检索结果-内蒙古大学图书馆

Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

引用

computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： Islam Zada Mohammed Naif Alatawi Syed Muhammad Saqlain Abdullah Alshahrani Adel Alshamran Kanwal Imran Hessa Alfraihi Department of Software Engineering International Islamic UniversityIslamabad25000Pakistan Information Technology Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science University of PeshawarPeshawar25121Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes K Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree

来源：评论

学校读者我要写书评

暂无评论

Cycle Consistency Loss with Long Short Connection in Convolutional Neural Network-Generative Adversarial Network for Image Steganography 1

Cycle Consistency Loss with Long Short Connection in Convolu...

引用

1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Fatima, Syeda Imrana Garapati, Yugandhar Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331529246

Image steganography is the practice of concealing secret information within a digital image in such a way that the alteration is undetectable to the human eye. It ensures secure communication by embedding data into the pixel values of a carrier image. However, the extraction of a secret message from highly compressed or noisy images poses some challenges, which diminishes the reliability and robustness of steganographic methods. This research proposes Cycle consistency loss, combined with Long Short Connection Convolutional Neural Network-Generative Adversarial Network (CLSCCNN-GAN) to enhance the security of hidden data and prevent the adversarial recovery of the secret message. In the discriminator, the CLSCCNN is incorporated to enhance the model's ability to distinguish among real and steganographic images by leveraging LSC and Cycle Consistency Loss (CCL). The LSC preserves significant image information that enhance robustness of the discriminator, while CCL ensures that the stego image looks similar to the cover image which makes it harder for adversaries to identify hidden information. The proposed CLSCCNN-GAN obtains a high Peak-to-Signal-Noise-Ratio (PSNR) with a PSNR of 42.09 and 49.46 respectively on the MNIST and CIFAR10 datasets, outperforming existing methods namely, Hiding data and Adversarial attacking with GAN (HAG-Net). © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

An effective compressed image authentication scheme based on N-variant AMBTC

引用

Multimedia Tools and Applications 2024年第2期83卷 3801-3829页

作者： Su, Guo-Dong Chang, Chin-Chen Lin, Chia-Chen School of Big Data and Artificial Intelligence Fujian Polytechnic Normal University Fuzhou350300 China Department of Information Engineering and Computer Science Feng Chia University 100 Wenhwa Road Seatwen Taichung40724 Taiwan Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung41170 Taiwan

Image authentication is one technique that provides integrity protection for digital images, making them sensitive to any slight modification. Recently, the detectability of absolute moment block truncation coding (AMBTC) compressed images has been attracting more attention due to its integrity authentication concerns. Thus, this paper proposes an effective N-variant AMBTC based image authentication scheme for AMBTC compressed images. First, a novel AMBTC compression code is derived using a variant AMBTC technique that can provide a better quality of compressed images. Then, each AMBTC compression code is further eliminated into a version requiring fewer bits using the proposed N-variant AMBTC technique, and those vacated N-bits are used to conceal authentication codes, which is generated by hashing the AMBTC compression code and location information. Experimental results confirm that the proposed scheme has superior stability and accuracy in tampering detection while providing better visual quality of the watermarked image. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-Based Autonomous Optimization of Unattended Wireless Sensor Networks Through Multi-Cause Reinforcement Learning

引用

SN computer science 2025年第2期6卷 1-11页

作者： Chaudhary, Jitendra Kumar Dudeja, Deepak Verma, Neeraj Kumar Saini, Arun Kumar Kapila, Dhiraj Mohapatra, Smaranika School of Computing Graphic Era Hill University Bhimtal Campus Uttrakhand Bhimtal India Department of Computer Science and Engineering M.M. Engineering College Maharishi Markandeshwar (Deemed to Be University) Mullana Haryana Ambala India Department of Data Science and Engineering Manipal University Jaipur Jaipur India Department of IcfaiTech The ICFAI University Jaipur India Department of Computer Science & Engineering Lovely Professional University Punjab Phagwara India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India

Self-configuration refers to a node's ability to dynamically adjust resource allocation based on changing network conditions, either autonomously or with minimal human input. Alongside this, self-optimization is aimed at maintaining optimal network performance despite fluctuating conditions. Protocols like MRL-SCSO, designed for Unattended Wireless Sensor Networks (UWSNs), ensure robust coverage, connectivity, and energy efficiency under varying loads. MRL-SCSO employs learning-based mechanisms, such as Reinforcing Multiple Agents, incorporating features like boundary construction, state scheduling, topology control, and data dissemination to enhance network management. When compared to the Collect Tree Protocol (CTP), MRL-SCSO exhibits faster packet transmission due to backbone nodes and traffic load prediction, reducing delays by 2–5 ms, which is a significant improvement over existing algorithms. Additionally, MRL-SCSO delivers a better packet delivery ratio, dropping only by 5–12% with shorter data transmission intervals, while CTP experiences a 20–40% drop when all nodes are active. Furthermore, MRL-SCSO improves throughput by 20–30 kb/s compared to CTP, demonstrating its superior ability to optimize network performance without sacrificing reliability. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： CTP MRL-SCSO PDR Reliability UWSN

来源：评论

学校读者我要写书评

暂无评论

Improving Emotion Recognition in Audio Signals: Leveraging Novel Features and Deep Learning for Improved Classification 3rd

Improving Emotion Recognition in Audio Signals: Leveraging N...

