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international Symposium on Advanced computing and Communication (ISACC)

作者： Richy Laskar Rakesh Das Rahnak Laskar Samarjit Das Meghna Dasgupta Bachelor of Technology in Computer Science and Engineering The Assam Royal Global University Guwahati Assam Royal School of Information Technology The Assam Royal Global University Guwahati Assam Royal School of Engineering Technology The Assam Royal Global University Guwahati Assam

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The rapid expansion of internet of Things (IoT) devices has significantly increased their vulnerability to cyber-attacks, with IoT botnets posing a critical threat by enabling distributed denial-of-service (DDoS) attacks, data exfiltration, and other malicious activities. Traditional security measures often fall short of effectively mitigating these threats due to the resource limitations of IoT devices. This research explores the use of machine learning (ML) models for IoT botnet detection, focusing on Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT) techniques. Furthermore, two hybrid models are proposed: Isolation Forest (IF) combined with SVM for anomaly detection and classification, and K-Means clustering integrated with Random Forest to enhance detection accuracy. These models are evaluated based on key performance metrics, including accuracy, precision, recall, and F1 score, to identify their effectiveness in detecting IoT botnets. The study demonstrates the potential of ML-based approaches, particularly hybrid models, to strengthen IoT security and overcome the limitations of traditional methods in resource-constrained environments.

关键词： Support vector machines Radio frequency Accuracy Biological system modeling Botnet Forestry internet of Things Decision trees Random forests Anomaly detection

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FedSimRep: A Federated Learning Method Based on Data Representation Selection 25

FedSimRep: A Federated Learning Method Based on Data Represe...

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Proceedings of the 2025 3rd international conference on Communication Networks and Machine Learning

作者： Falu Zhai Lu Wu Chunxiao Wang Tongyang Hao Shucang Zang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan Shandong China

ISBN: (纸本)9798400713231

As an emerging distributed technology, Federated learning enables model training while protecting privacy without sharing local data. However, the heterogeneity of geographic locations and devices leads to response delays from stragglers. Client grouping in federated learning can tolerate stragglers from a global perspective, but differences in data distribution within groups may result in local performance bias. To address this issue, we propose a federated learning method called FedSimRep, which selects representative clients based on data distribution similarity. FedSimRep performs group training by collecting readiness signals from topological communications and maintains synchronization tables and graphs to track training progress. Additionally, it selects representative clients by leveraging the relationship between local optimization similarity and data distribution similarity to optimize the global model. Experiments and theoretical analyses on public datasets such as MNIST, CIFAR-10, and UEU-Fed demonstrate that FedSimRep achieves significant advantages in terms of accuracy and efficiency.

关键词： Client Grouping

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Deep Forest-Based Intrusion Detection System for Edge Intelligence Assisted Smart Homes 3rd

Deep Forest-Based Intrusion Detection System for Edge Intell...

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3rd international conference on computing, Communication and Learning, CoCoLe 2024

作者： Jakka, Aishwarya Rani, J. Vakala Budhathoki, Binay Swathi, Y. Jayanthi, M. University of Pittsburgh Pittsburgh United States CMR Institute of Technology Bengaluru India Lead Engineer Memoria Inc. Nepal Kathmandu India Gopalan College of Engineering Bengaluru India CMR University Bengaluru India

ISBN: (纸本)9783031790409

Edge computing has revolutionized distributed architectures, enabling workloads to be strategically positioned at the network’s edge, where data is generated and actions are initiated. This paradigm facilitates various applications, including the internet of Things (IoT), smart grids, and smart homes, by providing cloud services directly to end-users. In particular, delay-sensitive applications benefit from the mobility, low latency, bandwidth, and location awareness offered by edge computing. Edge computing is an ideal platform for efficient data processing in smart homes. However, the security of edge gateways and nodes within smart home environments remains susceptible to security threats. This paper presents a novel approach employing a Deep Forest (DF)-based Intrusion Detection System (IDS) for Edge Intelligence. By embedding an IDS in edge nodes, this approach leverages edge computing to enhance security in smart home environments. The Deep Forest Classifier (DF) is innovative because it effectively addresses data imbalance in training datasets, a crucial factor in anomaly detection for binary-class classification. This integration enables real-time, low-latency threat detection and significantly improves security metrics, demonstrating substantial performance enhancements over traditional machine learning models. Experiments conducted on the UNSW-NB15 dataset demonstrate that the proposed binary classifier improves Accuracy by 26.24% over Naïve Bayes, 11.68% over Logistic Regression and 6.20% over KNN models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep Forest Classifier Edge computing internet of things Machine Learning Smart Home

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Identifying Application-Level distributed Denial-of-Service (DDoS) Attacks with Machine Learning (ML) 3rd

Identifying Application-Level Distributed Denial-of-Service ...

