检索结果-内蒙古大学图书馆

2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Tomar, Riya Chauhan, Naman Chauhan, Chhavi Tomar, Yash Singh, Soumyendra Chauhan, Nishant Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India University of Petroleum and Energy Studies School of Computer Science Dehradun India Cmr University Department of Computer Science and Engineering Bengaluru India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Mathematics Chennai India Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Noida India

ISBN: (纸本)9798350317558

This research paper delves into the intersection of advanced technology and military strategies, specifically focusing on the imperative task of camouflaged object detection within the Indian Paramilitary forces. Leveraging cutting-edge DL Frameworks, like CNNs, Faster RCNN, and YOLOv8, our methodology aims to enhance the precision and efficiency of identifying concealed weapons and camouflaged objects in real-world scenarios. The collaboration with the Indian Paramilitary has enabled the creation of a dataset tailored to the challenges faced on the ground, reflecting a commitment to addressing contemporary threats. The study explores the global positioning of India in military technology, emphasizing the nation's role as a key player in adopting sophisticated solutions for defense. Beyond technological innovation, this research underscores a noble cause the protection of lives and the fortification of national security. The fusion of advanced technology and a profound sense of duty forms the core of this work, exemplifying a commitment to the greater good. © 2024 IEEE.

关键词： Camouflaged object detection Convolutional Neural Networks (CNNs) Deep Learning Defense Strategies Faster RCNN Indian Paramilitary Military Technology Object Recognition Situational Awareness YOLOv8

来源：评论

学校读者我要写书评

暂无评论

Energy efficient resource utilization and load balancing in virtual machines using prediction algorithms

引用

International Journal of Cognitive computing in engineering 2023年第1期4卷 127-134页

作者： Udayasankaran, P. Thangaraj, S. John Justin Department of Computer Science and Engineering Saveetha School of Engineering Chennai Tamilnadu India

In recent years due to increase in the number of customers and organizations utilize cloud applications for personal and professionalization become greater. As a result of this increase in utilizing the Cloud services, Cloud data centers found to be huge consumption with boundless energy and harmful materials into the environment. The Primary goal of fully virtual machines is to minimize consumption of boundless energy utilization and providing uttermost resources. Balancing the loads on the servers has become a challenging task for the cloud service provider. Load balancing on servers helps to improve the performance of the virtual machines and to minimize energy and processing time in cloud systems. Here we discuss about various load balance r serves as the single and distributes incoming traffic across multiple targets. Reducing the consumption for the data center industries, power supply capacity has more increased and demand will be continued. With an aim to reduce and bring down active server infrastructure burdens. All about, this includes various challenges are taken care of by the environment. Behavior approaches of systems and consumers are not defined clearly. We present a novel resource optimization framework to discover jobs with minimum utilization. This progress ground work encircle boundless energy utilization. © 2023

关键词： Virtual machine

来源：评论

学校读者我要写书评

暂无评论

Enhanced Botnet Attack Detection using Machine Learning and Neural Networks 7

Enhanced Botnet Attack Detection using Machine Learning and ...

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Dharaneish, V.C. Rajakrishnan, Sandeep Sudhay, S. Senthil Kumar, T. Siddhesh, T.S. Vajipayajula, Sulakshan Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Coimbatore India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India Ibm India Pvt Ltd Ibm Security Bengaluru India

ISBN: (纸本)9798350359299

In an era characterized by pervasive interconnectivity, Internet of Things (IoT) devices have revolutionized automation, facilitating seamless data exchange. However, this technological leap has triggered heightened concerns about data security amid proliferating cyber-attacks. Given the omnipresence of IoT systems, the need for fortified networks to thwart potential intrusions is imperative. This work focuses on crafting an Intrusion Detection System (IDS) for IoT network security based on Machine learning (ML) and Deep Learning (DL) techniques, utilizing the Bot-IoT dataset from the University of New South Wales. Using a small dataset after preprocessing and feature selection, a comparative analysis of multiclass classification models, including Multinomial Logistic Regression, Artificial Neural Networks, Decision Tree Classifier, and Random Forest Classifier, is conducted. The results highlight the Decision Tree Classifier as the optimal model, with an exceptional accuracy of 99.98%, closely followed by the Random Forest Classifier with an accuracy of 99.91%. Automated hyperparameter tuning using GridSearchCV significantly contributes to enhancing model performance. A thorough evaluation, incorporating multi-classification metrics like Accuracy, Precision, Recall, and F1-score, is done to ensure a comprehensive assessment of the models. This research offers valuable insights into fortifying IoT network security, with implications for safeguarding the integrity of interconnected systems. © 2024 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Hybrid Deep Learning Based Attack Detection for Imbalanced Data Classification

引用

Intelligent Automation & Soft computing 2023年第1期35卷 297-320页

作者： Rasha Almarshdi Laila Nassef Etimad Fadel Nahed Alowidi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science Faculty of Computer Science and EngineeringUniversity of HailHailSaudi Arabia