引用

3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Chaudhary, Poonam Choudhary, Neeraj Department of Computer Science and Engineering The NorthCap University Gurugram India Department of Computer Science and Engineering University College of Engineering and Technology Bikaner India

ISBN: (纸本)9789819726707

Recently, there has been interest in classifying emotions using audio inputs and machine learning methods. Because a single statement might be delivered in a variety of emotional circumstances, textual data alone is insufficient for identifying the emotions, necessitating the adoption of novel feature extraction approaches. This paper aims to provide an improved framework which uses a detailed study on audio sound features like mel-frequency cepstral coefficients (MFCCs), zero crossing rate (ZCR), spectral bandwidth and many other features for audio analysis and uses the same for predicting emotions. The paper also emphasises on various audio augmentation techniques to improve the generalising power of the model. With the above-mentioned improvements in the feature vector and a carefully selected deep neural network, state-of-the-art classification accuracy is achieved on the two state-of-the-art datasets Toronto emotional speech set (TESS) and the Ryerson Audio-Visual database of Emotional Speech and Song (RAVDESS). The paper is concluded with the improved approach for emotion recognition using audio signals and leads to future scope of efficient speech recognition applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

T³Planner: Multi-Phase Planning Across Structure-Constrained Optical, IP, and Routing Topologies

引用

IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1823-1839页

作者： Hao, Yijun Yang, Shusen Li, Fang Zhang, Yifan Zhao, Cong Ren, Xuebin Zhao, Peng Xu, Chenren Wang, Shibo Xi’an Jiaotong University National Engineering Laboratory for Big Data Analytics Xi’an710049 China Xi’an Jiaotong University National Engineering Laboratory for Big Data Analytics Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an710049 China Peking University School of Computer Science Beijing100871 China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University Xi'an710049 China

Network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, and are incapable of satisfying all rigorous topological structure constraints (e.g., dual-homing rings) defined by network standards and operators, especially in large-scale networks. These significantly limit their usability and performance in production networks. We consider a general topology planning problem with typical structure constraints over three essential phases (greenfield, reconfiguration, and site expansion) and topological layers (optical, IP, and routing topologies). We present, T3Planner, a novel practical solver to this problem in production. Specifically, we develop a structure-driven encoder based on graph neural network (GNN) for concise structure encoding, and design a new learning framework with optical-centric layer compression/reconstruction and rule-aided reinforcement learning (RL) for fast convergence and high performance. Extensive experiments on nine real topologies demonstrate that T3Planner scales to large optical networks with hundreds of sites, saves 46.6% cost, and supports 3.12x more demand when compared to related existing approaches. © 1983-2012 IEEE.

关键词： Network topology

来源：评论

学校读者我要写书评

暂无评论

A Surface Crack Detection System Based on Image Input: The CNN Approach 2

A Surface Crack Detection System Based on Image Input: The C...

引用

2nd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Tiwari, Shweta Srivastava, Animesh Parihar, Parul Yadav, Puneet Kumar Chandigarh University Department of Computer Science and Engineering India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Alliance University Department of Computer Science and Engineering India

ISBN: (纸本)9798350327533

Any concrete construction that has surface fractures can seriously harm both its surroundings and the people nearby. Cracks that are discovered early on can help stop additional harm. Cracks are found using traditional techniques, which entail visual inspection by humans. The construction sector has its problems and complexities, identical to any other industry. Any pro blems that develop during construction can be found and fixed immediately. Due to time and money limitations, it is challenging to visually detect cracks and other flaws in particularly big constructions. This will cost an enormous amount of money, materials, labor, and time. Additionally, if cracks are found manually, the areas where they can be found are constrained, and human error is a possibility. This research study proposes a novel autonomous system that acts as surface recognition model using CNN with multiple layers, as well as basic preprocessing. To solve these issues, data enhancement has been done in this model to split the obtained data into two broad categories: The crack occurs means positive, and the crack does not exist meaning negative. The suggested surface classification approach achieved a precision of 99.51 % and 99.13%, respectively. Since pa vement or roadway cracks will be automatically identified and analyzed, fewer accidents will occur on roads because of this classification. When compared to manual observation, the suggested system will also be quicker. © 2024 IEEE.

