检索结果-内蒙古大学图书馆

2023 IEEE International Conference on Data and Software Engineering, ICoDSE 2023

作者： Widodo, Anteng Rochim, Adian Fatchur Warsito, Budi School of Postgraduated Studies Diponegoro University Doctoral Program of Information System Semarang Indonesia Muria Kudus University Faculty of Engineering Department of Information System Kudus Indonesia Diponegoro University Faculty of Engineering Department of Computer Engineering Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Statistics Semarang Indonesia

ISBN: (纸本)9798350381382

Low-Rate Denial of Service (LDoS) attacks, an emerging breed of DoS attacks, present a formidable challenge in terms of their detectability. Within the realm of network security, these attacks cast a substantial shadow over the availability and integrity of services. This study seeks to delve into the intricate mechanics of LDoS attacks and explore the landscape of developed detection methodologies. Employing bibliometric analysis, we employ a multifaceted approach to gather research insights pertaining to LDoS attack detection. This approach encompasses the examination of evolving research trends, scrutiny of widely cited scientific publications within the same domain, identification of prolific researchers shaping the field, and a judicious selection of reputable publishers for dependable information acquisition. Furthermore, our study undertakes a meticulous categorization and classification of previously proposed LDoS detection methods documented in well-established scientific literature. This endeavor not only provides a more profound comprehension of the evolutionary trajectory and thematic thrusts within LDoS attack detection research, but it also facilitates the identification of gaps and directions for future exploration. The culmination of these analytical efforts culminates in a comprehensive framework. This contribution manifests through structured categorization, classification, and a forward-looking research roadmap, which collectively serve as valuable tools for both current and aspiring researchers engaged in LDoS attack detection. In summary, this research not only delves into the complexities of LDoS attacks and their mechanisms but also augments the current understanding of detection capabilities. Through its nuanced contributions, this paper extends a helpful guide to the dynamic realm of LDoS attack detection, thus catalyzing advancements and insights for the future. copy;2023 IEEE. © 2023 IEEE.

关键词： Denial-of-service attack

来源：评论

学校读者我要写书评

暂无评论

An Automated Multi-Level Convolutional Neural Network Approach for Classification of White Blood Cells 14

An Automated Multi-Level Convolutional Neural Network Approa...

引用

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

作者： Sangeetha, M. Tamizarasu, K. Devi, R. Manjula Arokiaraj, Rex MacEdo Sudha, K. Kumar, K. Kavin Dr.N.G.P. Institute of Technology Department of Information Technology Tamilnadu Coimbatore India K S R Institute for Engineering and Technology Department of Computer Science and Engineering Tamilnadu Namakkal India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India -CAS Department of Information Technology Ibri Oman K S R Institute for Engineering and Technology Department of Cyber Security Tamilnadu Namakkal India Kongu Engineering College Department of Ece Tamilnadu Perundurai India

ISBN: (纸本)9798350335095

Blood cells known as white blood cells (or leukocytes) are generated in the marrow of the bone marrow and circulate through the circulatory system and lymph tissue. White blood cells (WBC) are the cells responsible for defending the body against outside invaders. They aid the body in its defence against infections and other illnesses. There are three types of white blood cells: granulocytes (which include basophils, eosinophils and neutrophils), monocytes, and lymphocytes (which include T cells and B cells). Usually, a total blood count test includes checking the blood's white blood cell count. Infection, inflammation, allergies, and leukaemia. White blood cells have a lifespan of 13 to 20 days before being eliminated in the lymphatic system. The first time immature WBCs leave the bone marrow and enter the peripheral circulation, they are known as bands or stabs. Phagocytosis is the method through which leukocytes combat infection. This work's primary goal is to find the type of white blood cell from the provided input of white blood cells images and to categorize cell type according to its variety. Based on the progress made in developing the Deep Learning algorithm, this study has the potential to aid in the prediction of cell kinds and in assisting medical professionals in the diagnosis of a wide range of abnormalities and disorders. Inceptionresnetv2, VGG-16, CNN, are few deep learning model that can automatically assess white blood cell shots from different angles and circumstances of illumination in order to determine what kind of cell they are. The dataset that is used in the project contains four categories or classes with values ranging from 0 to 3, where 0,1,2 and 3 denotes particular variety of white blood cell. Among the models that have been utilised for the classification of images of blood cells, the accuracy obtained by proposed custom-built CNN framework is 98.70% that outperform the other framework. © 2023 IEEE.

