The load profile is a key characteristic of the power grid and lies at the basis for the power flow control and generation scheduling. However, due to the wide adoption of internet-of-things (IoT)-based metering infrastructure, the cyber vulnerability of load meters has attracted the adversary’s great attention. In this paper, we investigate the vulnerability of manipulating the nodal prices by injecting false load data into the meter measurements. By taking advantage of the changing properties of real-world load profile, we propose a deeply hidden load data attack (i.e., DH-LDA) that can evade bad data detection, clustering-based detection, and price anomaly detection. The main contributions of this work are as follows: (i) We design a stealthy attack framework that exploits historical load patterns to generate load data with minimal statistical deviation from normal measurements, thereby maximizing concealment; (ii) We identify the optimal time window for data injection to ensure that the altered nodal prices follow natural fluctuations, enhancing the undetectability of the attack in real-time market operations; (iii) We develop a resilience evaluation metric and formulate an optimization-based approach to quantify the electricity market’s robustness against DH-LDAs. Our experiments show that the adversary can gain profits from the electricity market while remaining undetected.

In the rapidly evolving urban landscape, outdoor parking lots have become an indispensable part of the city’s transportation system. The growth of parking lots has raised the likelihood of spontaneous vehicle combustion, a significant safety hazard, making smoke detection an essential preventative step. However, the complex environment of outdoor parking lots presents additional challenges for smoke detection, which necessitates the development of more advanced and reliable smoke detection technologies. This paper addresses this concern and presents a novel smoke detection technique designed for the demanding environment of outdoor parking lots. First, we develop a novel dataset to fill the gap, as there is a lack of publicly available data. This dataset encompasses a wide range of smoke and fire scenarios, enhanced with data augmentation to ensure robustness against diverse outdoor conditions. Second, we utilize an optimized YOLOv5s model, integrated with the Squeeze-and-Excitation Network (SENet) attention mechanism, to significantly improve detection accuracy while maintaining real-time processing capabilities. Third, this paper implements an outdoor smoke detection system that is capable of accurately localizing and alerting in real time, enhancing the effectiveness and reliability of emergency response. Experiments show that the system has a high accuracy in terms of detecting smoke incidents in outdoor scenarios.