Presently, our digital landscape faces a pervasive onslaught of diverse cyber threats, encompassing distributed denial of service (DDoS), phishing, ransomware, and smishing, all orchestrated with malicious intent. Counteracting these malicious incursions poses a formidable challenge, particularly in devising efficacious detection solutions. Ransomware incidents are on the ascent, particularly within critical sectors such as healthcare, finance, and telecommunications. Consequently, this paper introduces a proof of concept (POC) aimed at detecting ransomware activities targeting Internet of Medical Things (IoMT) devices. The primary objective of this paper revolves around the identification and evaluation of factors correlating with ransomware attacks on IoMT and then developing a ransomware detector. The experimental framework involves the utilization of a simulated environment mirroring real-world IoMT devices and networks. The methodology integrates diverse approaches, encompassing data collection from IoMT devices, analysis of ransomware behaviour through the study of encryption patterns, and anomaly detection. The POC assesses the efficacy of these methodologies in detecting and responding to ransomware threats, with the experimentation conducted through hybrid analysis within a controlled laboratory setting. The dataset consists of 13 families with a total malware of 9251 taken from GitHub. For POC, a total of 3459 ransomware dataset samples have been selected. As a result of the experiment, thirteen (13) distinct features have been identified as trigger factors for ransomware attacks. These features are obtained by capturing of the processes initiated by the ransomware samples using a process monitor (procmon). A temporal pattern of the processes which include the file system, API, and access based on their frequency of occurrence were used to develop a ransomware detection model. The proposed approach achieved an accuracy of 99.2% and an error rate of 0.8 %, when evaluated on temporal pattern dataset and by using an enhanced artificial neural network (EANN).

Compressed Natural Gas (CNG) is an affordable fuel with a higher octane number. However, older CNG kits without electronic controls have the potential to supply more fuel when driving downhill due to the vacuum in the intake manifold. Therefore, this article presents a development of a CNG control system that accommodates road inclination angles to improve fuel efficiency. Machine learning is involved in this work to process engine speed, throttle valve position, and road slope angle. The control system is designed to ensure reduced fuel consumption when the vehicle is operating downhill. The results showed that the control system increases fuel consumption by 25.7% when driving downhill which an inclination of 5ᵒ. The AFR increased from 17.5 to 22 and the CNG flow rate decreased from 17.7 liters/min to 13.8 liters/min which is promising for applying to CNG vehicles.

The attack that occurred recently involved the utilization of malicious software, commonly referred to as malware, along with advanced techniques such as machine learning, specifically deep learning, code transformation, and polymorphism. This makes it harder for cyber experts to detect malware using traditional analysis methods. In view of the low accuracy and high false positive rate of traditional malware detection methods, this research proposes a fine-tuned deep learning model with a novel dataset that is compared with ANN, Support Vector Machine (SVM), random forest (RF), K-Nearest Neighbourhood (KNN) classifiers. Converting a malware code into an image could allow users to effectively identify the presence of malware, even if the original code is modified by the creator. This is due to the fact that the attributes of images remain unchanged, allowing for reliable identification. So, researchers used deep learning technology to detect malware, like detecting malaria from red blood cells. The deep learning model found that a more detailed analysis of malware data sets, focusing on RGB and greyscale images, is needed. These data sets currently rely on publicly available data, but the accuracy of the traditional model could be a lot higher. It also produces many false positive results. The main goal is to create a new data set and model using malware images to identify and categorize malware using deep learning without relying on existing image detection and transfer learning models. The researcher adjusted different hyper parameters, like the number of neurons, filters, stride, hidden units, layers, learning rate, batch size, activation function, optimizer, and epochs. Identifying and correcting issues in models, improving their clarity, stability, and fairness, as well as debugging and monitoring them, is a challenging task. To eliminate this obstacle, assess the model's behaviour and performance by employing various methods such as logging, profiling, testing, visualization, and model clarity. To overcome the challenges posed by the electricity shutdown, we utilized Google's cloud-based GPU and Python 3.7 language to conduct the experiment and train the model. Kali Linux is an operating system that can automatically encrypt file systems and has a lower chance of crashing the system due to malware in a virtual sandbox. The researcher used techniques like early stopping and cross-validation to prevent over fitting and assess generalization in addition to monitoring and evaluating the model's behaviour and performance. The researcher used different methods like L1 and L2 regularization, dropout, and batch normalization to improve the model's performance and avoid over fitting. The binary portable executable (PE) malware dataset is collected from Kaggle, Malimg, Virusshare, Malvis, MS Big2015, and VX-underground and finally converted to greyscale and RGB images to create a novel dataset to fill the lack of image dataset. The raw dataset was then rescaled to a 128x128 greyscale and RGB (red, green, blue) image and flattened to 1024-byte vector images input into convolution neural network (CNN) interpolation to extract features for malware detection. The newly acquired dataset is utilized as an input for the innovative DL algorithms in order to create a tailor-made model capable of accurately predicting malware. The findings in this customized CNN model by fine-tuning hyper-parameters with the three-channel RGB dataset outperformed a greyscale dataset with an accuracy of 98.7% and error rate of 1.3% in current malware compared with other artificial neural network (ANN), ML algorithms such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and also Random Forest (RF) models. The proposed approach is compatible with all operating systems (Windows, Linux, and Mac) and can identify different types of malwares, such as packed, polymorphic, obfuscated, metamorphic, or variations of a malware family. There are some limitations to the shortage of malware image datasets and computational costs for fine-tuning hyper parameters in the whole model. Furthermore, the proposed approach detects malware and groups it into families without using common techniques like disassembly, decompilation, de-obfuscation, or running malicious code in a virtual environment.

Compressed Natural Gas (CNG) is an alternative solution to the limited availability of fossil energy. CNG use's advantages include high octane value, applicable to vehicles requiring large power, cost-effectiveness, and lower emissions. However, applying the old CNG kit leaves emission problems and fuel wastage during deceleration. Although numerous studies have been carried out with numerous variables to improve engine performance and emissions, reports regarding deceleration interventions are still limited. Therefore, this study proposes enhanced modeling to optimize the fuel cut-off controller by applying fuzzy logic by controlling the throttle valve position based on the input sensor of the engine. Engine dynamics, fuel characteristics, and intake systems are considered strictly in the development of the control system to obtain more precise results that refer to the complete combustion process. The designed model has advantages over previous studies, which focused on achieving CNG AFR stoichiometry to improve fuel economy by using the fuel cut-off method during deceleration. The results showed that fuel savings could be increased during deceleration by cutting off fuel flow to the engine. This can be seen from the increase in AFR ± 57% and decrease ± 38% - 67% in CNG flow rate during deceleration which is promising to be widely applied. In the future, the proposed model could be used as part of the vehicle component in optimizing the fuel consumption that will support green technology sustainability.

The air conditioning system (AC) in passenger cars requires precise control to provide a comfortable and healthy driving. In an AC system with limited manual control, the driver has to repeatedly change the setting to improve comfort. This problem may be overcome by implementing an automatic control system to maintain cabin temperature and humidity to meet passenger's thermal comfort. Therefore, this paper presents the development of a laboratory-scale prototype air conditioning control system to regulate temperature, humidity and air circulation in the cabin. The experimental results show that the control system is able to control air temperature in the range of 21 °C to 23 °C and cabin air humidity between 40% to 60% in various simulated environmental conditions which indicate acceptance for comfort and health standards in the vehicle. In conclusion, this method can be applied to older vehicles with reasonable modifications.