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distributed BERT-based Detection systems for IoT networks Against Coordinated Bot Attacks

Distributed BERT-based Detection Systems for IoT Networks Ag...

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international conference on ent Computing and Control systems (ICICCS)

作者： Ayesha Siddhika A Balamurugan P Logeshwaran C K T Subalaxmi Computer Science and Engineering K.S. Rangasamy College of Technology India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

The fast development of Web of Things (IoT) systems has made botnet attacks, such as dissent of benefit (DDoS), gadget verification, and information cancelation, a genuine security opportunity. These attacks misuse vulnerabilities in IoT gadgets to disturb administration and uncover individual data. This paper presents a bot attack strategy utilizing the deep learning demonstrated by BERT (Bidirectional Encoder Representation with Transformers), which is broadly recognized for its viability in translating troublesome data. By mimicking IoT communication designs, BERT can separate between pernicious and authentic client behaviors. By utilizing computations for information handling zones and coordination with electronic gadgets for real-time checking, the arranging preparation brings down idleness and upgrades reaction time. Tests conducted tentatively illustrate that the framework can precisely distinguish attacks, ensuring the IoT networks' steadfastness and security. This strategy offers an adaptable way to bargain with changing issues within the IoT environment.

关键词： Deep learning Translation Bidirectional control Chatbots Transformers Solids Encoding security Internet of Things computer crime

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A Privacy-Preserving computer-Aided Diagnosis Framework for Medical Applications Using Federated Learning and Homomorphic Encryption 7th

A Privacy-Preserving Computer-Aided Diagnosis Framework for...

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7th international conference on Attacks and Defenses for Internet-of-Things, ADIoT 2024

作者： Xiong, Jichao Chen, Jiageng Liu, Hui Zhou, Guangyou Cui, Jianqun Lin, Junyu Jiao, Dian School of Computer Science and Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University Wuhan430079 China School of Central China Normal University Wollongong Joint Institute Central China Normal University Wuhan430079 China

ISBN: (纸本)9783031855924

Skin cancer is one of the most common cancers worldwide, where early detection significantly reduces mortality rates. Integrating Internet of Things (IoT) technologies into healthcare enables continuous monitoring and early diagnosis through connected devices, offering new prospects for real-time medical assessment. Advanced machine learning classifiers have shown superior performance over human experts in diagnosing pigmented skin lesions, underscoring their potential for IoT-based applications. However, training high-precision computer-aided diagnosis (CAD) systems requires extensive data, and secure data sharing poses significant challenges due to privacy concerns in IoT healthcare networks. This paper introduces an IoT-oriented skin cancer CAD system combining federated learning and homomorphic encryption to address these issues. Federated learning supports collaborative model training across distributed IoT devices, mitigating data scarcity without compromising data privacy. Homomorphic encryption ensures that patient data remains encrypted during diagnosis, enhancing security within IoT frameworks. To achieve an efficient and high-accuracy model suited for IoT, we implemented Self-Learnable Activation Functions (SLAF), optimized for homomorphic encryption and resource constraints of IoT devices. Our system, validated using the HAM10000 dataset, achieved 94.39% accuracy with dual privacy protection, enhancing both diagnostic precision and data security. Comprehensive evaluations and comparisons with state-of-the-art frameworks confirmed the effectiveness of our approach. Results demonstrate the strong potential and practicality of our IoT-based solution for real-world healthcare, providing a secure, efficient, and accurate diagnostic tool that supports the wider adoption of IoT in medical applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Detection of Anomalous Cryptocurrency Transactions Using Neural networks with Decision-making Explanation

Detection of Anomalous Cryptocurrency Transactions Using Neu...

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Euromicro conference on Parallel, distributed and Network-Based Processing

作者： Dmitry Levshun Ksenia Zhernova Andrey Chechulin St. Petersburg Federal Research Center of the Russian Academy of Sciences Saint Petersburg Russia

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

The objective of this paper is to explore an effective approach for detecting anomalies in cryptocurrency transactions using artificial intelligence techniques. The proposed method is compared with existing approaches for identifying illegal transactions within cryptocurrency networks. This research systematically analyzes various data processing methods applicable to the problem of detecting illicit transactions, as well as reviewing relevant studies in cryptocurrency transaction analysis, data visualization, and computer vision-based anomaly detection. Experimental evaluations on a cryptocurrency transaction dataset show significant insights, with results benchmarked against those obtained in prior studies on the same dataset (BABD). The findings suggest that the proposed method, utilizing statistical graph characteristics, deep and ensemble learning technologies, enhances the accuracy of identifying fraudulent transactions. This approach could be instrumental in developing software solutions for monitoring cryptocurrency transactions, potentially aiding in digital forensics and cybersecurity applications. The findings may benefit researchers in computer security and developers focused on information security systems.

