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Found 17 papers, 6 papers with code
A Big Data Architecture for Early Identification and Categorization of Dark Web Sites
The dark web has become notorious for its association with illicit activities and there is a growing need for systems to automate the monitoring of this space.
Mitigating Communications Threats in Decentralized Federated Learning through Moving Target Defense
A DFL scenario with physical and virtual deployments have been executed, encompassing three security configurations: (i) a baseline without security, (ii) an encrypted configuration, and (iii) a configuration integrating both encryption and MTD techniques.
RansomAI: AI-powered Ransomware for Stealthy Encryption
Cybersecurity solutions have shown promising performance when detecting ransomware samples that use fixed algorithms and encryption rates.
Fedstellar: A Platform for Decentralized Federated Learning
To overcome these challenges, this paper presents Fedstellar, a platform extended from p2pfl library and designed to train FL models in a decentralized, semi-decentralized, and centralized fashion across diverse federations of physical or virtualized devices.
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.
Adversarial attacks and defenses on ML- and hardware-based IoT device fingerprinting and identification
In contrast, attackers do not stay stalled and have developed adversarial attacks focused on context modification and ML/DL evaluation evasion applied to IoT device identification solutions.
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.
Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges
In recent years, Federated Learning (FL) has gained relevance in training collaborative models without sharing sensitive data.
Maximum Likelihood Distillation for Robust Modulation Classification
Deep Neural Networks are being extensively used in communication systems and Automatic Modulation Classification (AMC) in particular.
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.
Robust Federated Learning for execution time-based device model identification under label-flipping attack
In this context, newer approaches such as Federated Learning (FL) have not been fully explored yet, especially when malicious clients are present in the scenario setup.
SafeAMC: Adversarial training for robust modulation recognition models
We propose to use adversarial training, which consists of fine-tuning the model with adversarial perturbations, to increase the robustness of automatic modulation recognition (AMC) models.
Federated Learning for Malware Detection in IoT Devices
In this context, a framework that uses federated learning to detect malware affecting IoT devices is presented.
On the benefits of robust models in modulation recognition
When analyzing these vulnerable models we found that adversarial perturbations do not shift the symbols towards the nearest classes in constellation space.
A Survey on Device Behavior Fingerprinting: Data Sources, Techniques, Application Scenarios, and Datasets
The article at hand studies the recent growth of the device behavior fingerprinting field in terms of application scenarios, behavioral sources, and processing and evaluation techniques.
Cryptography and Security
