# Optimal Green Hybrid Attacks in Secure IoT

arXiv: https://arxiv.org/abs/1912.02097 · HTML: https://mlsanity.com/paper/1912.02097/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2019.

## Built on by (most cited first, 1 of 1)

- [Multi-Agent Reinforcement Learning for Offloading Cellular Communications with Cooperating UAVs](https://mlsanity.com/paper/2402.02957/) (2024)

## Abstract (arXiv)

In pursuance to understand the behavior of a potential green hybrid attacker in secure internet-of-things (IoT), this letter investigates optimal energy utilization from the attacker's viewpoint. Specifically, we propose a novel framework for optimizing the efficacy of a hybrid attacker, possessing capability to both eavesdrop and jam, while considering the underlying energy consumption. In particular, we maximize the attacker energy efficiency (AEE) in secure IoT by deriving the analytical solutions for jointly global-optimal attacking mode, eavesdropping rate, and jamming power. Numerical results, validating analytical claims, reveal that the proposed green design can offer about 45% improvement in average AEE over the relevant benchmarks.