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Federated Clustering Defense: Mitigating Data Exposure in Collaborative Learning

Federated Clustering Defense: Mitigating Data Exposure in Co...

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2025 international conference on Emerging Technologies for Intelligent systems, ETIS 2025

作者： Radhakrishnan, Anuja Krishna, Sathya Bama Sathyabhama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331507541

The development of intelligent industrial applications is being driven globally by advancements in the next generation of Internet of Things (IoT) technologies. At the same time, artificial intelligence (AI) technologies like machine learning and deep learning are becoming more widely used. Conventional machine learning models primarily rely on large data sets. However, the vast data sets generated by network-edge devices are expensive, inefficient to acquire and handle, and they pose major privacy hazards. Federated learning (FL) is a novel paradigm for statistical model training on distributed edge networks that allows data to take part in the training of federated models without being localized. Traditional machine learning issues with low data usage, data privacy, and information security brought on by data isolation can be resolved with this method. We propose a novel Federated Clustering with Differential Privacy (FedCDP) algorithm designed to efficiently model user behavior in an unsupervised manner. This algorithm focuses on optimizing computation and storage requirements while minimizing data leakage through robust differential privacy mechanisms. © 2025 IEEE.

关键词： Deep learning

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Attack Detection in Smart Home IoT networks: A Survey on Challenges, Methods and Analysis 14th

Attack Detection in Smart Home IoT Networks: A Survey on Cha...

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14th EAI international conference on Broadband Communications, networks, and systems, BROADNETS 2024

作者： Rreddy, M. Vinay Kuma Lathigara, Amit Reddy, Muthangi Kantha RK University Gujarat India Computer Engineering RK University Gujarat India Shri Vishnu Engineering College for Women Andhra Pradesh India

ISBN: (纸本)9783031811678

The ubiquity of Internet of Things (IoT) gadgets in smart homes has transformed our interactions with our living environments by providing never-before-seen levels of automation and convenience. However, because IoT devices are becoming possible targets for malicious attacks, this broad connectivity also poses serious security risks. Ensuring the privacy, safety, and integrity of smart home ecosystems requires prompt detection and mitigation of these threats. Data from IoT devices is gathered, pre-processed, feature engineered, labelled, and divided into training, validation, and testing sets as part of a machine learning method to threat detection in smart home IoT networks. The process of choosing and training appropriate machine learning models—which can include everything from classification techniques to anomaly detection algorithms—is crucial. Methods are surveyed to review different types of cyber-attacks, such as denial-of-service (DoS), distributed denial-of-service (DDoS), probing, user-to-root (U2R), remote-to-local (R2L), botnet attack, spoofing, and man-in-the-middle (MITM) attacks. To protect user information, data anonymization and encryption techniques are used with privacy considerations. Another strategy that has been put forth aims to improve the security of IoT networks in smart homes by providing a strong defence against new threats and equipping users with the information and resources they need to keep their connected world safe. To provide a full overview of the numerous advancements in this field, a list of all works published in the literature to date is incorporated. Lastly, the study also includes suggestions for future research directions. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Smart homes

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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： Federated learning

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Vulnerability Assessment of security Breach and Deadly Threat in Cloud Computing Environment 15

Vulnerability Assessment of Security Breach and Deadly Threa...

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15th IEEE international conference on Computational Intelligence and Communication networks, CICN 2023

作者： Gupta, Ashish Sinha, Shreya Singh, Harsh Kumar Bhushan, Bharat Sharda University Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350324433

Cloud computing allows us to access available systems and pay for what we require whenever needed. When there is access to the internet, it uses some techniques like Service-Oriented Architecture (SOA), virtualization, distributed computing, etc. Cloud computing has transformed the way people utilize and handle computer services. It enables sharing, pooling, and accessing resources on the Internet. It offers tremendous advantages that enhance the cost-effectiveness and efficiency of organizations, which is marked by security challenges or threats that can compromise data, service safety and privacy. This paper gives an overview of cloud computing and explores the threats and vulnerabilities related to cloud computing with its countermeasures. It also explores the recent advancement in cloud computing threats and countermeasures. Further, this paper highlights the case studies on recent attacks and vulnerabilities which are compromised. Finally, this paper concludes that cloud computing is efficiently used to mitigate the threats and vulnerabilities with its countermeasures. © 2023 IEEE.

关键词： Cost effectiveness

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A Survey on distributed Denial of Service Attack - Detection Mechanisms and Mitigation Mechanisms in the Cloud Environment 10

A Survey on Distributed Denial of Service Attack - Detection...