关键词： construction convolution crack detection model Surface

来源：评论

学校读者我要写书评

暂无评论

3-Multi ranked encryption with enhanced security in cloud computing

引用

Digital Communications and Networks 2023年第2期9卷 313-326页

作者： YeEun Kim Junggab Son Reza M.Parizi Gautam Srivastava Heekuck Oh Major in Bio Artificial Intelligence Dept.of Computer Science and EngineeringHanyang UniversityAnsanRepublic of Korea Department of Computer Science Kennesaw State UniversityMariettaUSA Department of Software Engineering Kennesaw State UniversityMariettaUSA Department of Mathematics and Computer Science Brandon UniversityBrandonCanada Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Dept.of Computer Science and Engineering Hanyang UniversityAnsanRepublic of Korea

Searchable Encryption(SE)enables data owners to search remotely stored ciphertexts selectively.A practical model that is closest to real life should be able to handle search queries with multiple keywords and multiple data owners/users,and even return the top-k most relevant search results when *** refer to a model that satisfies all of the conditions a 3-multi ranked search ***,SE schemes that have been proposed to date use fully trusted trapdoor generation centers,and several methods assume a secure connection between the data users and a trapdoor generation *** is,they assume the trapdoor generation center is the only entity that can learn the information regarding queried keywords,but it will never attempt to use it in any other manner than that requested,which is impractical in real *** this study,to enhance the security,we propose a new 3-multi ranked SE scheme that satisfies all conditions without these security *** proposed scheme uses randomized keywords to protect the interested keywords of users from both outside adversaries and the honest-but-curious trapdoor generation center,thereby preventing attackers from determining whether two different queries include the same ***,we develop a method for managing multiple encrypted keywords from every data owner,each encrypted with a different *** evaluation demonstrates that,despite the trade-off overhead that results from the weaker security assumption,the proposed scheme achieves reasonable performance compared to extant schemes,which implies that our scheme is practical and closest to real life.

关键词： Searchable encryption Multi-keyword search Multiple data owners/users Ranked search Enhanced security

来源：评论

学校读者我要写书评

暂无评论

Graph Contrastive Learning for Truth Inference 40

Graph Contrastive Learning for Truth Inference

引用

40th IEEE International Conference on data engineering, ICDE 2024

作者： Liu, Hao Liu, Jiacheng Tang, Feilong Li, Peng Chen, Long Yu, Jiadi Zhu, Yanmin Gao, Min Yang, Yanqin Hou, Xiaofeng Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong East China Normal University School of Data Science and Engineering Shanghai China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China University of Aizu School of Computer Science and Engineering Japan Simon Fraser University School of Computing Science Canada

ISBN: (纸本)9798350317152

Crowdsourcing has become a popular paradigm for collecting large-scale labeled datasets by leveraging numerous annotators. However, these annotators often provide noisy labels due to varying expertise. Truth inference aims to infer accurate consensus labels from noisy crowdsourced annotations. Existing approaches rely heavily on hand-engineered assumptions or ground truth data, limiting their applicability. To address this, we propose GOVERN, a graph contrastive learning framework for truth inference without such external supervision. GOVERN employs a novel graph data augmentation strategy to generate views capturing worker coordination patterns. A contrastive objective then encourages invariant representations across views, enabling the discovery of features related to the hidden consensus. Further, a label correction method based on k-nearest neighbors refines noisy pseudo-labels to supervise model training. Comprehensive experiments on 9 real-world datasets demonstrate that GOVERN outperforms state-of-the-art truth inference techniques. © 2024 IEEE.

关键词： Crowdsourcing

来源：评论

学校读者我要写书评

暂无评论

DeepVNE: Deep Reinforcement and Graph Convolution Fusion for Virtual Network Embedding 16

DeepVNE: Deep Reinforcement and Graph Convolution Fusion for...

引用

16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Keerthan Kumar, T.G. Kb, Aneesh Siddheshwar, Anagha Marali, Amulya Kamath, Anupama Koolagudi, S.G. Addya, Sourav Kanti National Institute of Technology Department of Computer Science and Engineering Karnataka India Siddaganga Institute of Technology Department of Computer Science and Engineering India Pes University Department of Computer Science and Engineering India

ISBN: (纸本)9798350383119

Network virtualization (NV) plays a crucial role in modern network management. One of the fundamental challenges in NV is allocating physical network (PN) resources to the demands of the virtual network requests (VNRs). This process is known as a virtual network embedding (VNE) and is NP-hard. Most of the existing approaches for VNE are based on heuristic, meta-heuristic, and exact strategies with limited flexibility and the risk of being stuck in local optimal solutions. In this concern, we provide a deep reinforcement learning (DRL) and graph convolution network (GCN) fusion for VNE (DeepVNE) for maximizing the revenue-to-cost ratio. The DeepVNE takes advantage of the power of actor-critic models within the DRL framework to detect network states and provide optimal solutions matched to current conditions. DeepVNE effectively captures the structural dependencies of both VNRs and PN resources by GCNs, allowing better decision-making during the embedding. Considering several features in the agents throughout the training phase improves resource utilization. The experiments show that DeepVNE outperforms the baselines by gaining a 51% acceptance ratio and 28% revenue-to-cost ratio. © 2024 IEEE.

关键词： Graph Convolutional Network Network Virtualization Reinforcement Learning Virtual Network Embedding

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：