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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024

作者： Yaqub, Muhammad Atif Mustafa, Muzzamil Chuhan, Saad Hussain Sattar, Basit Hasan, Muhammad Zulkifl Hussain, Muhammad Zunnurain Qaiser, Muhammad Saad Abdin, Zain ul Bilal, Afshan Ali, Jibran Department of Computer Science National College of Business Administration and Economics Lahore Pakistan Department of Artificial Intelligence University of Management and Technology Lahore Pakistan Faculty of IT and Computer Science University of Central Punjab Lahore Pakistan Department of Computer Science Bahria University Lahore Campus Lahore Pakistan Premier Systems Karachi Pakistan

ISBN: (纸本)9783031863042

The modern digital age has seen an exponential growth in internet penetration, which in turn has increased the probability of a denial-of-service attack. When an evil user launches a Denial of Service (DoS) attack, it’s with the goal of flooding computing and network resources and blocking access to them for normal users. These kind of assaults may happen at any level of the OSI model, from the application layer all the way down to the transport layer. This research focuses on application layer DoS attacks instead of transport and network DoS attacks and attempts to detect Denial of Service using Machine Learning and Neural Network approaches. An updated dataset of Denial of Service attacks divided into many subcategories was used in this experiment. According to the research, the decision tree approach performed well in terms of accuracy and splitting compared to the logistic regression approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Logistic regression

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Correlation Analysis of Tor Traffic Based on RAINBOW Scheme in Cloud computing Environment 3rd

Correlation Analysis of Tor Traffic Based on RAINBOW Schem...

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3rd international conference on Cyberspace Simulation and Evaluation, CSE 2024

作者： Xu, Haosheng Lu, Hui Peng, Jin Tian, Zhihong Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China Huangpu Research School of Guangzhou University Guangzhou510530 China

ISBN: (纸本)9789819645053

With the introduction and development of cloud computing technology, major internet companies gradually put their business services and huge amount of user data information in the cloud for processing and storage because it can itself realize distributed management of large amount of storage hardware, CPU, graphics card and other computer hardware resources using software, and provide users with hardware services that cannot be supported in the original computer system. However, some attack methods and approaches in traditional computer networks have also migrated to the remote network space, posing a threat to the resources of enterprises and individuals. At the same time, in order to hide their attack traces, some attackers are using anonymous networks to attack the cloud service platform. To address the costly and inefficient drawbacks of the Tor de-anonymization method, this paper focuses on the process of deploying a non-blind watermarking scheme called RAINBOW into the Tor network for watermarking association, mainly by (1) simulating the Tor network traffic in the cloud environment using mainflow traffic simulation tools;(2) studying and designing the RAINBOW non-blind traffic injection method to mark and identify the data traffic entering the Tor network, and then conduct an example analysis on the effect of mark identification;(3) evaluate the feasibility of the scheme for the Tor network data in the real environment. The experimental results verify the potential feasibility of this scheme for traffic correlation tracking in the Tor network. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semiconductor storage

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THE ARCHITECTURE OF AN INTELLIGENT VIRTUAL MATHEMATICAL SOFTWARE REPOSITORY SYSTEM

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MATHEMATICS AND COMPUTERS IN SIMULATION 1994年第4-6期36卷 269-279页

作者： BOISVERT, RF Computing and Applied Mathematics Laboratory National Institute of Standards and Technology Gaithersburg MD 20899 USA

Much reusable software is available for solving routine mathematical and statistical problems. Unfortunately, locating this software is often quite difficult in current distributed computing environments. The Guide to Available Mathematical Software (GAMS) virtual software repository seeks to remedy this by providing users with convenient access to thousands of software modules physically distributed among several internet repositories, including netlib. In this paper the author will illustrate the use of GAMS, describe its implementation, and outline plans for the incorporation of expert advisory capabilities.

关键词： MATHEMATICAL SOFTWARE SOFTWARE ADVISORY SYSTEM SOFTWARE CROSS-INDEX SOFTWARE DISTRIBUTION STATISTICAL SOFTWARE VIRTUAL SOFTWARE REPOSITORY

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Research on Information Security technology Based on Blockchain 3

Research on Information Security Technology Based on Blockch...

