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Found 14 papers, 0 papers with code
Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation
For both cases, solutions using the convergence of communication theory, control theory, and machine learning are proposed to enable effective and secure CAV navigation.
Two-Bit Aggregation for Communication Efficient and Differentially Private Federated Learning
In federated learning (FL), a machine learning model is trained on multiple nodes in a decentralized manner, while keeping the data local and not shared with other nodes.
Deep Learning for Rain Fade Prediction in Satellite Communications
Line of sight satellite systems, unmanned aerial vehicles, high-altitude platforms, and microwave links that operate on frequency bands such as Ka-band or higher are extremely susceptible to rain.
AoI-minimizing Scheduling in UAV-relayed IoT Networks
Our simulation results show that DQN-based scheduler outperforms MAF-MAD scheduler and three other baseline schedulers, i. e., Maximal AoI First (MAF), Round Robin (RR) and Random, employed at both hops under general conditions when the network is small (with 10's of IoT devices).
Reinforcement Learning for Deceiving Reactive Jammers in Wireless Networks
Different from existing works, in this paper, a novel anti-jamming strategy is proposed based on the idea of deceiving the jammer into attacking a victim channel while maintaining the communications of legitimate users in safe channels.
Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networks
To guarantee an efficient learning process, necessary and sufficient conditions for the optimal UAV network topology that maximizes the learning rate for cooperative channel modeling are derived, and the optimal CGAN learning solution per UAV is subsequently characterized, based on the distributed network structure.
Brainstorming Generative Adversarial Networks (BGANs): Towards Multi-Agent Generative Models with Distributed Private Datasets
In this paper, to address this multi-agent GAN problem, a novel brainstorming GAN (BGAN) architecture is proposed using which multiple agents can generate real-like data samples while operating in a fully distributed manner.
Generative Adversarial Networks for Distributed Intrusion Detection in the Internet of Things
To this end, in this paper, a distributed generative adversarial network (GAN) is proposed to provide a fully distributed IDS for the IoT so as to detect anomalous behavior without reliance on any centralized controller.
Cyber-Physical Security and Safety of Autonomous Connected Vehicles: Optimal Control Meets Multi-Armed Bandit Learning
For sensors having a prior information, a DIA detection approach is proposed and an optimal threshold level is derived for the difference between the actual and estimated values of sensors data which enables ACV to stay robust against cyber attacks.
Robust Deep Reinforcement Learning for Security and Safety in Autonomous Vehicle Systems
To this end, in this paper, the state estimation process for monitoring AV dynamics, in presence of CP attacks, is analyzed and a novel adversarial deep reinforcement learning (RL) algorithm is proposed to maximize the robustness of AV dynamics control to CP attacks.
Machine Learning for Wireless Connectivity and Security of Cellular-Connected UAVs
Cellular-connected unmanned aerial vehicles (UAVs) will inevitably be integrated into future cellular networks as new aerial mobile users.
Deep Learning for Signal Authentication and Security in Massive Internet of Things Systems
In the massive IoT system, due to a large set of available actions for the cloud, it is shown that analytically deriving the MSNE is challenging and, thus, a learning algorithm proposed that converges to the MSNE.
Deep Learning for Reliable Mobile Edge Analytics in Intelligent Transportation Systems
However, realizing the true potential of ITSs requires ultra-low latency and reliable data analytics solutions that can combine, in real-time, a heterogeneous mix of data stemming from the ITS network and its environment.
Deep Learning-Based Dynamic Watermarking for Secure Signal Authentication in the Internet of Things
In this paper, a novel deep learning method is proposed for dynamic watermarking of IoT signals to detect cyber attacks.
Information Theory Cryptography and Security Multimedia Information Theory