关键词： Cells

来源：评论

学校读者我要写书评

暂无评论

Semi-Supervised Malware Traffic Classification Via Consistency Regularization and Pseudo-Labeling 23

Semi-Supervised Malware Traffic Classification Via Consisten...

引用

23rd IEEE International Conference on Communication technology, ICCT 2023

作者： Jiao, Qianlong Zhang, Xixi Zhang, Weijie Miao, Fucheng Ohtsuki, Tomoaki Adebisi, Bamidele Wang, Yu Gui, Guan College of Telecommunications and Information Engineering Njupt Nanjing China Reading Academy Nanjing University of Information Science and Technology Nanjing China Nanjing Great Information Technology Co. Ltd Nanjing China Keio University Department of Information and Computer Science Yokohama Japan Manchester Metropolitan University Faculty of Science and Engineering Manchester United Kingdom

ISBN: (纸本)9798350325959

Malware traffic classification (MTC) technology plays a crucial role in cyber security by serving as the first step of network intrusion detection system. Recently, deep learning has been successfully introduced into the field of network intrusion detection. However, its performance relies heavily on the quantity and quality of labeled samples, which are often difficult to obtain in real-world applications. To address these challenges, we propose semi-supervised MTC method based on consistency regularization and pseudo-labeling. This method utilizes unlabeled malware traffic data to improve the classification performance of the model. Specifically, we train the model using labeled malware traffic data. Then, weak data augmentation is performed on unlabeled traffic data and pseudo labels are generated through the model. Finally, the model predicts unlabeled data after strong data augmentation and compares it with pseudo labels. Model parameters are updated using losses from labeled and unlabeled samples. Experimental results demonstrate that our proposed method outperforms other classical classification approaches, including ladder networks, virtual adversarial training (VAT), convolutional neural network (CNN), and support vector machine (SVM), when the ratios of labeled samples range from 1% to 5%. © 2023 IEEE.

关键词： data augmentation deep learning Malware traffic classification semi-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Leveraging the fog-based machine learning model for ECG-based coronary disease prediction

引用

International Journal of Business Intelligence and Data Mining 2022年第3期21卷 328-353页

作者： Hanumantharaju, R. Shreenath, K.N. Sowmya, B.J. Srinivasa, K.G. Department of Computer Science and Engineering M S Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering Siddaganga Institute of Technology Tumakuru India Visvesvaraya Technological University Belagavi India Information Management and Emerging Engineering National Institute of Technical Teachers Training and Research Chandigarh India

Smart healthcare systems need a remote monitoring system based on the internet of things. Smart healthcare services are an innovative way of synergising the benefits of sensors for large-scale analytics to communicate better patient care. Work provides the sick with healthcare administrations as a sound population through remote observation using detailed calculations, tools and methods for better care. The proposed system integrates architecture based on IoT, fog computing and machine learning (ML) algorithms. The dimensionality of the data collected about heart diseases is loaded, filtered and extracted attributes at the fog layer;the classification model is built at the fog nodes. The resultant of the model is sent to the cloud layer to train classifiers. Cloud layer estimates the level of ML algorithms to predict disease. Result shows that random forest has better feature extraction than naive Bayes with flawlessness of 3% in precision, 3% in recall, and 13% in f-measure. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Task Scheduling for Multi-Objective Optimization in Cloud Computing: A Review

SSRN

引用

SSRN 2023年

作者： Abdulghani, Abdulrahman M. Department of Computer Science Faculty of Computer Science and Information Technology University Putra Malaysia Serdang Malaysia

Nowadays, cloud computing technologies witness a huge turnout in our life which in turn cause a tremendous demand by users who consume the services deployed by the cloud providers, not to mention the virtualization and resource management for the system. Recently, a significant amount of research has been carried out on task scheduling in cloud computing. This study presents a comprehensive review of task scheduling in cloud computing. It provides an overview of existing task scheduling algorithms in cloud computing to execute the tasks considering the cloud resources. This paper is organized based on the perspective of different objectives such as time of completion (Makespan), load balancing, resource utilization, and cost used as parameters. The task scheduling based on hybridization techniques for a non-deterministic polynomial known as (NP) Complete problem is compared using CloudSim Software. The findings show that the hybrid methods show less makespan than the stand-alone ones, on top of that the IDPSO infusion came with a Makespan of 0.64 milliseconds for 150 tasks. Finally, we present the future research direction to address task scheduling issues in cloud computing. © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

