检索结果-内蒙古大学图书馆

2nd IEEE International Conference on Data, Decision and Systems, ICDDS 2023

作者： Hegadi, Rajendra Akella, Syam Sankar Karthik Reddy, Koushik Kumar Pavan, C. Indian Institute of Information Technology Dharwad Department of Data Science and Intelligent Systems Karnataka India Indian Institute of Information Technology Dharwad Department of Computer Science and Engineering Karnataka India

ISBN: (纸本)9798350314168

As we enter a new era of decentralized applications and blockchain technology, the issue of Web3 security becomes crucial and requires immediate attention. Given the growing popularity of using smart contracts in web3, it is vital to ensure the protection of digital assets and user data online. This paper aims to examine the different security risks associated with smart contracts in web3 and propose measures to mitigate them. This research article delves into the identification, causes, and potential mitigation steps for common vulnerabilities found in smart contracts in web3. Specifically, we focus on six major vulnerabilities: overflow and underflow, access control vulnerabilities, re-entrancy attacks, price manipulation attacks, signature reply attacks and self-destruct attacks. By comprehensively discussing these vulnerabilities and offering mitigation strategies, this research aims to disseminate the security vulnerabilities in smart contracts in Web3 ecosystem and provides a path way for adoption of blockchain technology. © 2023 IEEE.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

Enhancing Accuracy with Recursive Feature Selection Using Multiple Machine Learning and Deep Learning Techniques on NSL-KDD Dataset 1

引用

2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Mohanty, Subrat Kumar, Satendra Agarwal, Mayank Department of Mathematics Indian Institute of Technology Patna India Department of Computer Science and Engineering Indian Institute of Technology Patna India

ISBN: (数字)9789819995189

ISBN: (纸本)9789819995172

The world has moved toward digital revolution and more and more services are now being available online. This has presented significant challenges in ensuring the availability, integrity, and confidentiality of networks. Therefore, the implementation of intrusion detection systems (IDS) that actively monitor network activity and dynamically analyze incoming traffic has become essential. In modern computer networks, IDS play a critical role in identifying and alerting administrators of any malicious activities that could threaten data *** research aims to evaluate machine learning (ML) and deep learning (DL) techniques for intrusion detection using the NSL-KDD dataset. While having many features may seem advantageous, it doesn’t always lead to improved performance on large datasets. Reducing and picking just the right set of features may improve both speed and accuracy. This is why a method called recursive feature elimination (RFE) is utilized to select the most relevant features for the NSL-KDD dataset. In order to test the efficacy of ML and DL methods in IDS, we have undertaken a comprehensive experiment. Specifically, we tested the effectiveness of several algorithms, including CNN, RNN, LSTM, GB, CART, and CatBoost. Our study involved evaluating the performance of various algorithms and comparing them, and the detection accuracy [lowest-highest] for all the attacks are as follows: DoS [98.79–99.81%], Probe [96.34–99.64%], R2L [96.15–98.66%], U2R [79.98–99.73%]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

Forensic Analysis and Detection of Spoofing Based Email Attack Using Memory Forensics and Machine Learning 1

引用

18th EAI International Conference on Security and Privacy in Communication Networks, SecureComm 2022

作者： Shukla, Sanjeev Misra, Manoj Varshney, Gaurav Department of Computer Science and Engineering Indian Institute of Technology Roorkee India Department of Computer Science and Engineering Indian Institute of Technology Jammu India

ISBN: (数字)9783031255380

ISBN: (纸本)9783031255373

Emails encounter many types of cyber-attacks and email spoofing is one of the most common and challenging investigation problems. This paper identifies spoofing-based email attacks in an organization by analyzing received and replied emails. The detection works by capturing the email traces via memory forensics. Unlike the traditional approaches of capturing the entire physical memory, we only capture the memory of relevant processes for email header extraction. It significantly reduces the size of the memory dump and makes detection faster. We suggest a novel mechanism called URL extractor, which uses seven novel features from URL to identify the live running email message process by applying ML that traces received emails and captures their header fields for analysis. The authentication header fields of SPF, DKIM, DMARC, and ARC are examined closely to develop a detection algorithm for received emails. Similarly, novel header fields of Reference along with MX record are applied for the detection of replied emails. The MX record is fetched to verify the domain name by sending a forward ns-lookup query to DNS. It also includes an email attack alert mechanism for intimating IT admins of an organization regarding suspected attacks. The results thus obtained show that email detection takes 35 secs (apprx.) to complete with high accuracy and low false positives. © 2023, ICST institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Human Age Prediction Based on Gender Image Recognition 5

