Online sales and purchases are increasing daily, and they generally involve credit card transactions. This not only provides convenience to the end-user but also increases the frequency of online credit card fraud. In the recent years, in some countries, this fraud increase has led to an exponential increase in credit card fraud detection, which has become increasingly important to address this security issue. Recent studies have proposed machine learning (ML)-based solutions for detecting fraudulent credit card transactions, but their detection scores still need improvement due to the imbalance of classes in any given dataset. Few approaches have achieved exceptional results on different datasets. In this study, the Kaggle dataset was used to develop a deep learning (DL)-based approach to solve the text data problem. A novel text2IMG conversion technique is proposed that generates small images. The images are fed into a CNN architecture with class weights using the inverse frequency method to resolve the class imbalance issue. DL and ML approaches were applied to verify the robustness and validity of the proposed system. An accuracy of 99.87% was achieved by Coarse-KNN using deep features of the proposed CNN.

Modern wireless sensor networks have adopted the IEEE 802.15.4 standard. This standard defines the first two layers, the physical and medium access control layers; determines the radio wave used for communication; and defines the 128-bit advanced encryption standard (AES-128) for encrypting and validating the transmitted data. However, the standard does not specify how to manage, store, or distribute the encryption keys. Many solutions have been proposed to address this problem, but the majority are impractical in resource-constrained devices such as wireless sensor nodes or cause degradation of other metrics. Therefore, we propose an efficient and secure key distribution protocol that is simple, practical, and feasible to implement on resource-constrained wireless sensor nodes. We conduct simulations and hardware implementations to analyze our work and compare it to existing solutions based on different metrics such as energy consumption, storage overhead, key connectivity, replay attack, man-in-the-middle attack, and resiliency to node capture attack. Our findings show that the proposed protocol is secure and more efficient than other solutions.

Cyber-attacks are becoming progressively complicated; hence, the functional issues of intrusion-detection systems (IDSs) present ever-growing challenges. Failing to detect intrusions may jeopardize the trustworthiness of security services, such as privacy preservation, authenticity, and accessibility. To fight these risks, different organizations nowadays use a variety of approaches, techniques, and technologies to safeguard the systems’ credibility. Establishing policies and procedures, raising user awareness, implementing firewall and verification systems, controlling system access, and building computer-issue management groups are all examples of safeguarding methods. There is a lack of sufficient emphasis on the effectiveness of intrusion-detection systems. In enterprises, IDS is used to analyze the potentially dangerous activities taking place within the technological settings. The selection of efficient IDS is a challenging task for organizations. This research evaluates the impact of five popular IDSs for their efficiency and effectiveness in information security. The authors used the fuzzy analytical hierarchy process (AHP) and fuzzy technique for order performance by similarity to ideal solution (TOPSIS)-based integrated multi-criteria decision-making (MCDM) methodology to evaluate the efficacy of the popular IDSs. The findings of this research suggest that most of the IDSs appear to be highly potential tools. Even though Snort is extensively deployed, Suricata has a substantial advantage over Snort. Suricata uses multi-threading functionality in comparison to Snort to boost the processing performance.

Currently, fraud detection is employed in numerous domains, including banking, finance, insurance, government organizations, law enforcement, and so on. The amount of fraud attempts has recently grown significantly, making fraud detection critical when it comes to protecting your personal information or sensitive data. There are several forms of fraud issues, such as stolen credit cards, forged checks, deceptive accounting practices, card-not-present fraud (CNP), and so on. This article introduces the credit card-not-present fraud detection and prevention (CCFDP) method for dealing with CNP fraud utilizing big data analytics. In order to deal with suspicious behavior, the proposed CCFDP includes two steps: the fraud detection Process (FDP) and the fraud prevention process (FPP). The FDP examines the system to detect harmful behavior, after which the FPP assists in preventing malicious activity. Five cutting-edge methods are used in the FDP step: random undersampling (RU), t-distributed stochastic neighbor embedding (t-SNE), principal component analysis (PCA), singular value decomposition (SVD), and logistic regression learning (LRL). For conducting experiments, the FDP needs to balance the dataset. In order to overcome this issue, Random Undersampling is used. Furthermore, in order to better data presentation, FDP must lower the dimensionality characteristics. This procedure employs the t-SNE, PCA, and SVD algorithms, resulting in a speedier data training process and improved accuracy. The logistic regression learning (LRL) model is used by the FPP to evaluate the success and failure probability of CNP fraud. Python is used to implement the suggested CCFDP mechanism. We validate the efficacy of the hypothesized CCFDP mechanism based on the testing results.

Education 4.0 imitates Industry 4.0 in many aspects such as technology, customs, challenges, and benefits. The remarkable advancement in embryonic technologies, including IoT (Internet of Things), Fog Computing, Cloud Computing, and Augmented and Virtual Reality (AR/VR), polishes every dimension of Industry 4.0. The constructive impacts of Industry 4.0 are also replicated in Education 4.0. Real-time assessment, irregularity detection, and alert generation are some of the leading necessities of Education 4.0. Conspicuously, this study proposes a reliable assessment, irregularity detection, and alert generation framework for Education 4.0. The proposed framework correspondingly addresses the comparable issues of Industry 4.0. The proposed study (1) recommends the use of IoT, Fog, and Cloud Computing, i.e., IFC technological integration for the implementation of Education 4.0. Subsequently, (2) the Symbolic Aggregation Approximation (SAX), Kalman Filter, and Learning Bayesian Network (LBN) are deployed for data pre-processing and classification. Further, (3) the assessment, irregularity detection, and alert generation are accomplished over SoTL (the set of threshold limits) and the Multi-Layered Bi-Directional Long Short-Term Memory (M-Bi-LSTM)-based predictive model. To substantiate the proposed framework, experimental simulations are implemented. The experimental outcomes substantiate the better performance of the proposed framework, in contrast to the other contemporary technologies deployed for the enactment of Education 4.0.