High-Level Deep Features and Convolutional Neural Network for Evaluating the Classification Performance of File Cluster Types 1

引用

6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Ali, Rabei Raad Dawd, Lahib Nidhal Mostafa, Salama A. Rachmawanto, Eko Hari Jubair, Mohammed Ahmed National University of Science and Technology Thi-Qar Nasiriyah Iraq Computer Techniques Engineering Department Dijlah University College Baghdad Iraq Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia Department of Informatics Engineering Dian Nuswantoro University 207 Imam Bonjol Street Semarang50131 Indonesia Department of Computer Technical Engineering College of Information Technology Imam Ja’afar Al-Sadiq University Al-Muthanna66002 Iraq

ISBN: (数字)9789819933150

ISBN: (纸本)9789819933143

Accurate file recovery of the corrupted or fragmented file is a very important task when missing the file system, since file recovery procedure involves methods, techniques, and tools that analyze and classify the contents of each data cluster including JPG file or non-JPG file. For classification problems, convolutional neural networks and high-deep learning have been applied to be very efficient and effective in file recovery. This study aims at finding a systematic comparison between a support vector machine (SVM) classifier method and an extreme learning machine (ELM) classifier method for classification issues. The classification methods automatically classify the files in a continuous series of data clusters based on three deep features which are rate of change (RoC), byte frequency distribution (BFD), and entropy. The methods are automatically assigning a class label of JEG file or non-JPG file for fragmented data clusters. The RABEI-2017 dataset was used for evaluating the performance of this study. The results demonstrate that the SVM method outperforms the ELM method with a high accuracy rate to classify fragmented JPG files. The accuracy of classification of the SVM method is 90.15% and of the ELM method is 96.21%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

VulPin: Finer-Grained Slicing for Pinpointing Vulnerability in Binary Programs: Precision in Vulnerability Detection: Advanced Slicing Techniques for Binary Program Analysis 7

VulPin: Finer-Grained Slicing for Pinpointing Vulnerability ...

引用

7th International Conference on computer science and Artificial Intelligence, CSAI 2023

作者： Chukkol, Abdulrahman Hamman Adama Luo, Senlin Yunusa, Haruna Yusuf, Abdulganiyu Abdu Mohammed, Abulfatahi School of Information and Electronics Beijing Institute of Technology Beijing China Information System and Security and Countermeasures Experimental Center Beijing Institute of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China School of Computer Science Beijing Institute of Technology Beijing China School of Management and Information Technology Modibbo Adama University Yola Nigeria

ISBN: (纸本)9798400708688

Identifying vulnerabilities in binary code with precision is a critical area of study in the field of software security. Most existing techniques fail to pinpoint the vulnerability's exact location, due to the extensive labelled data requirement and lack of targeted approach to improve on the problematic code. In this paper, we introduce VulPin, a system specifically designed to pinpoint vulnerabilities in binary code. This framework uses a hierarchical attention network that analyze program slices along with a vulnerability position matrix. Experimental outcomes indicate that our framework not only outperforms existing approaches but also pinpoints vulnerabilities more rapidly with a high Accuracy and a very low rate of false positives. © 2023 ACM.

关键词： Binary codes

来源：评论

学校读者我要写书评

暂无评论

eSROP Attack: Leveraging Signal Handler to Implement Turing-Complete Attack Under CFI Defense 1

引用

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

作者： Zhang, Tianning Cai, Miao Zhang, Diming Huang, Hao Department of Computer Science and Technology Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China School of Computer and Information Hohai University Nanjing China College of Computer Engineering Jiangsu University of Science and Technology Nanjing China

