Search Results for author: Thomas La Porta

Found 5 papers, 0 papers with code

HoneyIoT: Adaptive High-Interaction Honeypot for IoT Devices Through Reinforcement Learning

no code implementations10 May 2023 Chongqi Guan, Heting Liu, Guohong Cao, Sencun Zhu, Thomas La Porta

One effective approach to improving IoT security is to deploy IoT honeypot systems, which can collect attack information and reveal the methods and strategies used by attackers.

reinforcement-learning Vocal Bursts Intensity Prediction

An Approach for Fast Cascading Failure Simulation in Dynamic Models of Power Systems

no code implementations29 Apr 2022 Sina Gharebaghi, Nilanjan Ray Chaudhuri, Ting He, Thomas La Porta

To solve this, we propose a fast cascading failure simulation approach based on implicit Backward Euler method (BEM) with stiff decay property.

Topology Estimation Following Islanding and its Impact on Preventive Control of Cascading Failure

no code implementations13 Apr 2021 Sai Gopal Vennelaganti, Nilanjan Ray Chaudhuri, Ting He, Thomas La Porta

Knowledge of power grid's topology during cascading failure is an essential element of centralized blackout prevention control, given that multiple islands are typically formed, as a cascade progresses.

Verifiable Failure Localization in Smart Grid under Cyber-Physical Attacks

no code implementations18 Jan 2021 Yudi Huang, Ting He, Nilanjan Ray Chaudhuri, Thomas La Porta

Our numerical evaluations based on the Polish power grid and IEEE 300-bus system demonstrate that the proposed algorithms are highly successful in verifying the states of truly failed links, and can thus greatly help in prioritizing repairs during the recovery process.

Performance

Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices

no code implementations27 Sep 2017 Zongqing Lu, Swati Rallapalli, Kevin Chan, Thomas La Porta

In doing so Augur tackles several challenges: (i) how to overcome pro ling and measurement overhead; (ii) how to capture the variance in different mobile platforms with different processors, memory, and cache sizes; and (iii) how to account for the variance in the number, type and size of layers of the different CNN configurations.

Self-Driving Cars

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