Human Age Prediction Based on Gender Image Recognition

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Vignesh, Bowgikar Keerthan, A V K Sai Shyamsunder, Bonkuru Satheesh Kumar, S. Computer Science and Information Technology Institute of Aeronautical Engineering Hyderabad India

ISBN: (纸本)9798331518097

Facial age and gender classification is a fundamental task for many applications, such as social media, security and personalized user experience. This project provides a detailed approach to age and gender classification based on CNNs, including model training and evaluation using the UTK Face dataset. The diversity and granularity of the datasets offered an excellent basis for training and evaluating models. Normalization and augmentation were added in our preprocessing pipeline to enhance the generalizability of this model among a wide variety of conditions, mitigating overfitting risks. Breathable CNN Model ArchitectureThe CNN model was designed in a deep architecture to incorporate several convolutional layers, pooling layerone after other. It helps the model to learn detailed complex patterns of face at different levels. We introduced techniques like batch normalization and dropout to improve training stability and mitigate overfitting. The CNN was shown to be correlated and robust by demonstrating a 95% accuracy for gender classification and an MAE of 95.04 for age estimation. The performance of the model was evaluated in a comprehensive manner using standard metrics like accuracy, precision, recall and F1-score. Ethics and Bias - This project also explored the ethics of using age and gender classifiers, investigating fairness in model performance across different demographic groups to identify potential biases. To summarize, this project demonstrates the use of CNNs for age and gender classification utilizing the UTK Face dataset, a practical approach with significant scope for real-world application. Future work will focus on refining the model to address even more diverse datasets and exploring seamless integration into real-time applications. © 2024 IEEE.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Exploration of Semantic Priming Studies in Native Language of Children:Study on Bengali L1 and English L2 Bilingual Children

引用

SN computer science 2025年第2期6卷 1-18页

作者： Banerjee, Jaya Nithin, Kota Sai Sai, Uppalapati Hari Krishna Chakraborty, Baisakhi Basu, Anupam Department of Computer Science & Engineering National Institute of Technology West Bengal Durgapur 713209 India National Institute of Technology West Bengal Durgapur India Department of Computer Science & Engineering Indian Institute of Technology (IIT) Kharagpur 721302 India

Word Priming is an event in which prior subjection to a particular word impacts the reply of the individual to the upcoming correlated word. The response time is inferred to be less in identifying the target word preceded by an associated word compared to the word in seclusion or isolation as per the experiments undertaken on an indian language where research incorporates Bengali-speaking children. Bengali is an important language of consideration on national and international platforms. A set of twenty-one school-going children with ages in the range of 7 to 10 years are subjected to a word priming assessment. Analysis established a positive impact of word prime in the reaction of a set of children aged seven to ten to certain listed words Due to the presence of word prime, the average error measure of the lower progressing answerers reduced by a significant 19.8%. With reference to the Bengali script, the average error measure is 10 percent. With the involvement of the word prime, the mean error measure is 1.6 percent. This paper proposes first of its kind of research studies and analysis with regard to the positive effect of word priming on the response time of target word identification on Bengali speaking bi-lingual dyslexic children to yield positive results. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bengali Children English Learning impairment Priming Psycholinguistics Second language Word identification Word priming

来源：评论

学校读者我要写书评

暂无评论

Automatic Creation of Ontologies and Rules from ML Models for Access Control in IoT 15

Automatic Creation of Ontologies and Rules from ML Models fo...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Tyagi, Surendra Jinwala, Devesh C. Bhattacharjee, Subhasis Computer Science & Engineering Indian Institute of Technology Jammu India Computer Science & Engineering S. V. National Institute of Technology Surat India

