Deep Learning -Powered Computational Intelligence for Cyber-Attacks Detection and Mitigation in 5G-Enabled Electric Vehicle Charging Station

An electric vehicle charging station (EVCS) infrastructure is the backbone of transportation electrification. However, the EVCS has various cyber-attack vulnerabilities in software, hardware, supply chain, and incumbent legacy technologies such as network, communication, and control. Therefore, proactively monitoring, detecting, and defending against these attacks is very important. The state-of-the-art approaches are not agile and intelligent enough to detect, mitigate, and defend against various cyber-physical attacks in the EVCS system. To overcome these limitations, this dissertation primarily designs, develops, implements, and tests the data-driven deep learning-powered computational intelligence to detect and mitigate cyber-physical attacks at the network and physical layers of 5G-enabled EVCS infrastructure. Also, the 5G slicing application to ensure the security and service level agreement (SLA) in the EVCS ecosystem has been studied. Various cyber-attacks such as distributed denial of services (DDoS), False data injection (FDI), advanced persistent threats (APT), and ransomware attacks on the network in a standalone 5G-enabled EVCS environment have been considered. Mathematical models for the mentioned cyber-attacks have been developed. The impact of cyber-attacks on the EVCS operation has been analyzed. Various deep learning-powered intrusion detection systems have been proposed to detect attacks using local electrical and network fingerprints. Furthermore, a novel detection framework has been designed and developed to deal with ransomware threats in high-speed, high-dimensional, multimodal data and assets from eccentric stakeholders of the connected automated vehicle (CAV) ecosystem. To mitigate the adverse effects of cyber-attacks on EVCS controllers, novel data-driven digital clones based on Twin Delayed Deep Deterministic Policy Gradient (TD3) Deep Reinforcement Learning (DRL) has been developed. Also, various Bruteforce, Controller clones-based methods have been devised and tested to aid the defense and mitigation of the impact of the attacks of the EVCS operation. The performance of the proposed mitigation method has been compared with that of a benchmark Deep Deterministic Policy Gradient (DDPG)-based digital clones approach. Simulation results obtained from the Python, Matlab/Simulink, and NetSim software demonstrate that the cyber-attacks are disruptive and detrimental to the operation of EVCS. The proposed detection and mitigation methods are effective and perform better than the conventional and benchmark techniques for the 5G-enabled EVCS

Synthesis and optical characterization of perovskite layer for solar cell application

Solvent engineering offers fine control over the photovoltaic efficiency, film morphology, and crystallization quality of perovskite films and also enables to optimize light transmittance and absorbance in solar cell applications. In the present work, the band gap and reflectance were reduced through solvent engineering. We found that perovskite thin films produced using DMF (Dimethyl formamide) solvent had a band gap that was 0.24 eV less than those produced using IPA (Isopropyl Alcohol) solvent. Perovskite thin films produced using DMF solvent also exhibited considerably lower solar spectrum reflectance

Clinical characteristics and outcomes of pregnant patients with COVID-19 at BPKIHS, a prospective study

Background: Coronavirus disease (COVID-19) can vary in presentation, women present with a spectrum of clinical manifestations that range from mild symptoms and signs to severe illness, may require immediate advanced critical care support. Therefore, a hospital-based study was conducted to evaluate the effect of COVID-19 on maternal and perinatal outcomes of COVID-19 infected pregnant women. Methods: This was prospective descriptive study conducted at B. P. Koirala institute of health science. Total duration of study was one year from September 2020 to August 2021. This study was done in 70 pregnant ladies with confirmed SARS CoV2 infection. Different clinical presentation, mode of delivery, treatment given, need of oxygen, need of ICU admission, maternal outcomes, neonatal outcomes were observed. Result: The most common symptom was fever in 38 (54.2%) patients, followed by cough in 22 (31.5%) patients. Eighteen (25.71%) had mild degree of pneumonia with fall in oxygen saturation below 95% and required oxygen via face mask. Three patients (4.28%) develop sever pneumonia and required mechanical ventilation two (2.85%) had maternal mortality due to covid pneumonia. Most common mode of delivery was LSCS 21 (61.76%). Seven (10%) patients had preterm delivery. Conclusions: Most of the COVID-19 positive pregnant women remained asymptomatic or had mild infections. COVID-19 infections in late pregnancy might lead to an increased incidence of caesarean deliveries as observed in the present study. Adverse perinatal outcomes are mostly due to delayed presentation of patient to the hospital during COVID pandemic. High maternal mortality rate has been seen in present study. So proper intensive care is must for the management of such patient during pandemics. Also, efforts to limit exposure to COVID-19 of pregnant women should be strengthened for saving mother and child