关键词： Adaptation models Accuracy Sensitivity Neural networks Digital forensics Information security Cryptocurrency Fraud Ensemble learning Anomaly detection

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New Cloud Computing Authentication Based on Secure Hash Algorithm (SHA-3) and Lightweight Sosemanuk Algorithm 2nd

New Cloud Computing Authentication Based on Secure Hash Algo...

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2nd international conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Niaf, Jolan Rokan Kadhim, Aqeel Kamil Mohammed, Qutaiba Humadi Hoomod, Haider K. Salman, Muna Muhsen Iraqi Commission for Computers & Informatics Informatics Institute for Postgraduate Studies Baghdad Iraq Computer Techniques Engineering Department Imam Al-Kadhim College Baghdad Iraq College of Nursing University of Baghdad Baghdad Iraq College of Education Al-Mustansiriyah University Baghdad Iraq Ministry of Science and Technology Department of Information Technology Baghdad Iraq

ISBN: (纸本)9789819776023

The term "cloud computing" refers to a vast network of linked, virtually hosted, and easily updatable computer resources. That are capable of consisting of a number of different apps and offering a number of different services to customers, and that have the potential to do so. Customers are required to pay for the cost of using the Services in accordance with the company's "pay-for-use only" policy. However, there are certain firms, such as Google Inc, that provide customers with particular free storage space even though the services provided by the company are limited, specifically with regard to the public cloud. A third-party service provider frequently owns and maintains the infrastructure required to run cloud computing services. But because more and more data and files pertaining to individuals and businesses are being stored in the cloud, worries have started to grow about how safe it actually is. Among these are the protection of software for virtualization and distributed computing, the safety of application software, the administration of identities, and the authentication and restriction of access. Nevertheless, efficient user authentication is the most essential need of cloud computing, as it limits unauthorized usage of a cloud server. Cloud computing has become increasingly popular in recent years. In this research, we present a new authentication method between users and online computing systems that is based on the suggested secure hash algorithm (SHA-3) and makes use of the lightweight Sosemanuk method. We created this technique. That system is going to be known as the SoSha3 system. The design that has been proposed for the system deals with data in three different sizes: 128, 256, and 512 bits. In order to make these authentication systems suitable for cloud computing at high rates, some modifications were made to them. The three-dimensional chaotic system, also known as the Lorenz system, will be combined with the authentication architecture tha

关键词： Cloud security

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A Cutting-Edge Method on Big Data Analytics Recent Developments for Business Operations and security Management 6th

A Cutting-Edge Method on Big Data Analytics Recent Developme...

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6th international conference on Smart Computing and Informatics, SCI 2024

作者： Razzhivin, Oleg Anatolevich Ollanazarov, Bekmurod Davlatmuratovich Okagbue, Hilary Izuchukwu Andryushchenko, Iryna Evgenievna Department of Theory and Methods of Physical Culture and Life Safety of Elabuga Institute Kazan Federal University Kazan Russian Federation Department of Tourism Urgench State University Urgench Uzbekistan Sydani Institute for Research and Innovation Sydani Group Abuja Nigeria Department of Mathematics Covenant University Ota Nigeria Khorezm University of Economics Urgench Uzbekistan Mamun University Khiva Uzbekistan Department of Finance Banking and Insurance National University “Zaporizhzhia Polytechnic” Zaporizhzhia Ukraine