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10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and computer Engineering, UPCON 2023

作者： Kandikattu, Chris Harry Panguluri, Sam Sangeeth Kumar, Sandeep Bulla, Suneetha Shaik, Abdul Raheem Koneru Lakshmaiah Educational Foundations Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350382471

It is evident that problems associated with vast amounts of information occur with complicated systems and processes that act collaboratively to provide services. Also, other related issues such as security problems, cost optimization and confinement, deficiency of Assets or expertise, governance or supervision, conformity, handling several clouds, and many others have left a serious threat within the cloud environment. It can be framed that the voluminous data transmitted between various organizations and cloud service providers is a channel to induce such a threat. These threats can mainly be estimated as unsolicited and malicious sensitive data leaks to countless third-party bodies. Also termed as distributed Denial-of-Service (DDoS), there are these situations wherein a targeted server's traffic is disturbed by creating some internet traffic within its surroundings. According to a survey conducted by Arbor networks, it is revealed that roughly 1200 DDoS attacks occur every other day. Therefore, this paper aims to offer a comprehensive analysis of the DDoS attack within the cloud setting, including strategies for identifying and mitigating it. © 2023 IEEE.

关键词： Cloud attacks Cloud environment Cloud security DDoS

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The Smart Development of Multichip Communication systems with The Help of Short Transmission Nodes 2

The Smart Development of Multichip Communication Systems wit...

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2nd IEEE international conference on distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Sunil, M.P. Devaraj Verma, C. Department of Electronics and Communication Engineering Bangalore India Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

Multichip communication systems offer faster and more efficient data transmission and processing than single-chip systems. Multichip communication systems are made up of multiple chips that are interconnected and communicate with each other to carry out tasks more quickly. By using multiple chips, more complex operations can be completed faster and more efficiently than if done by a single processor. Multichip communication systems are used in many different industries, from automotive to aerospace to consumer electronics. Short transmission nodes are nodes that are used to transfer data from one network to another, usually over shorter distances. They are often used in the networking of computers, as they allow data to be sent more quickly and securely than over long distances. Short transmission nodes are also frequently used in wireless networks, where they provide a more reliable connection than longer distance nodes. In general, short transmission nodes are designed to be more efficient than longer distance nodes, allowing for faster data transfer speeds and more reliable connections. They are also often used for security purposes, as they are harder to intercept than signals passing over longer distances. © 2023 IEEE.

关键词： Data transfer

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26th international Symposium on Stabilization, Safety, and security of distributed systems, SSS 2024

26th International Symposium on Stabilization, Safety, and S...

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26th international Symposium on Stabilization, Safety, and security of distributed systems, SSS 2024

ISBN: (纸本)9783031744976

The proceedings contain 33 papers. The special focus in this conference is on Stabilization, Safety, and security of distributed systems. The topics include: Invited Paper: Gathering Oblivious Robots in the Plane;invited Paper: The Smart Contract Model;invited Paper: Using Signed Formulas for Online Certification;optimal Asynchronous Perpetual Grid Exploration;gathering of Robots in Butterfly networks;brief Announcement: Pebble Guided Rendezvous Despite Fault;complete Graph Identification in Population Protocols;efficient Self-stabilizing Simulations of Energy-Restricted Mobile Robots by Asynchronous Luminous Mobile Robots;brief Announcement: Perpetual Exploration of Triangular Grid by Myopic Oblivious Robots Without Chirality;an Optimal Algorithm for Geodesic Mutual Visibility on Hexagonal Grids;Coating in SILBOT with One Axis Agreement;rendezvous and Merging for Two Metamorphic Robotic systems Without Global Compass;gathering Semi-Synchronously Scheduled Two-State Robots;selective Population Protocols;partially Disjoint Shortest Paths and Near-Shortest Paths Trees;brief Announcement: Towards Proportionate Fair Assignment;BlindexTEE: A Blind Index Approach Towards TEE-Supported End-to-End Encrypted DBMS;tight Bounds for Constant-Round Domination on Graphs of High Girth and Low Expansion;adding All Flavors: A Hybrid Random Number Generator for dApps and Web3;SUPI-Rear: Privacy-Preserving Subscription Permanent Identification Strategy in 5G-AKA;anomaly Detection Within Mission-Critical Call Processing;brief Announcement: Make Master Private-Keys Secure by Keeping It Public;Selection Guidelines for Geographical SMR Protocols: A Communication Pattern-Based Latency Modeling Approach;byzantine Reliable Broadcast with One Trusted Monotonic Counter;brief Announcement: On the Feasibility of Local Failover Routing on Directed Graphs;TRAIL: Cross-Shard Validation for Byzantine Shard Protection;softening the Impact of Collisions in Contention Resolution;generating the Converge

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A Novel Private Encryption Model in IoT Under Cloud Computing Domain

A Novel Private Encryption Model in IoT Under Cloud Computin...

