Improving Reliability for Detecting Anomalies in the MQTT Network by Applying Correlation Analysis for Feature Selection Using Machine Learning Techniques

Anomaly detection AD has captured a significant amount of focus from the research field in recent years with the rise of the Internet of Things IoT application Anomalies often known as outliers are defined as the discovery of anomalous occurrences or observations that differ considerably from the mainstream of the data The IoT which is described as a network of Internet based digital sensors that continuously generate massive volumes of data and use to communicate with one another theMessage Queuing Telemetry Transport MQTT protocol Brute force Denial of Service DoS Malformed Flood and Slowite attacks are the most common in theMQTT network One of the significant factors in IoT AD is the time consumed to predict an attack and take preemptive measures For instance if an attack is detected late the loss of attack is irreversible This paper investigates the time to detect an attack using machine learning approaches and proposes a novel approach that applies correlation analysis to reduce the training and testing time of these algorithms The new approach has been evaluated on Random Forest Decision Tree Na ve Bayes Multi Layer Perceptron Artificial Neural Network Logistic Regression and Gradient Boost The findings indicate that the correlation analysis is significantly beneficial in the process of feature engineering primarily to determine the most relevant features in the MQTT dataset This is to the best of our knowledge the first study on MQTTset that reduces the prediction time for DoS 0 92 95 CI 0 378 2 22 reduced to 0 77 95 CI 0 414 1 97 and for Malformed 2 92 95 CI 2 6 8 44 reduced to 0 49 95 CI 0 273 1 2

Realtime Feature Engineering for Anomaly Detection in IoT Based MQTT Networks

The MQTTset dataset has been extensively investigated for enhancing anomaly detection in IoT-based systems, with a focus on identifying Denial of Service (DoS) attacks. The research addresses a critical gap in MQTT traffic anomaly detection by proposing the incorporation of the source attribute from PCAP files and utilizing hand-crafted feature engineering techniques. Various filtering methods, including data conversion, attribute filtering, handling missing values, and scaling, are employed. Anomalies are categorized and prioritized based on frequency of occurrence, with a specific emphasis on DoS attacks. The study compares the performance of the decision tree and its eight variant models (ID3, C4.5, Random Forest, CatBoost, LightGBM, XGBoost, CART, and Gradient Boosting) for anomaly detection in IoT-based systems. Evaluation metrics such as prediction accuracy, F1 score, and computational times (training and testing) are utilized. Hyperparameter fine-tuning techniques like grid search and random search are applied to enhance model performance, accuracy, and reduce computational costs. Results indicate that the benchmark Decision Tree model achieved 92.57 accuracy and a 92.38 F1 score with training and testing times of 2.95 seconds and 0.86 seconds, respectively. The Feature Engineering (Modified) dataset demonstrated a substantial improvement, reaching 98.56 accuracy and a 98.50 F1 score, with comparable training and testing times of 0.70 seconds and 0.02 seconds. Furthermore, the Modified Decision Tree Algorithm significantly improved accuracy to 99.27 , F1 score to 99.26 , and reduced training time to 0.73 seconds and testing time to 0.14 seconds. The research contributes valuable insights into feature engineering and guides the selection of effective approaches for anomaly detection in IoT-based systems, providing early threat warnings and enhancing overall system security and reliability

Assessing the potential of GHG emissions for the textile sector: A baseline study.

The carbon footprint (CFP) is a measure of greenhouse gases (GHGs) emitted throughout the lifecycle of a product or activity, while the energy footprint (EFP) and water footprint (WFP) measure energy and water consumption, respectively. These footprints are essential for managing emissions and consumption and promoting low-carbon consumption. A carbon labeling scheme could help consumers make informed choices. Asia is a major textile producer and consumer, so studying textiles' carbon, energy, and water footprints is essential for managing domestic emissions, energy and water consumption, and international trade negotiations. This paper presents a method and framework for assessing CFP, EFP, and WFP at the product level and calculates the footprints for textile products. The results show that the total CFP of all textile products produced is 42,624.12 MT CO2e, with indirect emissions contributing significantly more than direct emissions. The total EFP is 248.38 PJ, with electricity consumption being the main contributor, while the total WFP is 80.71 billion liters. The spinning stage of production has the highest CFP and EFP, and energy consumption is the main contributor to all footprints. These results can help compare different products and reduce the footprints of the textile sector

Charge injection and trapping in TiO2 nanoparticles decorated silicon nanowires arrays

We investigate carrier transport properties of silicon nanowire (SiNW) arrays decorated with TiO2 nanoparticles (NPs). Ohmic conduction was dominant at lower voltages and space charge limited current with and without traps was observed at higher voltages. Mott’s 3D variable range hoping mechanism was found to be dominant at lower temperatures. The minimum hopping distance (Rmin) for n and p-SiNWs/TiO2 NPs devices was 1.5 nm and 0.68 nm, respectively, at 77 K. The decrease in the value of Rmin can be attributed to higher carrier mobility in p-SiNWs/TiO2 NPs than that of n-SiNWs/TiO2 NPs hybrid device

State-of-the-art and future perspectives of environmentally friendly machining using biodegradable cutting fluids

