The exponential growth of the Internet of Things (IoT) sector has resulted in a surge of interconnected gadgets in smart households, thus exposing them to new cyber-attack susceptibilities. This systematic literature review investigates machine learning methodologies for detecting malware in smart homes, with a specific emphasis on identifying common threats such as denial-of-service attacks, phishing efforts, and zero-day vulnerabilities. By examining 56 publications published from 2019 to 2023, this analysis uncovers that users are the weakest link and that there is a possibility of attackers disrupting home automation systems, stealing confidential information, or causing physical harm. Machine learning approaches, namely, deep learning and ensemble approaches, are emerging as effective tools for detecting malware. In addition, this analysis highlights prevention techniques, such as early threat detection systems, intrusion detection systems, and robust authentication procedures, as crucial measures for improving smart home security. This study offers significant insights for academics and practitioners aiming to protect smart home settings from growing cybersecurity threats by summarizing the existing knowledge.

Nowadays, credit card transactions are exponentially increasing as a form of online payment. That creates technical pressure on the financial institution to incur losses due to credit card fraud. It is no wonder that credit card fraud is making people feel insecure and unsafe about using the services provided by banks. Data mining reasons on offline and online transactions can cause fraud detection. Number one is the changes in the behaviour of the frauds that are changing, and number two is the fraudulent dataset, which is asymmetric. In addition, the variables and techniques used by the researcher can impact fraud detection activities for credit cards. Therefore, the paper intends to investigate the suitability of the k-nearest-neighbour, decision tree, support vector machine, logistic regression, and Catboost. The data set has a total of 284807 transactions coming from the European credit card holder. Evaluation of the performance can be measured through the specificity, accuracy, sensitivity, precision, and lastly, the recall rate. After completing the comprehensive cross-validation, it was discovered that the catboost’s accuracy was extraordinary, which amounted to 93.39%, leaving the other classification matrix far behind.

As computer networks become increasingly important in various domains, the need for secure and reliable networks becomes more pressing, particularly in the context of blockchain-enabled supply chain networks. One way to ensure network security is by using intrusion detection systems (IDSs), which are specialised devices that detect anomalies and attacks in the network. However, these systems are vulnerable to data poisoning attacks, such as label and distance-based flipping, which can undermine their effectiveness within blockchain-enabled supply chain networks. In this research paper, we investigate the effect of these attacks on a network intrusion detection system using several machine learning models, including logistic regression, random forest, SVC, and XGB Classifier, and evaluate each model via their F1 Score, confusion matrix, and accuracy. We run each model three times: once without any attack, once with random label flipping with a randomness of 20%, and once with distance-based label flipping attacks with a distance threshold of 0.5. Additionally, this research tests an eight-layer neural network using accuracy metrics and a classification report library. The primary goal of this research is to provide insights into the effect of data poisoning attacks on machine learning models within the context of blockchain-enabled supply chain networks. By doing so, we aim to contribute to developing more robust intrusion detection systems tailored to the specific challenges of securing blockchain-based supply chain networks.

There has been very limited research conducted to predict rental prices in the German real estate market using an AI-based approach. From a general perspective, conventional approaches struggle to handle large amounts of data and fail to consider the numerous elements that affect rental prices. The absence of sophisticated, data-driven analytical tools further complicates this situation, impeding stakeholders, such as tenants, landlords, real estate agents, and the government, from obtaining the accurate insights necessary for making well-informed decisions in this area. This paper applies novel machine learning (ML) approaches, including ensemble techniques, neural networks, linear regression (LR), and tree-based algorithms, specifically designed for forecasting rental prices in Munich. To ensure accuracy and reliability, the performance of these models is evaluated using the R² score and root mean squared error (RMSE). The study provides two feature sets for model comparison, selected by particle swarm optimisation (PSO) and CatBoost. These two feature selection methods identify significant variables based on different mechanisms, such as seeking the optimal solution with an objective function and converting categorical features into target statistics (TSs) to address high-dimensional issues. These methods are ideal for this German dataset, which contains 49 features. Testing the performance of 10 ML algorithms on two sets helps validate the robustness and efficacy of the AI-based approach utilising the PyTorch framework. The findings illustrate that ML models combined with PyTorch-based neural networks (PNNs) demonstrate high accuracy compared to standalone ML models, regardless of feature changes. The improved performance indicates that utilising the PyTorch framework for predictive tasks is advantageous, as evidenced by a statistical significance test in terms of both R² and RMSE (p-values < 0.001). The integration results display outstanding accuracy, averaging 90% across both feature sets. Particularly, the XGB model, which exhibited the lowest performance among all models in both sets, significantly improved from 0.8903 to 0.9097 in set 1 and from 0.8717 to 0.9022 in set 2 after being combined with the PNN. These results showcase the efficacy of using the PyTorch framework, enhancing the precision and reliability of the ML models in predicting the dynamic real estate market. Given that this study applies two feature sets and demonstrates consistent performance across sets with varying characteristics, the methodology may be applied to other locations. By offering accurate projections, it aids investors, renters, property managers, and regulators in facilitating better decision-making in the real estate sector.