Robust genetic machine learning ensemble model for intrusion detection in network traffic.

Network security has developed as a critical research subject as a result of the Rapid advancements in the development of Internet and communication technologies over the previous decades. The expansion of networks and data has caused cyber-attacks on the systems, making it difficult for network security to detect breaches effectively. Current Intrusion Detection Systems (IDS) have several flaws, including their inability to prevent attacks on their own, the requirement for a professional engineer to administer them, and the occurrence of false alerts. As a result, a plethora of new attacks are being created, making it harder for network security to properly detect breaches. Despite the best efforts, IDS continues to struggle with increasing detection accuracy while lowering false alarm rates and detecting new intrusions. Therefore, network intrusion detection enhancement by preprocessing and generation of highly reliable algorithms is the main focus nowadays. Machine learning (ML) based IDS systems have recently been implemented as viable solutions for quickly detecting intrusions across the network. In this study, we use a combined data analysis technique with four Robust Machine learning ensemble algorithms, including the Voting Classifier, Bagging Classifier, Gradient Boosting Classifier, and Random Forest-based Bagging algorithm along with the proposed Robust genetic ensemble classifier. For each algorithm, a model is created and tested using a Network Dataset. To assess the performance of both algorithms in terms of their ability to anticipate the anomaly occurrence, graphs of performance rates have been evaluated. The suggested algorithm outperformed other methods as it shows the lowest values of mean square error (MSE) and mean absolute error (MAE). The experiments were conducted on the Network traffic dataset available on Kaggle, on the Python platform, which has limited samples. The proposed method can be applied in the future with more machine learning ensemble classifiers and deep learning techniques.

Group theoretical analysis for unsteady magnetohydrodynamics flow and radiative heat transfer of power-law nanofluid subject to Navier's slip conditions.

The present work covers the flow and heat transfer model for the Power-law nanofluid in the presence of a porous medium over a penetrable plate. The flow is caused by the impulsive movement of the plate embedded in Darcy's porous medium. The flow and heat transfer models are examined with the effect of linear thermal radiation in the flow regime. The Rosseland approximation is utilized for the optically thick nanofluid. The governing partial differential equations are solved using Lie symmetry analysis to find the reductions and invariants for the closed-form solutions. These invariants are then utilized to obtain the exact solutions for the shear-thinning, Newtonian, and shear-thickening nanofluids. In the end, all solutions are plotted for the Cu-water nanofluid to observe the effect of different emerging flow and heat transfer parameters.

SLC41A1 is the only magnesium responsive gene significantly overexpressed in placentas of preeclamptic women.

OBJECTIVE: To examine expression profile of magnesium responsive genes (MRGs) in placentas of normoevolutive and preeclamptic women. METHODS: The expression profiles of MRGs were determined in placentas of normoevolutive (N=26) and preeclamptic (N=25) women by RT-qPCR. RESULTS: Among all tested MRGs (9) only SLC41A1 (encoding for Na(+)/Mg(2+) exchanger) was significantly overexpressed in ~54.2% of preeclamptic (n=24) and in ~9.5% of normoevolutive (n=21) specimens. On average, SLC41A1 was overexpressed sixfold in the preeclamptic group. Presence of SLC41A1 in placentas was confirmed by Western blot analysis. CONCLUSION. SLC41A1 is significantly overexpressed in nearly 55% of preeclamptic placentas. This may indicate a direct contribution of changed Mg homeostasis in the development of preeclampsia.