ISBN: (纸本)9798350370249

Ontologies have been increasingly used in designing access control policies in Internet of Things (IoT) systems. Machine Learning (ML) has been extensively used to identify patterns from existing data. Trained ML models can be used on end devices for applying access control for resources and users. However it is unreasonable to deploy ML models into the low-powered and mostly hand-held IoT devices. This paper demonstrates an attempt to exploit the power of ML for generating access control rules. We propose algorithms for generating Ontologies and policy rules from trained ML models. Motivated by their simple design, low or almost no requirement for data preprocessing, no requirement for scaling/normalization of data, simple, intuitive design, and most essentially an inherent ability to optimize, by handling irrelevant features through pruning, we use Decision Trees (DT) for the classification in the proposed approach. For smart home dataset, we show in this paper, automatic creation of Ontologies and policy rules from trained DT models. The resulting Ontologies and Semantic Web Rule Language (SWRL) rules are then deployed into an Ontological framework (Protege) and tested for consistency using a Reasoner. Our approach demonstrates a significant improvement in the efficiency of rule generation and offers a promising direction towards designing automated access control systems. In Protege, we achieve 100% consistency in deployment and testing of the generated access control policies by firing queries. We obtain exactly the same accuracy from Protege queries as we get from ML model. We also propose pruning on DT as a fall-back method for IoT devices with limited computing and memory resources that can handle only limited number of policy rules. The proposed method creates a deployable access control system in a few minutes without any human intervention. Our experiment shows a turnaround time of 5 minutes for the Smart Home dataset. ©2024 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

MDFIDNet: Multi-domain Feature Integration Denoising Network 27th

MDFIDNet: Multi-domain Feature Integration Denoising Network

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Das, Debashis Maji, Suman Kumar Department of Computer Science and Engineering Indian Institute of Technology Bihar Patna801106 India

ISBN: (纸本)9783031781247

In the realm of computer vision, image denoising remains a formidable challenge with profound implications for fields like medical imaging, remote sensing, and photography. Despite notable advancements in deep learning, there are enduring challenges: current convolutional neural networks (CNNs) frequently struggle with training complexities due to their emphasis on increased network depth. At the same time, these networks often fail to adequately consider the crucial role of gradient information in the denoising process. Furthermore, there is a distinct gap in leveraging transform domain analysis in image denoising. This study addresses these limitations with MDFIDNet, a novel triple-phase attentive fusion network tailored for image denoising. MDFIDNet integrates three independent feature extraction pipelines: a frequency domain processing pipeline (FDP) enhanced by a multi-scale convolutional attention Block (MSCAB), a spatial domain processing pipeline (SDP) focusing on detail feature preservation, and a gradient-domain processing pipeline (GDP) driven by multidirectional gradient information. Experimental validation demonstrates that MDFIDNet surpasses existing benchmarks, exhibiting robust performance across diverse datasets. Comprehensive ablation studies underscore the individual contributions of each network component, elucidating the novel advancements that underpin MDFIDNet’s superior denoising efficacy. The source code and further details are available in the https://***/debashis15/MDFIDNet. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Random Fusion of Mix 3D and Polar Mix to Improve Semantic Segmentation Performance in 3D Lidar Point Cloud

引用

computer Modeling in engineering & sciences 2024年第7期140卷 845-862页

作者： Bo Liu Li Feng Yufeng Chen School of Computer Science and Engineering Macao University of Science and TechnologyMacao999078China School of Computer Science and Artificial Intelligence Chaohu UniversityChaohu238000China Institute of Vehicle Information Control and Network Technology Hubei University of Automotive TechnologyShiyan442002China