ISBN: (纸本)9789819619801

The management of a corporation could be significantly enhanced by the use of big data. There has not been enough statistical investigation to determine the importance of large data in the malignancy of the highly functional and methodical investigations. Extremely massive data compilations featuring a more intricate and diversified structure are referred to as big data. These traits usually lead to more challenges when it comes to data extraction, analysis, storing, and further operations. “Big data analytics” is the process of examining enormous volumes of complicated information to uncover hidden patterns or connections. However, there is an obvious conflict between the expanding usage of big data as well as its safety and confidentiality. It addresses the most crucial elements of computer network management and organization, including privacy, in order to meet the demanding security standards of big data applications. Since more and more individuals share personal data and materials on social networks as well as public cloud services via their PCs and smartphones, it is crucial to find a solution to this problem. Consequently, creating a secure foundation for social networks has become a hot research subject. One of each of the case study sections in this chapter deals with this last subject. In addition, Big Data computing systems are not appropriate for the use of conventional security measures like firewalls and demilitarized areas. But if information is not sufficiently protected against threats like phishing and hacking. These difficulties include vulnerabilities in open databases;safeguards against hacking and data leakage;and so on. When managing large-scale, dispersed data sets, safeguarding and privacy requirements provide a serious challenge to monitoring and tracking data use and access in a dynamic, distributed context. This effort aims to investigate the issues that arise in attempting to maintain the security and privacy of large amounts of data, a

关键词： Big data Cloud Operation management Privacy security Social applications

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Proposing a novel approach based on GAN neural networks to address data poisoning attacks in federated learning 29

Proposing a novel approach based on GAN neural networks to a...

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29th international computer conference, computer Society of Iran, CSICC 2025

作者： Rahmat, Mohammad Ghabel Zarafshan, Faraneh Department of Computer Engineering Karaj Branch Islamic Azad University Karaj Iran

ISBN: (纸本)9798331523114

Data poisoning attacks are one of the serious threats in federated learning, which can disrupt the performance of the central model by injecting poisoned data into local nodes. These attacks are particularly significant in cloud environments, where distributed and decentralized data are often managed, due to the increasing need for data security. Identifying and isolating poisoned data from clean data in local nodes is a fundamental step toward enhancing the security and stability of these systems. In this paper, a method based on Generative Adversarial networks (GANs) is proposed, which can act as an effective tool for identifying poisoned samples. In the proposed method, the GAN discriminator’s task shifts from solving a binary problem, as is standard in GANs, to identifying the true label of data in a multi-class problem. During the GAN training phase, the semantic information of the data, including the true labels, is incorporated into the discriminator. To evaluate the proposed method, experiments were conducted on the standard MNIST dataset. Compared to standard GANs, the proposed method showed better performance for classes where label-flipping data poisoning was applied. These results demonstrate the effectiveness and applicability of the proposed method in various federated learning environments. The presented method can be used as a solution to enhance the security of data and models in federated learning and to identify the true labels of data in cases where there is no prior information about the occurrence of an attack. © 2025 IEEE.

关键词： Data Poisoning Attacks distributed Machine Learning Federated Learning GAN Neural Network Multi-Class Discriminator

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Function Scheduling with Data security in Serverless Computing systems

Function Scheduling with Data Security in Serverless Computi...

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international Communication systems and networks and Workshops, COMSNETS

作者： Srestha Saha Arvind Pandey Sourav Kanti Addya Shubha Brata Nath Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati India Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu India Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

In serverless computing, the service provider takes full responsibility for function management. However, serverless computing has many challenges regarding data security and function scheduling. To address these challenges, we have proposed a system to secure the data of an end-user. We also aim to meet the quality of service (QoS) for the end-user requests. This work presents a Simulated Annealing-based optimization algorithm for function placement. Also, we have Hyperledger Fabric, a blockchain framework in the system architecture for securing the data of an end-user. We have conducted experiments in Amazon Elastic Compute Cloud (EC2) taking virtual machine instances. The experiments in Amazon EC2 indicate that the proposed system secures the data and enhances the end-user’s QoS.