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Proceedings of the international conference on Intelligent systems and Network, ICISN 2023

作者： Yadala, Sucharitha Pundru, Chandra Shaker Reddy Solanki, Vijender Kumar Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology TS Hyderabad India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Department of Computer Science and Engineering CMR Institute of Technology Hyderabad India

ISBN: (纸本)9789819947249

The presence of the Internet of Things (IoT) works with the assortment and diffusion of metropolitan information data. In any case, it can release clients’ very own security data in brilliant urban areas. Many works have been done and different security systems identifying with the precautions of distributed computing have been implemented from copious points of view. Be that as it may, they don’t propose a quantitative way to deal with dissecting and assessing protection and security in distributed computing frameworks. Accordingly, we propose another private data encryption strategy in IoT under distributed computing climate. Below IoT, as per the properties and securing time, protection data can be isolated into numerous subspaces. Because of the stream figure instrument, we plan an encryption framework model of data assortment. In the subspace, the security data is encoded and moved to the transfer hub. Subsequent to encoding, they are divided and rebuilt. At long last, we use stream figure and dual-key calculation to finish opportunity nondestructive change among plaintext and ciphertext to guarantee the trustworthiness of the encoded private data. Test outcomes illustrate that the presented strategy requires some investment in the encryption and unscrambling measure, which has superior ciphertext transformation yield impact and experiences fewer organization assaults in a similar encryption time. As far as computation cost, the proposed strategy diminishes by roughly 11%. Furthermore, it has greater security and works on the security and integrity of the protection data assortment procedure. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cryptography

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distributed-computing based versatile healthcare services framework for diagnostic markers 2nd

Distributed-computing based versatile healthcare services fr...

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2nd international conference on Next-Generation Computing and Information systems, ICNGCIS 2023

作者： Chunduri, Venkata Posinasetty, Bhargavi Son, Mukesh Byeon, Haewon Department of Mathematics & Computer Science Indiana State University United States The University of Southern Mississippi HattiesburgMS United States Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Pune Tathawade India Department of Digital Anti-Aging Healthcare Inje University Gimhae Korea Republic of

ISBN: (纸本)9781032738659

Recently, there has been a boom in imaginative medical services technologies that have enhanced the delivery of medical care data. Human-to-human and gadget-to-gadget networks are crucial in people’s lives and function 24 hours a day, seven days a week. MobileCloud-Computing becomes a crucial tool in this regard. Given the tremendous improvements in the Internet of Things, such a combination has become a major worry. This study developed a framework that included two essential application components: flexible usage and a server request. The server application records patient data while a Counterfeit Neural Network (CNN) module distinguishes between the two stroke subtypes. Similarly, as a framework for stroke patients, our model ensures accessibility, security, and adaptability by utilising the Stroke dataset for CNN computation and the Multilayer Perceptron Algorithm (MLP), which has been completed without precedent for working with large amounts of data in this extension. © 2025 The Author(s).

关键词： Hospital data processing

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Securing networks: Leveraging Machine Learning for Enhanced DDoS Detection 1

Securing Networks: Leveraging Machine Learning for Enhanced ...

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1st international conference on Software, systems and Information Technology, SSITCON 2024

作者： Chauhan, Som Byahatti, Prajval Patel, Sunil Kumar Manipal University Jaipur Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350352931

The growing complexity of distributed Denial of Service (DDoS) attacks poses a serious threat to network security, making the development of effective detection and mitigation techniques essential. This study tackles this issue by introducing a novel approach that utilizes machine learning (ML) to improve the identification of DDoS attacks using realworld datasets. By leveraging the CSE-CIC-IDS2018 AWS dataset, we develop a strong classifier to examine network traffic patterns that suggest potential DDoS activity. Our approach encompasses comprehensive steps, including data collection, preprocessing, and the deployment of sophisticated ML algorithms. The outcomes reveal impressive performance, with the model achieving a 99.98 %accuracy rate and nearperfect precision and F1 scores. These results highlight the strength of neural network-based models, especially when implemented using advanced frameworks such as Keras. This research ultimately bolsters network defenses against DDoS attacks and emphasizes the necessity of ongoing innovation in detection techniques to protect digital resources and maintain uninterrupted online services in our increasingly digitalized environment. © 2024 IEEE.

关键词： Network intrusion

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