This study presents a novel feature-engineered–natural gradient descent ensemble-boosting (NGBoost) machine-learning framework for detecting fraud in power consumption data. The proposed framework was sequentially executed in three stages: data pre-processing, feature engineering, and model evaluation. It utilized the random forest algorithm-based imputation technique initially to impute the missing data entries in the acquired smart meter dataset. In the second phase, the majority weighted minority oversampling technique (MWMOTE) algorithm was used to avoid an unequal distribution of data samples among different classes. The time-series feature-extraction library and whale optimization algorithm were utilized to extract and select the most relevant features from the kWh reading of consumers. Once the most relevant features were acquired, the model training and testing process was initiated by using the NGBoost algorithm to classify the consumers into two distinct categories (“Healthy” and “Theft”). Finally, each input feature’s impact (positive or negative) in predicting the target variable was recognized with the tree SHAP additive-explanations algorithm. The proposed framework achieved an accuracy of 93%, recall of 91%, and precision of 95%, which was greater than all the competing models, and thus validated its efficacy and significance in the studied field of research.

A Taxonomy of Virtualization Security Issues in Cloud Computing Environments

The increasing number of security holes in the Internet of Things (IoT) networks creates a question about the reliability of existing network intrusion detection systems. This problem has led to the developing of a research area focused on improving network-based intrusion detection system (NIDS) technologies. According to the analysis of different businesses, most researchers focus on improving the classification results of NIDS datasets by combining machine learning and feature reduction techniques. However, these techniques are not suitable for every type of network. In light of this, whether the optimal algorithm and feature reduction techniques can be generalized across various datasets for IoT networks remains. The paper aims to analyze the methods used in this research and whether they can be generalized to other datasets. Six ML models were used in this study, namely, logistic regression (LR), decision trees (DT), Naive Bayes (NB), random forest (RF), K-nearest neighbors (KNN), and linear SVM. The primary detection algorithms used in this study, Principal Component (PCA) and Gini Impurity-Based Weighted Forest (GIWRF) evaluated against three global ToN-IoT datasets, UNSW-NB15, and Bot-IoT datasets. The optimal number of dimensions for each dataset was not studied by applying the PCA algorithm. It is stated in the paper that the selection of datasets affects the performance of the FE techniques and detection algorithms used. Increasing the efficiency of this research area requires a comprehensive standard feature set that can be used to improve quality over time.

There has been an increase in the adoption of mobile payment systems worldwide in the past few years. However, poor Internet connection in rural regions continues to be an obstacle to the widespread use of such technologies. On top of that, there are significant problems with the currently available offline wallets; for instance, the payee cannot verify the number of coins received without access to the Internet. Additionally, it has been demonstrated that some existing systems are susceptible to false token generation, and some do not even permit the user to divide the offline token into smaller portions to be used as change. This paper proposes a blockchain-based wallet system that provides a secure mobile payment service even if a user cannot access a reliable Internet connection. Our approach relies on Bluetooth and digital signatures to establish and build a trust connection between the parties. The proposed solution overcomes the main limitations of existing systems that use offline transactions, such as the generation of fake offline tokens and the indivisibility of offline tokens. The user buys Offline Tokens (OTs) from a server called an Offline Token Manager (OTM) to use them later to perform offline transactions. Each mobile device must store a single, signed offline token transaction to prevent fake tokens. On the other hand, all offline transactions will be kept as a history in a particular local database. Finally, when the receiver becomes online, it will send a convert request to the OTM to change the value of the OTs to the appropriate amount in real coins. This step requires a connection to the Internet. To evaluate the effectiveness of the system, the Solidity programming language was used to develop a smart contract on the Ethereum blockchain with a backend application programming interface (API) and an android mobile application. The proposed method has an advantage over other prominent wallets.

Effective intervention and detection of developmental disabilities (DDs) should be carried out early and accurately. This paper discusses a novel AI-based approach for detecting DDs precisely, by incorporating both speech and behavioral data in a unique manner. The new architecture comprises several key stages, with innovations attributed to the system: Enhanced Gaussian-Based Noise Filtering (EGNF) is used to enhance speech; Conv-BiLSTM and Transformer Embeddings for performing strong feature extraction from speech and behavior; Harmony-ReliefF Optimization (HRO) for feature selection, and BioNeuroFusionNet (BNFN) as a novel classification network integrating IndRNN and MnasNet, with GNA selected as the class superiors. The Harmony-ReliefF Optimization (HRO) strategy combines the exploration capabilities of the Harmony Search Algorithm (HSA) with the feature ranking ability of ReliefF for optimizing the feature space. The features selected and optimally extracted are then fed into the novel BioNeuroFusionNet (BNFN) intended for final classification. The BNFN is another significant contribution of this work, where Independently Recurrent Neural Networks (IndRNN) are incorporated for fast processing of sequential data set and MnasNet (MN) for extracting high-level abstract features. The experimental result indicates that this integrated architecture works effectively with high accuracy (0.98942 for voice; 0.99632 for behavior) and very high scores in precision, recall, specificity, and other relevant metrics. Indeed, these results indicate that the system proposed in this work represents a considerable improvement in the area of early diagnosis and intervention of developmental disorders.

Most remote systems require user authentication to access resources. Text-based passwords are still widely used as a standard method of user authentication. Although conventional text-based passwords are rather hard to remember, users often write their passwords down in order to compromise security. One of the most complex challenges users may face is posting sensitive data on external data centers that are accessible to others and do not be controlled directly by users. Graphical user authentication methods have recently been proposed to verify the user identity. However, the fundamental limitation of a graphical password is that it must have a colorful and rich image to provide an adequate password space to maintain security, and when the user clicks and inputs a password between two possible grids, the fault tolerance is adjusted to avoid this situation. This paper proposes an enhanced graphical authentication scheme, which comprises benefits over both recognition and recall-based graphical techniques besides image steganography. The combination of graphical authentication and steganography technologies reduces the amount of sensitive data shared between users and service providers and improves the security of user accounts. To evaluate the effectiveness of the proposed scheme, peak signal-to-noise ratio and mean squared error parameters have been used.