ISBN: (数字)9783031255380

ISBN: (纸本)9783031255373

Signal Return Oriented Programming (SROP) is a dangerous code reuse attack method. Recently, defense techniques have been proposed to defeat SROP attacks. In this paper, we leverage the signal nesting mechanism provided by current operating systems and propose a new variant of SROP attack called enhanced SROP (eSROP) attack. eSROP provides the ability of invoking arbitrary system calls, simulating Turing-complete computation, and even bypassing the fine-grained label-based CFI defense, without modifying the return address and instruction register in the signal frame. Because the signal returns to the interrupted instruction, the shadow stack defense can hardly detect our attack. Signal has strong flexibility which can interrupt the normal control flow. We leverage such flexibility to design a new code reuse attack. To evaluate eSROP, we perform two exploits on two real-world programs, namely Proftpd and Wu-ftpd. In our attacks, adversaries can invoke arbitrary system calls and obtain a root shell. Both attacks succeed within 10 min under strict system defense such as data execution prevention, address space layout randomization, and coarse-grained control flow integrity. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Network security

来源：评论

学校读者我要写书评

暂无评论

Smart Healthcare Monitoring system: Integrating IoT, Deep Learning, and XGBoost for Real-Time Patient Diagnosis 21

Smart Healthcare Monitoring System: Integrating IoT, Deep Le...

引用

21st OITS International Conference on information technology, OCIT 2023

作者： Paulraj, Kruthika Soms, Nisha David Samuel Azariya, S. Sathya Priya, S. Jeba Emilyn, J. Sureshkumar, Vidhushavarshini Sona College of Technology Department of Information Technology Tamil Nadu Salem India KPR Institute of Engineering and Technology Department of Computer Science Tamil Nadu Coimbatore India M. Kumarasamy College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Karur India SRMIST Department of Computer Science Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350358230

Integrating the Internet of Things (IoT), Deep Learning (DL), and the XGBoost algorithm has paved the way for transformative advancements in healthcare. This paper presents a pioneering study on a "Smart Healthcare Monitoring system"that harnesses the synergy of these technologies for real-Time patient diagnosis. With the escalating demand for accurate and swift medical assessments, our work addresses the crucial need for timely and precise diagnosis. The proposed system amalgamates IoT-enabled wearable devices and sensors to capture comprehensive patient data. A hybrid approach is employed to leverage this data, comprising a Deep Learning model for intricate pattern recognition and the XGBoost algorithm for rapid decision-making. Nonetheless, challenges such as data security and model interpretability are acknowledged, highlighting avenues for future research. This paper underscores the transformative potential of the IoT-Deep Learning-XGBoost integration, offering a robust foundation for further innovation in intelligent healthcare systems. As healthcare delivery evolves, our work underscores the promise of this interdisciplinary fusion in revolutionizing patient diagnosis and care. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Optimization of Cognitive Radio system Using Self-Learning Salp Swarm Algorithm

引用

computers, Materials & Continua 2022年第2期70卷 3821-3835页

作者： Nitin Mittal Harbinder Singh Vikas Mittal Shubham Mahajan Amit Kant Pandit Mehedi Masud Mohammed Baz Mohamed Abouhawwash Department of Electronics&Communication Engineering Chandigarh UniversityMohali140413India School of VLSI Design and Embedded Systems National Institute of TechnologyKurukshetra136119India School of Electronics&Communication Engineering Shri Mata Vaishno Devi UniversityKatra182320India Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computer and Information TechnologyTaif UniversityTaif21994Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

CognitiveRadio(CR)has been developed as an enabling technology that allows the unused or underused spectrum to be used dynamically to increase spectral *** improve the overall performance of the CR systemit is extremely important to adapt or reconfigure the *** Decision Engine is a major module in the CR-based system that not only includes radio monitoring and cognition functions but also responsible for parameter *** meta-heuristic algorithms offer numerous advantages compared to traditional mathematical approaches,the performance of these algorithms is investigated in order to design an efficient CR system that is able to adapt the transmitting parameters to effectively reduce power consumption,bit error rate and adjacent interference of the channel,while maximized secondary user ***-Learning Salp Swarm Algorithm(SLSSA)is a recent meta-heuristic algorithm that is the enhanced version of SSA inspired by the swarming behavior of *** this work,the parametric adaption of CR system is performed by SLSSA and the simulation results show that SLSSA has high accuracy,stability and outperforms other competitive algorithms formaximizing the throughput of secondary *** results obtained with SLSSA are also shown to be extremely satisfactory and need fewer iterations to converge compared to the competitive methods.

关键词： Cognitive radio meta-heuristic algorithm cognitive decision engine salp swarm algorithm

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：