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Found 10 papers, 0 papers with code
Programming Wireless Security through Learning-Aided Spatiotemporal Digital Coding Metamaterial Antenna
Physical layer (PHY) security has been put forth as a cost-effective alternative to cryptographic mechanisms that can circumvent the need for explicit key exchange between communication devices, owing to the fact that PHY security relies on the physics of the signal transmission for providing security.
Watch from sky: machine-learning-based multi-UAV network for predictive police surveillance
This paper presents the watch-from-sky framework, where multiple unmanned aerial vehicles (UAVs) play four roles, i. e., sensing, data forwarding, computing, and patrolling, for predictive police surveillance.
Network Traffic Shaping for Enhancing Privacy in IoT Systems
We show that in special cases the proposed mechanism recovers existing shapers which standardize the output independently from the input.
A hybrid model-based and learning-based approach for classification using limited number of training samples
In this paper, a hybrid classification method -- termed HyPhyLearn -- is proposed that exploits both the physics-based statistical models and the learning-based classifiers.
Prosumer Behavior: Decision Making with Bounded Horizon
Modeling the utility of the prosumer using parameters such as the offered price on a day, the number of energy units the prosumer has available for sale on a day, and the probabilities of the forecast prices, we fit both traditional EUT and the proposed behavioral model with bounded time windows to data collected from 57 homeowners over 68 days in a simulated energy market.
Estimation of Individual Device Contributions for Incentivizing Federated Learning
Federated learning (FL) is an emerging technique used to train a machine-learning model collaboratively using the data and computation resource of the mobile devices without exposing privacy-sensitive user data.
Learning-Aided Physical Layer Attacks Against Multicarrier Communications in IoT
It is in this vein that PHY spoofing performance of adversaries equipped with supervised and unsupervised ML tools are investigated in this paper.
Learning-based Resource Optimization in Ultra Reliable Low Latency HetNets
Modeling the latency and reliability constraints of users with probabilistic guarantees, the joint problem of user offloading and resource allocation (JUR) in a URLLC setting is formulated as an optimization problem to minimize the cost of serving users for the MBS.
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.
