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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 combiningmachine 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 themethods 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, UNSWNB15, 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. [ABSTRACT FROM AUTHOR]

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 blockchainbased 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. [ABSTRACT FROM AUTHOR]