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3rd IEEE international conference on Cloud computing and Big Data Analysis (ICCCBDA)

作者： Liu, Liang Xu, Budong Shandong Normal Univ Jinan Shandong Peoples R China

ISBN: (纸本)9781538643013

Information security is the key to the development of modern internet technology. The distributed mechanism, decentralized mechanism, password mechanism and scripted mechanism of the Blockchain present a completely new perspective for the development of internet information security technology. The Blockchain technology redefines the storage and dissemination methods of the information in the network. Neither participant needs to know each other, and nor does it require third-party certification bodies to participate. It records, transmits and stores transferring activities of the information value by distributed technology, ensures that data is not tampered and forged based on an asymmetric cryptographic algorithm, enables all participants reached a consensus on the status of blockchain data information. And from the current industry research on blockchain technology, it expounds the application of blockchain technology in identity authentication, data protection and network security. The Blockchain technology will be a great driving force in the process of information security technology change, and will have a far-reaching impact on the expansion of information security.

关键词： blockchain distributed timestamp decentralized information security

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Towards a Load Balancing in a Three-level Cloud computing Network

Towards a Load Balancing in a Three-level Cloud Computing Ne...

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3rd IEEE international conference on Computer Science and Information technology (ICCSIT)

作者： Wang, Shu-Ching Yan, Kuo-Qin Liao, Wen-Pin Wang, Shun-Sheng Chaoyang Univ Technol Taichung Taiwan

ISBN: (纸本)9781424455379

Network bandwidth and hardware technology are developing rapidly, resulting in the vigorous development of the internet. A new concept, cloud computing, uses low-power hosts to achieve high reliability. The cloud computing, an internet-based development in which dynamically scalable and often virtualized resources are provided as a service over the internet has become a significant issue. The cloud computing refers to a class of systems and applications that employ distributed resources to perform a function in a decentralized manner. Cloud computing is to utilize the computing resources (service nodes) on the network to facilitate the execution of complicated tasks that require large-scale computation. Thus, the selecting nodes for executing a task in the cloud computing must be considered, and to exploit the effectiveness of the resources, they have to be properly selected according to the properties of the task. However, in this study, a two-phase scheduling algorithm under a three-level cloud computing network is advanced. The proposed scheduling algorithm combines OLB (Opportunistic Load Balancing) and LBMM (Load Balance Min-Min) scheduling algorithms that can utilize more better executing efficiency and maintain the load balancing of system.

关键词： distributed System Cloud computing Scheduling Load Balancing

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Fault Tolerant Consensus with Multiagent Systems for distributed Coordinated Control Algorithm in the Energy internet Network 3

Fault Tolerant Consensus with Multiagent Systems for Distrib...

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3rd international conference on Artificial Intelligence, Automation and Control Technologies (AIACT)

作者： Hsieh, Chung-Hung Chen, Yee Ming Yuan Ze Univ Tao Yuan Dept Ind Engn & Management Taoyuan Taiwan

The future power system can be considered as an aggregation of controllable distributed systems devices that coordinate with each other through the internet of Things (IoT), energy internet (EI) new paradigms arise. In EI environments, agreement among multiple computers is essential for power generation, transmission, distribution, and consumption etc. applications. Coordinated control and security protection play important roles in the application of a distributed computing environment. A consensus approach can provide fault tolerance and prevent system errors and attacks, thereby providing the system with strong security in distributed computing environment. In this paper, a distributed Kalman filter with consensus coordinated control algorithm for multiagent systems (MAS) was proposed to investigate the energy internet with links failure by cyber attack. The proposed algorithm is evaluated in the energy internet through a MAS model using MATLAB software.

关键词： multiagent systems consensus energy internet

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An information divergence based approach to detect flooding DDoS attacks and Flash Crowds 3

An information divergence based approach to detect flooding ...

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3rd international conference on Applied and Theoretical computing and Communication technology (iCATccT)

作者： Kaur, Gursharanjeet Behal, Sunny Shifali SBS State Technical Campus Ferozepur Punjab India Ferozepur College of Engineering and Technology Ferozepur Punjab India

ISBN: (纸本)9781538611449

distributed Denial of Service (DDoS) attacks poses a severe threat to widely used internet-based services and applications. Currently, during in the technology era, the internet is the only real exigent that delivers a glut of services like businesses, banking, communication, education, e-commerce, etc. distributed Denial of Service attack aims to deny access by legitimate users to shared services or resources. Because of the vulnerability of the initial style of the web, attackers can merely mimic the patterns of legitimate network traffic. The prevailing fingerprint or feature-based methods are not appropriate to detect a recent DDoS attack. In this paper our goal is to detect a DDoS attack as well as Flash Crowd using various information distance measures such as Kullbeck-Leibler, Hellinger and Manhattan distance to measure the flow similarity among the traffic. Further the detection parameters such as Detection Rate, Classification Rate, Precision Rate and False Positive Rate is computed and shows that the Kullbeck-Leibler detection metric effectively detects the attack as compared to the other two.

关键词： DDoS attack Flash crowd distance measurement Flow similarity

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