Secure Collaborative Deep Learning Against GAN Attacks in the Internet of Things

Chen, Zhenzhu; Fu, Anmin; Zhang, Yinghui; Liu, Zhe; Zeng, Fanjian; Deng, Robert H.

doi:10.1109/jiot.2020.3033171

Cited by 26 publications

(10 citation statements)

References 25 publications

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“…Differential privacy [46] is a statistical disclosure control algorithm that can disturb each user's real data but still can have a relatively accurate statistical result from a group of people. To prevent GAN-based attacks, anti-GAN algorithms [47,48] are invented to add noise to users' different types of data to prevent fake data generation. The noise is invisible to people but will lead the GAN to generate fake data that are different from the real data.…”

Section: Privacy Issues and Solutionsmentioning

confidence: 99%

Security and Privacy in Metaverse: A Comprehensive Survey

Huang

Cai

2023

Big Data Min. Anal.

113

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Metaverse describes a new shape of cyberspace and has become a hot-trending word since 2021. There are many explanations about what Meterverse is and attempts to provide a formal standard or definition of Metaverse.However, these definitions could hardly reach universal acceptance. Rather than providing a formal definition of the Metaverse, we list four must-have characteristics of the Metaverse: socialization, immersive interaction, real world-building, and expandability. These characteristics not only carve the Metaverse into a novel and fantastic digital world, but also make it suffer from all security/privacy risks, such as personal information leakage, eavesdropping, unauthorized access, phishing, data injection, broken authentication, insecure design, and more. This paper first introduces the four characteristics, then the current progress and typical applications of the Metaverse are surveyed and categorized into four economic sectors. Based on the four characteristics and the findings of the current progress, the security and privacy issues in the Metaverse are investigated. We then identify and discuss more potential critical security and privacy issues that can be caused by combining the four characteristics. Lastly, the paper also raises some other concerns regarding society and humanity.

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Section: Privacy Issues and Solutionsmentioning

confidence: 99%

Security and Privacy in Metaverse: A Comprehensive Survey

Huang

Cai

2023

Big Data Min. Anal.

113

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“…Possible Solution: Secure multiparty computation or mechanisms for malicious participant detection are the primary defense for FL from GAN reconstruction. Chen et al proposed a mechanism to protect collaborative training against GAN reconstruction attacks using secure multiparty computation [90]. For this sake, the authors used an improved Du-Atllah scheme, a method for multiple parties to perform calculations without knowledge of the raw data [93].…”

Section: ) Gan Reconstruction Attackmentioning

confidence: 99%

A Survey on Securing Federated Learning: Analysis of Applications, Attacks, Challenges, and Trends

et al. 2023

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The growth of data generation capabilities, facilitated by advancements in communication and computation technologies, as well as the rise of the Internet of Things (IoT), results in vast amounts of data that significantly enhance the performance of machine learning models. However, collecting all necessary data to train accurate models is often unfeasible due to privacy laws. Federated Learning (FL) evolved as a collaborative machine learning approach for training models without sharing private data. Unfortunately, several in-design vulnerabilities have been exposed, allowing attackers to infer private data of participants and negatively impacting the performance of the federated model. In light of these challenges and to encourage the development of FL solutions, this paper provides a comprehensive analysis of secure FL proposals that both protect user privacy and enhance the performance of the model. We performed a systematic review using predefined criteria to screen and extract data from multiple electronic databases, resulting in a final set of studies for analysis. Through the systematic review methodology, the paper groups the security vulnerabilities of FL into model-performance and data privacy attacks. It also presents an analysis and comparison of potential mitigation strategies against these attacks. Additionally, the paper conducts a security analysis of state-of-the-art FL applications and proposals based on the vulnerabilities addressed. Finally, the paper outlines the main applications of secure FL and lists future research challenges. The survey highlights the crucial role of security strategies in ensuring the protection of user privacy and model performance in the context of future FL applications.

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“…Generative adversarial network (GAN) is a good example of hybrid deep learning. GAN has been adapted into the IoT environment for security purposes [ 98 , 99 ]. GAN may show improved success because it can learn different attack scenarios that are combined to generate samples similar to a zero-day attack scenario.…”

Section: Intrusion Detection System (Ids) In Iotmentioning

confidence: 99%

“…Implementing distributed anomaly-based IDSs can be investigated as a solution [ 12 , 14 , 15 , 24 , 60 , 69 , 71 , 72 , 74 , 75 , 76 , 77 , 81 , 82 , 99 ] to providing a less invasive IDS. Regarding detection techniques, such as the anomaly-based IDSs, the preparation and testing time needed to accomplish the normal behavior of networks is high.…”

Section: Conclusion and Future Directionmentioning

confidence: 99%

IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses

Albulayhi

Smadi

Sheldon

et al. 2021

Sensors

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This paper surveys the deep learning (DL) approaches for intrusion-detection systems (IDSs) in Internet of Things (IoT) and the associated datasets toward identifying gaps, weaknesses, and a neutral reference architecture. A comparative study of IDSs is provided, with a review of anomaly-based IDSs on DL approaches, which include supervised, unsupervised, and hybrid methods. All techniques in these three categories have essentially been used in IoT environments. To date, only a few have been used in the anomaly-based IDS for IoT. For each of these anomaly-based IDSs, the implementation of the four categories of feature(s) extraction, classification, prediction, and regression were evaluated. We studied important performance metrics and benchmark detection rates, including the requisite efficiency of the various methods. Four machine learning algorithms were evaluated for classification purposes: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and an Artificial Neural Network (ANN). Therefore, we compared each via the Receiver Operating Characteristic (ROC) curve. The study model exhibits promising outcomes for all classes of attacks. The scope of our analysis examines attacks targeting the IoT ecosystem using empirically based, simulation-generated datasets (namely the Bot-IoT and the IoTID20 datasets).

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Secure Collaborative Deep Learning Against GAN Attacks in the Internet of Things

Cited by 26 publications

References 25 publications

Security and Privacy in Metaverse: A Comprehensive Survey

Security and Privacy in Metaverse: A Comprehensive Survey

A Survey on Securing Federated Learning: Analysis of Applications, Attacks, Challenges, and Trends

IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses

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