Internet of Things(IoT)is the most widespread and fastest growing technology *** to the increasing of IoT devices connected to the Internet,the IoT is the most technology under security *** IoT devices are not designed with security because they are resource constrained ***,having an accurate IoT security system to detect security attacks is *** Detection Systems(IDSs)using machine learning and deep learning techniques can detect security attacks *** paper develops an IDS architecture based on Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)deep learning *** implement our model on the UNSW-NB15 dataset which is a new network intrusion dataset that cate-gorizes the network traffic into normal and attacks *** this work,interpolation data preprocessing is used to compute the missing ***,the imbalanced data problem is solved using a synthetic data generation *** experiments have been implemented to compare the performance results of the proposed model(CNN+LSTM)with a basic model(CNN only)using both balanced and imbalanced ***,with some state-of-the-art machine learning classifiers(Decision Tree(DT)and Random Forest(RF))using both balanced and imbalanced *** results proved the impact of the balancing *** proposed hybrid model with the balance technique can classify the traffic into normal class and attack class with reasonable accuracy(92.10%)compared with the basic CNN model(89.90%)and the machine learning(DT 88.57%and RF 90.85%)***,comparing the proposed model results with the most related works shows that the proposed model gives good results compared with the related works that used the balance techniques.

关键词： IoT IDS deep learning machine learning CNN LSTM

来源：评论

学校读者我要写书评

暂无评论

Optimizing DDoS Attack Detection Using Machine Learning 2

Optimizing DDoS Attack Detection Using Machine Learning

引用

2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Aggarwal, Saransh Behera, Bhagrajyoti Singh, Murari Kumar Sharma, Ajeet Kumar School of Computing Science and Engineering Sharda University Greater Noida India School of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (纸本)9798331530389

The increase in the Distributed Denial of Service attack (DDoS) leads to a significant threat to the network security. Inability to timely and accurately detect DDoS attacks disrupts services offered by companies and the government causing financial losses. Additionally, prolonged DDoS attacks can spoil user experience and degrade company reputation. The recent literature reveals various techniques for detecting DDoS attacks, including pre-trained machine learning and deep learning algorithms. This research paper addresses this challenge by tweaking random forest algorithms for maximum accuracy using CICDDoS2019 dataset. The methodology includes collecting of dataset, data processing, feature scaling and training multiple machine learning algorithms to identify maximum accuracy for detecting DDoS attacks. The result shows improvement of accuracy and precision of machine learning models used. Future work may include training the model for other types of attacks, real time implementation of the model developed, developing a hybrid approach for detection and protection from DDoS attack. © 2025 IEEE.

关键词： Network security

来源：评论

学校读者我要写书评

暂无评论

Fake News Detection on Social Media Using Ensemble Methods

引用

computers, Materials & Continua 2024年第12期81卷 4525-4549页

作者： Muhammad Ali Ilyas Abdul Rehman Assad Abbas Dongsun Kim Muhammad Tahir Naseem Nasro Min Allah Department of Computer Science COMSATS UniversityIslamabad45550Pakistan School of Computer Science and Engineering Kyungpook National UniversityDaegu41566Republic of Korea Department of Computer Science and Engineering Korea UniversitySeoul02841Republic of Korea Department of Electronic Engineering Yeungnam UniversityGyeongsan-si38541Republic of Korea Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam34223Saudi Arabia

In an era dominated by information dissemination through various channels like newspapers,social media,radio,and television,the surge in content production,especially on social platforms,has amplified the challenge of distinguishing between truthful and deceptive *** news,a prevalent issue,particularly on social media,complicates the assessment of news *** pervasive spread of fake news not only misleads the public but also erodes trust in legitimate news sources,creating confusion and polarizing *** the volume of information grows,individuals increasingly struggle to discern credible content from false narratives,leading to widespread misinformation and potentially harmful *** numerous methodologies proposed for fake news detection,including knowledge-based,language-based,and machine-learning approaches,their efficacy often diminishes when confronted with high-dimensional datasets and data riddled with noise or *** study addresses this challenge by evaluating the synergistic benefits of combining feature extraction and feature selection techniques in fake news *** employ multiple feature extraction methods,including Count Vectorizer,Bag of Words,Global Vectors for Word Representation(GloVe),Word to Vector(Word2Vec),and Term Frequency-Inverse Document Frequency(TF-IDF),alongside feature selection techniques such as Information Gain,Chi-Square,Principal Component Analysis(PCA),and Document *** comprehensive approach enhances the model’s ability to identify and analyze relevant features,leading to more accurate and effective fake news *** findings highlight the importance of a multi-faceted approach,offering a significant improvement in model accuracy and ***,the study emphasizes the adaptability of the proposed ensemble model across diverse datasets,reinforcing its potential for broader application in real-world *** introduce a pioneering ensemble

关键词： Fake news detection Machine Learning(ML) Deep Learning(DL) Chi-Square ensembling

来源：评论

学校读者我要写书评

暂无评论

Prediction of Lumpy Virus Skin Disease Using Artificial Intelligence 1

引用

5th International Conference on Data and Information sciences, ICDIS 2023

作者： Kholiya, Pankaj Singh Kriti Mishra, Amit Kumar School of Computing DIT University Dehradun India Department of Computer Science and Engineering Jain University Bengaluru India

