• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.262-264
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• 2023

Internet of Things (IoT) networks currently employ an increased number of users and applications, raising their susceptibility to cyberattacks and data breaches, and endangering our security and privacy. Intrusion detection, which includes monitoring and analyzing incoming and outgoing traffic to detect and prohibit the hostile activity, is critical to ensure cybersecurity. Conventional intrusion detection systems (IDS) are centralized, making them susceptible to cyberattacks and other relevant privacy issues because all the data is gathered and processed inside a single entity. This research aims to create a blockchain-based architecture to support federated learning and improve cybersecurity and intrusion detection in IoT networks. In order to assess the effectiveness of the suggested approach, we have utilized well-known cybersecurity datasets along with centralized and federated machine learning models.

• Smart Media Journal
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• v.12 no.10
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• pp.9-18
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• 2023

Harvest forecasting is the great demand of multiple aspects like temperature, rain, environment, and their relations. The existing study investigates the climate conditions and aids the cultivators to know the harvest yields before planting in farms. The proposed study uses federated learning. In addition, the additional widespread techniques such as bagging classifier, extra tees classifier, linear discriminant analysis classifier, quadratic discriminant analysis classifier, stochastic gradient boosting classifier, blending models, random forest regressor, and AdaBoost are utilized together. These presented nine algorithms achieved exemplary satisfactory accuracies. The powerful contributions of proposed algorithms can create exact harvest forecasting. Ultimately, we intend to compare our study with the earlier research's results.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.151-153
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• 2023

Federated learning (FL)-based network intrusion detection techniques have enormous potential for securing the Industrial Internet of Things (IIoT) cybersecurity. The openness and connection of systems in smart industrial facilities can be targeted and manipulated by malicious actors, which emphasizes the significance of cybersecurity. The conventional centralized technique's drawbacks, including excessive latency, a congested network, and privacy leaks, are all addressed by the FL method. In addition, the rich data enables the training of models while combining private data from numerous participants. This research aims to create an FL-based architecture to improve cybersecurity and intrusion detection in IoT networks. In order to assess the effectiveness of the suggested approach, we have utilized well-known cybersecurity datasets along with centralized and federated machine learning models.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.1-9
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• 2024

Although federated learning is designed to be safer than centralized methods in terms of security and privacy, it still has many vulnerabilities. An attacker performing an adversarial attack intentionally manipulates the deep learning model by injecting carefully crafted input data, that is, adversarial examples, into the client's training data to induce misclassification. A common defense strategy against this is so-called adversarial training, which involves preemptively learning the characteristics of adversarial examples into the model. Existing research assumes a scenario where all clients are under adversarial attack, but considering the number of clients in federated learning is very large, this is far from reality. In this paper, we experimentally examine aspects of adversarial training in a scenario where some of the clients are under attack. Through experiments, we found that there is a trade-off relationship in which the classification accuracy for normal samples decreases as the classification accuracy for adversarial examples increases. In order to effectively utilize this trade-off relationship, we present a method to perform adversarial training by adaptively selecting a loss function depending on whether the client is attacked.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.76-90
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• 2024

Although artificial intelligence (AI) can be utilized in various domains such as smart city, healthcare, it is limited due to concerns about the exposure of personal and sensitive information. In response, the concept of distributed machine learning has emerged, wherein learning occurs locally before training a global model, mitigating the concentration of data on a central server. However, overall learning phase in a collaborative way among multiple participants poses threats to data privacy. In this paper, we systematically analyzes recent trends in privacy protection within the realm of distributed machine learning, considering factors such as the presence of a central server, distribution environment of the training datasets, and performance variations among participants. In particular, we focus on key distributed machine learning techniques, including horizontal federated learning, vertical federated learning, and swarm learning. We examine privacy protection mechanisms within these techniques and explores potential directions for future research.

