Securing online financial transactions has become a critical concern in an era where financial services are becoming more and more digital. The transition to digital platforms for conducting daily transactions exposed customers to possible risks from cybercriminals. This study proposed a framework that combines multi-factor authentication and machine learning to increase the safety of online financial transactions. Our methodology is based on using two layers of security. The first layer incorporates two factors to authenticate users. The second layer utilizes a machine learning component, which is triggered when the system detects a potential fraud. This machine learning layer employs facial recognition as a decisive authentication factor for further protection. To build the machine learning model, four supervised classifiers were tested: logistic regression, decision trees, random forest, and naive Bayes. The results showed that the accuracy of each classifier was 97.938%, 97.881%, 96.717%, and 92.354%, respectively. This study’s superiority is due to its methodology, which integrates machine learning as an embedded layer in a multi-factor authentication framework to address usability, efficacy, and the dynamic nature of various e-commerce platform features. With the evolving financial landscape, a continuous exploration of authentication factors and datasets to enhance and adapt security measures will be considered in future work.

In mobile networks, 5G Ultra-Dense Networks (UDNs) have emerged as they effectively increase the network capacity due to cell splitting and densification. A Base Station (BS) is a fixed transceiver that is the main communication point for one or more wireless mobile client devices. As UDNs are densely deployed, the number of BSs and communication links is dense, raising concerns about resource management with regard to energy efficiency, since BSs consume much of the total cost of energy in a cellular network. It is expected that 6G next-generation mobile networks will include technologies such as artificial intelligence as a service and focus on energy efficiency. Using machine learning it is possible to optimize energy consumption with cognitive management of dormant, inactive and active states of network elements. Reinforcement learning enables policies that allow sleep mode techniques to gradually deactivate or activate components of BSs and decrease BS energy consumption. In this work, a sleep mode management based on State Action Reward State Action (SARSA) is proposed, which allows the use of specific metrics to find the best tradeoff between energy reduction and Quality of Service (QoS) constraints. The results of the simulations show that, depending on the target of the 5G use case, in low traffic load scenarios and when a reduction in energy consumption is preferred over QoS, it is possible to achieve energy savings up to 80% with 50 ms latency, 75% with 20 ms and 10 ms latencies and 20% with 1 ms latency. If the QoS is preferred, then the energy savings reach a maximum of 5% with minimal impact in terms of latency.

In this paper, we optimize the energy efficiency (bits/s/Hz/J) of device-to-multi-device (D2MD) wireless communications. While the device-to-device scenario has been extensively studied to improve the spectral efficiency in cellular networks, the use of multicast communications opens the possibility of reusing the spectrum resources also inside the groups. The optimization problem is formulated as a mixed integer nonlinear joint optimization for the power control and allocation of resource blocks (RBs) to each group. Our model explicitly considers resource sharing by letting co-channel transmission over a RB (up to a maximum of r transmitters) and/or transmission through s different channels in each group. We use an iterative decomposition approach, using first matching theory to find a stable even if sub-optimal channel allocation, to then optimize the transmission power vectors in each group via fractional programming. Additionally, within this framework, both the network energy efficiency and the max-min individual energy efficiency are investigated. We characterize numerically the energy-efficient capacity region, and our results show that the normalized energy efficiency is nearly optimal (above 90% of the network capacity) for a wide range of minimum-rate constraints. This performance is better than that of other matching-based techniques previously proposed.

The IEEE 802.3az standard provides a mechanism to build energy efficient Ethernet interfaces via a low power idle mode that they can enter when there is no data to transmit. Several competing algorithms have appeared that make use of this mode to minimize energy consumption with little disruption to the traffic. Two algorithms stand out among those because of their simplicity and performance: frame transmission and burst transmission. Although these algorithms have been shown to be very efficient in simulated scenarios, there is a lack of general analytical models for their behavior. In fact, to this date, the only analyzed traffic patterns have been variants of Poisson traffic. In this paper we provide a general GI/G/1 model for energy consumption and traffic delay for both algorithms. We then develop specializations of the general model for Poisson and deterministic traffic. Finally, we validate the model with the help of both synthetic traffic and real Internet traffic traces.

Several recent traffic measurement studies have convincingly shown the presence of self-similarity in modern high-speed networks, involving a very important revolution in the stochastic modeling of traffic. Thus the use of self-similar processes has opened new problems and research fields in network performance analysis, mainly in simulation studies, where the efficient synthetic generation of sample paths (traces) corresponding to self-similar traffic is one of the main topics. In this article, we justify the selection of interarrival time instead of counting processes for modeling arrivals. Also, we discuss the advantages and drawbacks of the most important self-similar processes when applied to traffic modeling in simulation studies, proposing the use of models based in F-ARIMA, mainly due to their flexibility to capture both long- and short-range correlations. However, F-ARIMA processes have been little used in simulation studies, mainly because the synthetic generation methods available in the literature are very inefficient compared with those for FGN. In order to solve this problem, we propose a new method that can generate high-quality traces corresponding to a F-ARIMA(p, d, q) process. A comparison with existing methods shows that the new method is significantly more efficient, and even slightly better than the best method for FGN.