ISBN: (数字)9789819969067

ISBN: (纸本)9789819969050

Lumpy skin disease (LSD) is a skin infection caused by the lumpy skin disease virus, which belongs to the genus of Capripoxviruses. The disease transmitted by arthropod vectors has high morbidity and low mortality rates. Contagious through arthropod-borne feces and infected insects, it is virtually a non-vectored disease. It takes one to four weeks for viremia to develop. It affects all animals regardless of their age or gender. The use of LSD by cattle in Asia has recently been reported as a terrifying threat. A morbidity rate of 7.1% was reported among cattle for the first time in India for LSD. Clinical manifestations of the disease include fever, anorexia, nodules on the mucus membranes of the mouth, nose, genital, udder, eyes, and rectum, less milk production, infertility, abortion, and even death. First introduced in Bangladesh in July 2019, the disease has since spread to China, Bhutan, India, Nepal, Vietnam, and Myanmar in Southeast Asia. This paper presents a comprehensive overview of LSD outbreaks in Asian countries over the past three years and also sheds light onto the existing methods of disease detection based on artificial intelligence techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Systematic Review of Emerging Deep Learning approaches for Crop Diseases detection 7

Systematic Review of Emerging Deep Learning approaches for C...

引用

7th International Conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Vasuki Rohinidevi, V. Deeban Chakravarthy, V. Deepak, S. Prem Kumar SRM Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India B V Raju Institute of Technology Department of Computer Science and Engineering Narsapur India

ISBN: (纸本)9798350372816

Deep learning, a branch of artificial intelligence, has drawn interest from the academic and corporate realms, especially in areas like speech and image analysis, video processing, and natural language processing. Its use in agricultural plant protection has expanded to include the diagnosis and evaluation of pests and plant diseases. In this situation, there are several benefits to using deep learning. It removes the biases related to manually chosen pathogen properties, improves objectivity when determining disease characteristics, and hastens the advancement of contemporary technology. In order to diagnose agricultural leaf illnesses, this study explores the most recent developments in deep learning methods, including CNN, DENSNET, InceptionV3, VGG16, Resnet50, and MobileNet, in addition to a variety of sensors. This project aims to aid scientists in the field by tackling challenges in merging deep learning and cutting-edge imaging tools to enhance illness identification. It addresses current trends and barriers, shedding light on unresolved issues and paving the way for further research. By clarifying persistent problems and posing new questions, it fosters progress and innovation in the field of medical imaging and diagnosis. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Committed-programming reductions: formalizations,implications and relations

引用

science China(Information sciences) 2024年第10期67卷 151-171页

作者： Jiang ZHANG Yu YU Dengguo FENG Shuqin FAN Zhenfeng ZHANG State Key Laboratory of Cryptology Department of Computer Science and Engineering Shanghai Jiao Tong University Trusted Computing and Information Assurance Laboratory Institute of SoftwareChinese Academy of Sciences

In this work, we introduce a class of black-box(BB) reductions called committed-programming reduction(CPRed) in the random oracle model(ROM) and obtain the following interesting results:(1) we demonstrate that some well-known schemes, including the full-domain hash(FDH) signature(Eurocrypt1996) and the Boneh-Franklin identity-based encryption(IBE) scheme(Crypto 2001), are provably secure under CPReds;(2) we prove that a CPRed associated with an instance-extraction algorithm implies a reduction in the quantum ROM(QROM). This unifies several recent results, including the security of the Gentry-Peikert-Vaikuntanathan IBE scheme by Zhandry(Crypto 2012) and the key encapsulation mechanism(KEM) variants using the Fujisaki-Okamoto transform by Jiang et al.(Crypto 2018) in the ***, we show that CPReds are incomparable to non-programming reductions(NPReds) and randomly-programming reductions(RPReds) formalized by Fischlin et al.(Asiacrypt 2010).

关键词： provable security random oracle model quantum random oracle model black-box reduction/separation programmability

来源：评论

学校读者我要写书评

暂无评论

Real-Time Threat Management using Deep Q-Learning and Mininet 3

Real-Time Threat Management using Deep Q-Learning and Minine...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sivakumar, Amrutha Bhavya Reddy, G. Bhanot, Shreya Rajagopal, Shinu M. Prashanth, B.N. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The research combines Deep Q-Learning(DQN) with a Mininet-based network simulation and Scapy intrusions detection system (IDS) for malicious traffic prioritizing. The RL agent continuously learns to act based on real-time traffic data and builds an adaptive model that detects and prioritizes threats such as SYN Flood, UDP Flood, Slowloris, HTTP Flood etc. The RL agent then adjusts its policy via the seamless combination of network simulation and live traffic detection to make sure lossy channels representing high-severity attacks are dealt with first, lessening the damage. The system improves the identification of intrusions by stimulating the intrusion prioritization dynamically with continuous learning and feedback, increasing network security. © 2025 IEEE.

关键词： Network intrusion

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：