• Smart Media Journal
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• v.10 no.1
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• pp.55-62
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• 2021

We applied machine learning of semi-supervised learning, transfer learning, and federated learning as examples of AI use cases that can be applied to the three major industries(Automobile industry, Energy industry, and AI/Healthcare industry) of Gwangju Metro-city, and established an ML strategy for AI services for the major industries. Based on the ML strategy of AI service, practical approaches are suggested, the semi-supervised learning approach is used for automobile image recognition technology, and the transfer learning approach is used for diabetic retinopathy detection in the healthcare field. Finally, the case of the federated learning approach is to be used to predict electricity demand. These approaches were tested based on hardware such as single board computer Raspberry Pi, Jaetson Nano, and Intel i-7, and the validity of practical approaches was verified.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.405-416
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• 2022

Current AI technology is improving the quality of life by using machine learning based on data. When using machine learning, transmitting distributed data and collecting it in one place goes through a de-identification process because there is a risk of privacy infringement. De-identification data causes information damage and omission, which degrades the performance of the machine learning process and complicates the preprocessing process. Accordingly, Google announced joint learning in 2016, a method of de-identifying data and learning without the process of collecting data into one server. This paper analyzed the effectiveness by comparing the difference between the learning performance of data that went through the de-identification process of K anonymity and differential privacy reproduction data using actual financial data. As a result of the experiment, the accuracy of original data learning was 79% for k=2, 76% for k=5, 52% for k=7, 50% for 𝜖=1, and 82% for 𝜖=0.1, and 86% for Federated learning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4280-4300
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• 2017

The fact that cloud computing services have been proposed in recent years, organizations and individuals face with various challenges and problems such as how to migrate applications and software platforms into cloud or how to ensure security of migrated applications. This study reviews the current challenges and open issues in cloud computing, with the focus on autonomic resource management especially in federated clouds. In addition, this study provides recommendations and research roadmaps for scientific activities, as well as potential improvements in federated cloud computing. This survey study covers results achieved through 190 literatures including books, journal and conference papers, industrial reports, forums, and project reports. A solution is proposed for autonomic resource management in the federated clouds, using machine learning and statistical analysis in order to provide better and efficient resource management.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.582-584
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• 2023

연합학습(Federated Learning)에서는 여러 참가자가 서로 간의 데이터를 공유하지 않고 협력하여 하나의 모델을 학습할 수 있다. 그 중 수직 연합학습(Vertical Federated Learning)은 참가자 간에 동일한 샘플에 대해 서로 다른 특성(Feature)를 가지고 학습한다. 또한 서로 다른 특성(Feature)에는 입력의 라벨(Label)도 포함하기 때문에 라벨을 소유한 참가자 외에는 라벨 정보 또한 접근할 수 없다. 이처럼 다양한 참가자가 학습에 참여하는 경우 악의적인 참가자에 의해 모델이 포이즈닝 될 여지가 존재함에도 불구하고 수직 연합학습에서는 관련 연구가 부족하다. 포이즈닝 공격 중 백도어 공격은 학습 과정에 관여하여 특정 입력 패턴에 대해서 모델이 공격자가 원하는 타겟 라벨로 예측하도록 오염시키는 공격이다. 수직 연합학습에서는 참가자가 학습과 추론 모든 과정에서 관여하기 때문에 백도어 공격에 취약할 수 있다. 본 논문에서는 수직 연합학습에서의 최신 백도어 공격과 한계점에 대해 분석한다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1055-1065
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• 2023

Recently, various studies using deep reinforcement learning (deep RL) technology have been conducted to solve complex problems using big data collected at industrial internet of things. Deep RL uses reinforcement learning"s trial-and-error algorithms and cumulative compensation functions to generate and learn its own data and quickly explore neural network structures and parameter decisions. However, studies so far have shown that the larger the size of the learning data is, the higher are the memory usage and search time, and the lower is the accuracy. In this study, model-agnostic learning for efficient federated deep RL was utilized to solve privacy invasion by increasing robustness as 55.9% and achieve 97.8% accuracy, an improvement of 5.5% compared with the comparative optimization-based meta learning models, and to reduce the delay time by 28.9% on average.