关键词： Processor scheduling distributed ledger Data security Serverless computing systems architecture Quality of service Simulated annealing Virtual machines Fabrics Time factors

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A Survey on Intelligent Attack Mitigation and Latency Optimization in High-Speed Edge networks using Deep Learning and Bio-Inspired Algorithms

A Survey on Intelligent Attack Mitigation and Latency Optimi...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： Gandham Kiran Kumar M. Thirumaran Department of Computer Science and Engineering Puducherry Technological University Puducherry India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

As the Internet connected devices and cyber– physical systems (CPS) are exponentially increasing, edge computing has become a crucial enabler to support real time processing in high-speed networks (HSNs). However, integrating edge computing into broadband infrastructure is a challenging problem and that includes mitigating security risks such as DDoS and time-delay attacks and keeping ultra-low latency. Explored for this survey are some recent technologies on how to detect attacks and optimize latency on the edge, focusing on forms of deep learning and bioinspired approaches. It analyzes the lack of effectiveness in traditional mitigation strategies and represents new means, based on the use of ensemble learning, dimensionality reduction and hybrid models. distributed edge infrastructures are specially emphasized on real time implementation, computational overhead and security. This paper attempts to contribute valuable insight for researchers developing robust, scalable, and intelligent edge-based solutions in high-speed networking environments by consolidating recent methodologies and their outcomes.

关键词： Surveys Deep learning High-speed networks Image edge detection Prevention and mitigation Focusing Real-time systems security Optimization Edge computing

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distributed Primal-Dual Interior Point Framework for Analyzing Infeasible Combined Transmission and Distribution Grid networks 58

Distributed Primal-Dual Interior Point Framework for Analyzi...

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58th Hawaii international conference on System Sciences, HICSS 2025

作者： Ali, Muhammad Hamza Pandey, Amritanshu University of Vermont United States

ISBN: (纸本)9780998133188

The proliferation of distributed energy resources has heightened the interactions between transmission and distribution (T&D) systems, necessitating novel analyses for the reliable operation and planning of interconnected T&D networks. A critical gap is an analysis approach that identifies and localizes the weak spots in the combined T&D networks, providing valuable information to system planners and operators. The research goal is to efficiently model and simulate infeasible (i.e. unsolvable in general settings) combined positive sequence transmission and three-phase distribution networks with a unified solution algorithm. We model the combined T&D network with the equivalent circuit formulation. To solve the overall T&D network, we build a Gauss-Jacobi-Newton (GJN) based distributed primal dual interior point optimization algorithm capable of isolating weak nodes. We validate the approach on large combined T&D networks with 70k+ T and 15k+ D nodes and demonstrate performance improvement over the alternating direction method of multipliers (ADMM) method. © 2025 IEEE computer Society. All rights reserved.

关键词： Combined T&D networks distributed optimization grid planning infeasibility analysis primal-dual interior point

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Advanced Machine Learning Approach for Cyber Attack Detection in Cloud Computing

Advanced Machine Learning Approach for Cyber Attack Detectio...

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2025 international conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Yadav, Shashi Bhushan Singh Shelke, Nitin Arvind Singh, Jagendra Yasmeen Pandey, Deependra Himanshi Bbd Northern India Institute of Technology Department Computer Science & Engineering Lucknow India School of Computer Science Engineering & Technology Bennett University Greater Noida India Cvr College of Engineering Department of CSE-DS Hyderabad India Amity School of Engineering and Technology Amity University Department of Ece Lucknow India Raj Kumar Goel Institute of Technology Department of Information Technology Ghaziabad India

ISBN: (纸本)9798331527495

Cloud computing has become the most important part of modern IT infrastructure, which is scalable and flexible. However, with such wide-scale adoption, new vulnerabilities have been introduced into the system, making it a favorite target for cyber-attacks, especially distributed Denial-of-Service (DDoS) and Man-in-the-Cloud (MitC) attacks. security measures in cloud environments are more than ever required to be effective. This study introduces a state-of-the-art approach using machine learning techniques to identify and prevent such attacks;the four machine learning algorithms that have been used here are Support Vector Machine, Artificial Neural Network, Recurrent Neural Network, and k-Nearest Neighbors. A dataset of 3,200 simulated records has been used for the purpose of training and testing;70% of data were used for training purposes, and 30% of data were used for testing purposes. The models were rated based on precision, running time, and memory usage. The results highlighted that SVM was the strongest model and had an accuracy of 97.86%, while ANN was about 93.4% followed by RNN which was about 89.45% and KNN at 86.7%. This would mean that among the above models, the SVM is the most efficient with respect to real-time attack detection as it is in high precision and has efficiency in resource use. The proposed approach can be very well applied to real-time cloud security systems, offering a robust solution in identifying and mitigating cyber threats in dynamic cloud environments, ensuring enhanced protection against evolving attack vectors. © 2025 IEEE.

关键词： Recurrent neural networks

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