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Found 9 papers, 2 papers with code
Sentinel: An Aggregation Function to Secure Decentralized Federated Learning
The rapid integration of Federated Learning (FL) into networking encompasses various aspects such as network management, quality of service, and cybersecurity while preserving data privacy.
CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation
Recent research has shown that the integration of Reinforcement Learning (RL) with Moving Target Defense (MTD) can enhance cybersecurity in Internet-of-Things (IoT) devices.
RCVaR: an Economic Approach to Estimate Cyberattacks Costs using Data from Industry Reports
Therefore, managing risk exposure and cybersecurity strategies is essential for digitized companies that want to survive in competitive markets.
RansomAI: AI-powered Ransomware for Stealthy Encryption
Cybersecurity solutions have shown promising performance when detecting ransomware samples that use fixed algorithms and encryption rates.
FederatedTrust: A Solution for Trustworthy Federated Learning
Subsequently, an algorithm named FederatedTrust is designed based on the pillars and metrics identified in the taxonomy to compute the trustworthiness score of FL models.
RL and Fingerprinting to Select Moving Target Defense Mechanisms for Zero-day Attacks in IoT
Reinforcement Learning (RL) could be an effective approach to optimize the MTD selection through trial and error, but the literature fails when i) evaluating the performance of RL and MTD solutions in real-world scenarios, ii) studying whether behavioral fingerprinting is suitable for representing SBC's states, and iii) calculating the consumption of resources in SBC.
Analyzing the Robustness of Decentralized Horizontal and Vertical Federated Learning Architectures in a Non-IID Scenario
However, there is a lack of work evaluating the robustness of decentralized vertical FL and comparing it with horizontal FL architectures affected by adversarial attacks.
A Lightweight Moving Target Defense Framework for Multi-purpose Malware Affecting IoT Devices
Malware affecting Internet of Things (IoT) devices is rapidly growing due to the relevance of this paradigm in real-world scenarios.
Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors
Device fingerprinting combined with Machine and Deep Learning (ML/DL) report promising performance when detecting cyberattacks targeting data managed by resource-constrained spectrum sensors.