The use of cutting fluids has played a vital role in machining operations in lubrication and cooling. Most cutting fluids are mineral oil-based products that are hazardous to the environment and the worker, cause severe diseases and pollute the environment. In addition, petroleum re-sources are becoming increasingly unsustainable. Due to environmental and health issues, legislations have been established to ensure that the consumption of mineral oil is reduced. Consequently, researchers are making efforts to replace these mineral oil-based products. Vegetable oils are grasping attention due to their better lubricating properties, ease of availability, biodegradability, low prices, and non-toxicity. In this study, a detailed review and critical analysis are conducted of the research works involving vegetable oils as cutting fluids keeping in view the shortcomings and possible solutions to overcome these drawbacks. The purpose of the review is to emphasise the benefits of vegetable oil-based cutting fluids exhibiting comparable performance to that of mineral oil-based products. In addition, an appropriate selection of non-edible vegetable oil-based cutting fluids along with optimum cutting parameters to avoid a scanty supply of edible oils is also dis-cussed. According to this research, vegetable oils are capable of substituting synthetic cutting fluids, and this option might aid in the successful and cost-efficient implementation of green machining

State-of-the-Art of Establishing Test Procedures for Real Driving Gaseous Emissions from Light- and Heavy-Duty Vehicles

Air pollution caused by vehicle emissions has raised serious public health concerns. Vehicle emissions generally depend on many factors, such as the nature of the vehicle, driving style, traffic conditions, emission control technologies, and operational conditions. Concerns about the certification cycles used by various regulatory authorities are growing due to the difference in emission during certification procedure and Real Driving Emissions (RDE). Under laboratory conditions, certification tests are performed in a ‘chassis dynamometer’ for light-duty vehicles (LDVs) and an ‘engine dynamometer’ for heavy-duty vehicles (HDVs). As a result, the test drive cycles used to measure the automotive emissions do not correctly reflect the vehicle’s real-world driving pattern. Consequently, the RDE regulation is being phased in to reduce the disparity between type approval and vehicle’s real-world emissions. According to this review, different variables such as traffic signals, driving dynamics, congestions, altitude, ambient temperature, and so on have a major influence on actual driving pollution. Aside from that, cold-start and hot-start have been shown to have an effect on on-road pollution. Contrary to common opinion, new technology such as start-stop systems boost automotive emissions rather than decreasing them owing to unfavourable conditions from the point of view of exhaust emissions and exhaust after-treatment systems. In addition, the driving dynamics are not represented in the current laboratory-based test procedures. As a result, it is critical to establish an on-road testing protocol to obtain a true representation of vehicular emissions and reduce emissions to a standard level. The incorporation of RDE clauses into certification procedures would have a positive impact on global air quality

The role of pharmacists in developing countries: the current scenario in Pakistan

During the past few years, the pharmacy profession has expanded significantly in terms of professional services delivery and now has been recognized as an important profession in the multidisciplinary provision of health care. In contrast to the situation in developed countries, pharmacists in developing countries are still underutilized and their role as health care professionals is not deemed important by either the community or other health care providers. The aim of this paper is to highlight the role of pharmacists in developing countries, particularly in Pakistan. The paper draws on the literature related to the socioeconomic and health status of Pakistan's population, along with background on the pharmacy profession in the country in the context of the current directions of health care

Heat transfer and pressure drop characteristics of ZnO/DIW based nanofluids in small diameter compact channels: An experimental study

This experimental study is focused on heat transfer performance and pressure drop characteristics of ZnO/DIW-based nanofluids (NFs) in horizontal mini tubes of different (1.0-2.0 mm) diameters. Different mass concentrations (0.012-0.048 wt %) of nanoparticles (NPs) were tested with varying fluid flow rates (12-24 ml/min) of NFs. The thermal conductivity (TC) and viscosity (VC) of stable NFs were tested at 20-60 °C, at a fixed temperature (40 °C), and concentration of NPs (0.048 wt%) the maximum rise was 18.27% and 20.31%, respectively. The local and average heat transfer coefficients (HTCs) and the pressure gradient were noticed to be directly proportional to volume flow rate of NFs and the mass concentration of NPs. However, an inverse trend was noticed with the test section's diameter. At 0.048 wt % of NPs and 24.0 ml/min flow rate of NFs, the maximum rise in local and average HTCs and pressure gradient was 17.11-11.61% and 13.05-9.79%, and 29.19-12.25%, respectively, in a tube's diameter of 1.0-2.0 mm. The friction factor increased with NP's loading while the same reduced with the fluid flow rate. The corresponding maximum change in the friction factor was 28.85-12.72% for the tubes with 1.0-2.0 mm diameters, respectively, at a 12.0 ml/min flow rate of NFs. The comparison of experimental findings for the HTCs, pressure gradients and friction factors with the standard Shah and Darcy's correlations showed that the observations are in good agreement with the predicted ones

In vitro bioactivity of titanium-doped bioglass

Previous studies have suggested that incorporating relatively small quantities of titanium dioxide into bioactive glasses may result in an increase in bioactivity and hydroxyapatite formation. The present work therefore investigated the in vitro bioactivity of a titanium doped bioglass and compared the results with 45S5 bioglass. Apatite formation was evaluated for bioglass and Ti-bioglass in the presence and absence of foetal calf serum. Scanning electron microscopy (SEM) images were used to evaluate the surface development and energy dispersive X-ray measurements provided information on the elemental ratios. X-ray diffraction spectra confirmed the presence of apatite formation. Cell viability was assessed for bone marrow stromal cells under direct and indirect contact conditions and cell adhesion was assessed using SEM