Securing data transactions across computer networks presents significant challenges, particularly concerning data privacy and cybersecurity threats. These issues are particularly critical in electric vehicle charging stations (EVCSs) due to the sensitive data involved, such as data breaches, unauthorized access, and privacy concerns. The primary challenge within EVCS architecture lies in defending against various cyberattacks. Several machine learning models, including convolutional neural networks, recurrent neural networks, and long short-term memory, have been employed to enhance EVCS security. However, these models often struggle to effectively handle the temporal dependencies, complexity, and scalability required for real-time threat detection. In this study, we propose a novel temporal convolutional network (TCN) to enhance EVCS security by rapidly detecting Open Charge Point Protocol (OCPP) attacks. Our model was trained and tested on the latest CICEVSE2024 dataset, which encompasses attacks targeting EVCS communication protocols, including OCPP and ISO15118, during charging and idle states. The proposed model achieved 100% accuracy in binary classification and five-class scenarios, and 93% accuracy in multiclass classification, surpassing existing approaches. Additionally, we conducted a comparative analysis with a hybrid model, demonstrating that the proposed TCN delivers superior performance with lower computational complexity, fewer parameters, a smaller model size, and shorter computation times. These features make the TCN model highly suitable for real-time EVCS applications. This work establishes a new benchmark for EVCS security, showcasing the potential of TCNs to deliver scalable and efficient solutions for safeguarding critical charging infrastructure against cyber threats.

Electric Vehicle Charging Station (EVCS) systems have become increasingly critical to the energy and transportation sectors. The detection of various attacks in EVCS, including data interception in the Open Charge Point Protocol (OCPP), poses substantial cybersecurity challenges that existing deep learning methods struggle to address effectively. This work investigates the impact of 16 types of attacks on EVCS, such as denial-of-service (DoS), reconnaissance, cryptojacking, and backdoor attacks. To address these threats, we propose an innovative model designed to identify diverse cyber threats targeting EVCS. The proposed Temporal Convolutional Network (TCN)-based Intrusion Detection System (IDS) architecture integrates four key innovations: multi-receptive fields, a gating mechanism, iterative dilation, and a self-attention mechanism combined with a Squeeze-and-Excitation (SE) block to recalibrate feature responses by explicitly modeling interactions between different channels. The proposed model effectively processes multiple temporal scales, regulates the flow of information, adapts to varying time steps, and focuses on essential components of time-series data. Experimental evaluations demonstrate that the proposed model outperforms state-of-the-art methods in terms of accuracy and detection rates across all 16 attack types in the CICEVSE2024 dataset, which comprises extensive attack vectors and variants associated with the OCPP. The proposed approach achieves higher accuracy compared to other TCN variants and exhibits high resilience against complex attacks.

Malaysia is rapidly expanding the generation capacity of solar power through large scale solar (LSS) projects with the aim to achieve 20% renewable energy mix by 2025. This has motivated many solar industry players to explore the usage of solar PV with single axis tracker (SAT) system. However, many are still hesitant due to the lack of understanding on the comparative performance between fixed mounted solar PV with solar PV with SAT system. This paper aims to provide a comparative analysis on the performance of both systems. Simulation using PVSyst 6.83 was performed in five potential LSS sites spread across Peninsular Malaysia in Perlis, Kelantan, Pahang, Selangor and Johor with the same installed capacity of 10.32MWp. The energy yield and capacity factor for 21 years were simulated. On the average, it was found that SAT outperforms fixed mounted solar PV system by 15.08% based on their performance on their first year operation.

Grounding enhancement material (GEM) is used to lower the earthing resistance value of a given earthing system. In this paper, a commercially available GEM is experimented at the field alongside with Sodium Chloride, Copper II Sulphate and planting soil. The well established Wenner’s 4 pole method and fall of potential method was employed to measure the soil resistivity and earthing resistance respectively. It was found that the salts i.e., Sodium Chloride and Copper II Sulphate are superior in reducing the earthing resistance as reduction of more than 85% were observed. However, the commercial GEM has exhibited the most stable earthing resistance value over a period of 101 days, exhibiting the lowest standard deviation. This seems to suggest that the commercial GEM has superior moisture retention capability. This study also proven that Sodium Chloride can be dissolved by heavy downpour and replenishing it periodically is needed in a tropical country like Malaysia with regular thunderstorms and heavy downpours.

Background COVID-19 has drastically dampened human activities since early 2020. Studies have shown that this has resulted in changes in air temperature and humidity. Since lightning activities are dependent on air temperature and humidity, this study is conducted to evaluate the correlation between the intensity of lightning activities with the atmospheric changes, and investigates the changes, in lightning activities due to atmospheric changes during the COVID-19 pandemic. Methods The hypothesis was tested through a t-test and Pearson’s correlation study. The variation trend of lightning strikes count (LSC) in Europe and Oceania during the five months COVID-19 lockdown period (March – July) compared to the same period in the previous five years from 2015 to 2019 is investigated. Results Statistical analysis shows the LSC in Europe and Oceania during the lockdown period dropped significantly by more than 50% and 44% respectively compared to the same period in previous five years. Furthermore, LSC was found to be positively correlated with air temperature and relative humidity in Europe. However, in Oceania, LSC seems to be only positively correlated with air temperature but negatively correlated with relative humidity. Conclusions This study seems to suggest that lightning activities have significantly changed during this pandemic due to reduction in human activities.