Effect of integrated nutrient management on growth and yield of radish

Integrated Nutrient Management (INM) is necessary to enhance sustainable yield in an eco-friendly way. A field experiment was conducted in the research field of Midwest Academy and Research Institute College of Live Sciences, Tulsipur, Dang from November 2018 to January 2019 to investigate the effect of integrated nutrient management on growth and yield of radish. Mino Early variety was used in the experiment. The experiment was laid out on Randomized Complete Block Design with four replications and 5 treatments. Nitrogen (N) was supplied through different sources. The treatment combinations were: control (T1), 100% recommended N through chemical fertilizer (T2), 50% recommended N through chemical fertilizer + 50% N through farmyard manure (FYM) (T3), 50% recommended N through chemical fertilizer + 50% N through poultry manure (T4) and 50% recommended N through chemical fertilizer + 50% vermicompost (T5). Significant effect was noted on leaf numbers, root length, root diameter and yield per ha but no significant effect was noted on the germination percentage and plant height. The highest germination percentage  (77.00 %), plant height (13.27 cm), root length (16.94 cm), root diameter (3.01 cm), and yield per ha (16.55 t/ha) was recorded at T4 (50% recommended N through chemical fertilizer + 50% N through poultry manure). T5 (50% recommended N through chemical fertilizer + 50% vermicompost) recorded the highest leaf numbers (10.40). In our experiment, T4 (50% recommended N through chemical fertilizer + 50% N through poultry manure) was found to be superior, so in inner terai places like Tulsipur, Dang it is suggested to apply 50% recommended N through chemical fertilizer + 50% N through poultry manure to obtain a high yield of radish

Deep learning-based intrusion detection system for electric vehicle charging station

The integration of the open communication layer to the physical layer of the power grids facilitates bidirectional communication, automation, remote control, distributed, and embedded intelligence, and smart resource management, in the grids. However, cybersecurity threats are inherent with the open communication layer, which can violate the confidentiality, integrity, and availability (CIA) of the grid resources. The soaring usage and popularity of electric vehicles (EVs) demand the robust deployment of trustworthy electric vehicle charging station (EVCS). We propose the novel deep learning-based intrusion detection systems (IDS) to detect the denial of service (DoS) attacks in the EVCS. The deep neural network (DNN) and long-short term memory (LSTM) algorithms are implemented (in python 3.7.8) to detect and classify DoS attacks in the EVCS. Results show that both the DNN and LSTM based IDS achieved more than 99% detection accuracy. On top, the LSTM method is superior to the DNN method in terms of accuracy, precision, recall, and measure

WCGAN-Based Cyber-Attacks Detection System in the EV Charging Infrastructure

Transportation electrification and connected automated vehicle infrastructures are the future of sustainable e-mobility, fueling the robust deployment of vehicle charging infrastructures and evolving network and communication architectures. The incumbent technologies of sustainable e-mobility migrate the inherent vulnerabilities in software, hardware, protocols, communication, and human that state-funded or ill-willed cyber attackers could exploit. The existing deep learning-based detection algorithms suffer from constrained performance due to insufficient cyberattack data. Inspired by the synthetic data generation by the generative adversarial network (GAN), we propose the external classifier Wasserstein condition GAN (EC-WCGAN)-based network intrusion detection systems (NIDS) to detect the distributed denial of service (DDoS) attacks in the EV Charging infrastructures. Moreover, it can inform the different DDoS attack classes as well. The proposed method surpasses the DL-based model in terms of data usage and performance metrics. This method enhances the detection by generating plausible synthetic data for the low sample classes and training the classifier to get more than 99% performance metrics

A deep learning perspective on Connected Automated Vehicle (CAV) cybersecurity and threat intelligence

The connected and autonomous vehicle (CAV) is the next-generation mobility service—powered by intelligent automation and robust communication—which is aimed at replacing human-maneuvered vehicles with the software agent matching or even exceeding the human-level intelligence, control, and agility and minimizing errors. The next generation of transportation and mobility envisions safe, reliable, agile, automated, trustworthy, and service-based mobility architecture. The architecture should be able to eliminate human errors by using intelligent decision-making software agents based on the situational and behavioral information collected by sensors and transceivers through communication. Apart from that, service-based architecture removes the concept of vehicle ownership and incorporates more diversity in passengers, including the disabled and elderly people. CAV is the evolving technology to achieve the envisioned goals of future mobility and transportation. This chapter sheds light on cyber-physical vulnerabilities and risks that originated in informational technology (IT), operational technology (OT), and the physical domains of the CAV ecosystem, eclectic threat landscapes, and threat intelligence

Exploring cybersecurity issues in 5G enabled electric vehicle charging station with deep learning

