarial attacks or unauthorized access, according a consortium statement. The approach to quantum -enhanced federated learning presented provides a solution not only with secure improvements but also implementing more extreme circumstances in decentralised learning scenarios, such as challenges due to different data available and regulatory demands. This paper provides a theoretical benchmark for quantum federated learning and focuses on the underlying concepts leading to excellent security guarantees. Applications and simulations show that this framework can be used in practice with effectiveness. Furthermore, this work benchmarks the scalability of quantum federated learning for different classical and common machine-learning models. The results are a major step towards the goal of making more sophisticated privacy solutions available and allow to create secure, private-first workspace for collaborative machine learning. By leveraging the latest in quantum computing along with specialized tactics designed to combat rapidly evolving data protection threats, this approach will provide a strong foundation for future applications of shared machine learning within distributed quantum environments. © 2024 IEEE.

关键词： Adversarial machine learning

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