This paper focuses on the effective utilization of data augmentation techniques for 3Dlidar point clouds to enhance the performance of neural network *** point clouds,which represent spatial information through a collection of 3D coordinates,have found wide-ranging *** augmentation has emerged as a potent solution to the challenges posed by limited labeled data and the need to enhance model generalization *** of the existing research is devoted to crafting novel data augmentation methods specifically for 3D lidar point ***,there has been a lack of focus on making the most of the numerous existing augmentation *** this deficiency,this research investigates the possibility of combining two fundamental data augmentation *** paper introduces PolarMix andMix3D,two commonly employed augmentation techniques,and presents a new approach,named *** of using a fixed or predetermined combination of augmentation methods,RandomFusion randomly chooses one method from a pool of options for each instance or *** innovative data augmentation technique randomly augments each point in the point cloud with either PolarMix or *** crux of this strategy is the random choice between PolarMix and Mix3Dfor the augmentation of each point within the point cloud data *** results of the experiments conducted validate the efficacy of the RandomFusion strategy in enhancing the performance of neural network models for 3D lidar point cloud semantic segmentation *** is achieved without compromising computational *** examining the potential of merging different augmentation techniques,the research contributes significantly to a more comprehensive understanding of how to utilize existing augmentation methods for 3D lidar point *** data augmentation technique offers a simple yet effective method to leverage the diversity of augmentation techniques and boost the ro

关键词： 3D lidar point cloud data augmentation RandomFusion semantic segmentation

来源：评论

学校读者我要写书评

暂无评论

Development of Emotion Recognition via Federated Learning: A Multi-modal Approach with Hybrid Convolutional Network

Development of Emotion Recognition via Federated Learning: A...

引用

2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Kaur, Dilsheen Misra, Anuradha Vyas, O.P. Amity University Amity School of Engineering and Technology Department of Computer Science and Engineering Lucknow Campus Lucknow India Indian Institute of Information Technology Allahabad Department of Information Technology Prayagraj India

ISBN: (纸本)9798350385199

Human emotion recognition is the crucial process for performing effective communication and intelligent interaction. Identifying human emotion through discrete technologies is an essential step. Affective computing is one machine learning process in which multi-task learning has gathered a great deal of attention. It leverages shared data across associated activities to enhance performance. The physiological results from the aforementioned devices may also constitute a privacy risk because they reveal extremely sensitive information. To address this issue, we develop a federated learning approach with multi-task learning to precisely recognize facial emotion from the human. The required face images and Electroencephalogram (EEG) signals are collected from online sources. The face images are considered as feature set 1. From the EEG signals, the deep features are extracted through the Restricted Boltzmann Machine (RBM) and these features are considered as feature set 2. Both feature set 1 and feature set 2 are applied to the federated learning network, where the Hybrid Convolution-based Convolutional Neural Network (HC-CNN) is adopted for the recognition of human facial emotions. The face images are applied to the 2D layer and the extracted deep features from the EEG signal are applied to the 1D layer of the hybrid convolution network to provide an effective face recognition outcome. The recognition outcome of the proposed model is compared with the traditional face emotion recognition models concerning various measures. The recommended model successfully recognizes the emotion of the human with an accuracy of 95.75 % and precision of 95.97%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Biomarkers to Predictive Modeling: Leveraging Feature Selection and Ensemble Learning for Ovarian Cancer Prognosis 2024

Biomarkers to Predictive Modeling: Leveraging Feature Select...

引用

16th International Conference on Contemporary Computing, IC3 2024

作者： Srivastava, Jaya Rathi, Megha Department of Computer Science & Engineering Jaypee Institute of Information Technology India

ISBN: (纸本)9798400709722

Treatment outcomes and patient survival rates are greatly improved by early identification of ovarian cancer. However, to increase diagnostic accuracy, effective predictive modeling is required due to the biomarkers' variability and complexity. This research proposes a complex methodology that combines feature selection and ensemble learning approaches to create a forecast model for the diagnosis of ovarian cancer. Recursive feature elimination (RFE), a thorough feature selection technique, was originally used to identify important biomarkers in order to lower dimensionality and improve model interpretability. We then used ensemble learning techniques, which fuse together several machine learning models, to boost the prognosis's stability and predictive performance. Using a dataset containing clinical and genetic data from ovarian cancer patients, ensemble techniques called Adaboost, Catboost, Random Forest, and Gradient boosting were put into practice and validated. Performance metrics like precision, accuracy, F1-score, recall and AUC-ROC curve were used to measure the techniques' efficacy. The findings show that our approach not only performs better than conventional single-predictor models, with results that are roughly 99% correct, but it also offers important insights into the biomarkers which are most symptomatic of the course of ovarian cancer. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：