The surging usage of electric vehicles (EVs) demand the robust deployment of trustworthy electric vehicle charging station (EVCS) with millisecond range latency and massive machine to machine communications where 5G could act. However, 5G suffers from inherent protocols, hardware, and software vulnerabilities that seriously threaten the communicating entities\u27 cyber-physical security. To overcome these limitations in the EVCS system, this paper analyses the impact of False Data Injection (FDI) and Distributed Denial of Services (DDoS) attacks on the operation of EVCS. This work is an extension of the previously published conference paper about the EVCS. As new features, this paper simulates the FDI attack and the syn flood DDoS attacks on 5G enabled remote Supervisory Control and Data Acquisition (SCADA) system that controls the solar photovoltaics (PV) controller, Battery Energy Storage (BES) controller, and EV controller of the EVCS. The extent of delay has been increased to more than 500 ms with the severe DDoS attack via 5G. The attacks make the EVCS system oscillate or shift the DC operating point. The frequency of oscillation, its damping, and the system\u27s resiliency are found to be related to the attacks\u27 intensity and the target controller. Finally, the novel stacked Long Short-Term Memory (LSTM) based intrusion detection systems (IDS) are proposed solely based on the electrical fingerprint. This model can detect the stealthy cyberattacks that bypass the cyber layer and go unnoticed in the monitoring system with nearly 100% detection accuracy

Ransomware detection using deep learning in the SCADA system of electric vehicle charging station

The Supervisory control and data acquisition (SCADA) systems have been continuously leveraging the evolution of network architecture, communication protocols, next-generation communication techniques (5G, 6G, Wi-Fi 6), and the internet of things (IoT). However, SCADA system has become the most profitable and alluring target for ransomware attackers. This paper proposes the deep learning-based novel ransomware detection framework in the SCADA controlled electric vehicle charging station (EVCS) with the performance analysis of three deep learning algorithms, namely deep neural network (DNN), 1D convolution neural network (CNN), and long short-term memory (LSTM) recurrent neural network. All three-deep learning-based simulated frameworks achieve around 97% average accuracy (ACC), more than 98% of the average area under the curve (AUC) and an average F1-score under 10-fold stratified cross-validation with an average false alarm rate (FAR) less than 1.88%. Ransomware driven distributed denial of service (DDoS) attack tends to shift the state of charge (SOC) profile by exceeding the SOC control thresholds. Also, ransomware driven false data injection (FDI) attack has the potential to damage the entire BES or physical system by manipulating the SOC control thresholds. It\u27s a design choice and optimization issue that a deep learning algorithm can deploy based on the tradeoffs between performance metrics

Bartonella henselae Recombinant Pap31 for the Diagnosis of Canine and Human Bartonelloses

Bartonella spp. comprise a genus of Gram-negative alphaproteobacteria that are slow growing, fastidious, and facultative intracellular pathogens with zoonotic potential. Immunofluorescent antibody assays (IFAs), Western blot (WB), and enzyme-linked immunosorbent assays (ELISAs), the frequently used modalities for the serological diagnosis of canine and human Bartonelloses, generate numerous false negative results. Therefore, the development of a reliable serodiagnostic assay for Bartonelloses is of clinical and epidemiological importance. Pap31, a heme binding surface protein of B. henselae, is associated with bacterial adhesion and related to bacterial colonization. To our knowledge, B. henselae Pap31 and its fragments (N-terminal (NTD), middle (MD), and C-terminal (CTD) domains) have not been investigated for the serodiagnosis of canine and human Bartonelloses. In this study, we evaluate the diagnostic utility of B. henselae recombinant whole Pap31 (rPap31) and Pap31 fragments by ELISA using sera from 70 dogs (36 Bartonella spp. IFA-positive (naturally infected), and 34 Bartonella spp. IFA- and PCR-negative (control dogs)) and 36 humans (18 Bartonella spp. IFA-positive (naturally infected) and 18 controls)). In the dogs, the area under the curve (AUC) score of recombinant whole Pap31 was 0.714 with a sensitivity of 42% and specificity of 94% at an OD cutoff value of 0.8955. Among the evaluated recombinant Pap31 proteins for the diagnosis of canine Bartonelloses, rPap31-NTD yielded the highest AUC score of 0.792 (95% CI 0.688–0.895) with a sensitivity of 44% and specificity of 100% at a cutoff value of 1.198. In concordance with this finding, rPap31-NTD also had the highest AUC score of 0.747 (95% CI 0.581–0.913) among the Pap31 recombinant proteins for the diagnosis of human Bartonelloses, with 39% sensitivity and 94% specificity at a cutoff value of 1.366. Recombinant whole Pap31 (rPap31) resulted in 72% sensitivity and 61% specificity at a cutoff value of 0.215 for human Bartonelloses. Due to either low sensitivity or questionable specificity, our findings indicate that recombinant Pap31 and the selected fragments may not be appropriate diagnostic targets in detecting anti-Bartonella antibodies in Bartonella-infected dogs and humans. The findings from this study can be used to further assess the antigenicity and immunogenicity of B. henselae Pap31 as a diagnostic target