Accurate threat hunting in industrial internet of things edge devices

Yazdinejad, Abbas; Zolfaghari, Behrouz; Dehghantanha, Ali; Karimipour, Hadis; Srivastava, Gautam; Parizi, Reza M.

doi:10.1016/j.dcan.2022.09.010

Cited by 32 publications

(18 citation statements)

References 31 publications

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“…FEC enables the secure and efficient sharing of data between multiple devices, improving the accuracy of predictive models and helping to prevent physical system failures. In addition, its scalability, flexibility, and ability to collaborate in real-time make it an invaluable tool for protecting critical infrastructure [38,39].…”

Section: Federated Learningmentioning

confidence: 99%

Toward Efficient Protecting Cyber-Physical Systems with Cyber Threat Hunting and Intelligence

Khordadpour¹

2023

Preprint

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Section: Federated Learningmentioning

confidence: 99%

Toward Efficient Protecting Cyber-Physical Systems with Cyber Threat Hunting and Intelligence

Khordadpour¹

2023

Preprint

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“…Limited research has considered privacy-preserving issues when applying ML in blockchain-based environments [45], [46], [47]. These studies primarily…”

Section: Litreture Reviewmentioning

confidence: 99%

“…Firstly, it eliminates the need to share raw data among different parties, thus enhancing data privacy. By focusing on the exchange of local gradients of raw data, federated learning minimizes the risk of data breaches and unauthorized access to sensitive information [44], [45], [47].…”

Section: Privacy-preserving Threat Detection In a Blockchain-based En...mentioning

confidence: 99%

Efficient Privacy-Preserving Framework for Federated and Blockchain Networks

Khordadpour¹

2023

Preprint

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<p>Machine learning (ML) models, at the core of Artificial intelligence (AI), are widely applied in our digital world. To build these models, huge amounts of data must be collected, and many assets must be protected under privacy law. Data privacy is a critical issue when training and testing ML models. For privacy concerns to be adequately addressed in today’s ML systems, there needs to be considered privacy gaps in ML, as trained ML models can be vulnerable to adversarial attacks. In this regard, federated learning and blockchain networks are the new paradigms that have emerged with the promise of privacy-preserving by design while utilizing ML models. The new paradigms have promising privacy-preserving potential; however, they neglect several fundamental privacy and security issues the fact that adversaries can exploit shared gradients and global parameters, and the parameter server may drop gradients that have been mistakenly or deliberately updated. Also, enough data is available to train models. The proposed research addresses privacy concerns in federated and distributed environments. This is a general overview to establish a privacy-preserving framework that can provide privacy in federated and blockchain-based networks while address to fundamental privacy and security issues.</p>

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“…As a result of the advancements in these technologies, there are also additional cyber security threats and vulnerabilities that can be exploited to control on-field actuators, sensors, and autonomous vehicles such as tractors and drones [1,21]. The potential demonstrates the severity of these threats for exploits that could destroy an entire field of crops or greenhouse by altering environmental parameters [2,22,23].…”

Section: Introductionmentioning

confidence: 99%