Hydrangea serrata extract exerts tumor inhibitory activity against hepatocellular carcinoma HepG2 cells via inducing p27/CDK2-mediated cell cycle arrest and apoptosis

Objective:To examine the inhibitory effect of Hydrangea serrata extract against hepatocellular carcinoma HepG2 cells and its underlying mechanisms.Methods:The effects of Hydrangea serrata extract on growth inhibition of tumor cells and spheroids were assessed using MTT and 3D culture assays.Quantitative real-time PCR and Western blot analyses were employed to investigate the changes in mRNA and protein expression levels of molecules related to cell cycle and apoptosis.Results:Hydrangea serrata extract effectively inhibited the growth of both tumor cells and spheroids.The extract also significantly upregulated p27 mRNA expression and downregulated CDK2 mRNA expression,leading to cell cycle arrest.Moreover,increased BAX/Bcl-2 ratio as well as caspase-9 and-3 were observed after treatment with Hydrangea serrata extract,indicating the induction of tumor cell apoptosis.Conclusions:Hydrangea serrata extract has the potential to alleviate tumors by effectively modulating cell-cycle-related gene expressions and inducing apoptosis,thereby inhibiting tumor growth.

A Model Training Method for DDoS Detection Using CTGAN under 5GC Traffic

With the commercialization of 5th-generation mobile communications(5G)networks,a large-scale internet of things(IoT)environment is being built.Security is becoming increasingly crucial in 5G network environments due to the growing risk of various distributed denial of service(DDoS)attacks across vast IoT devices.Recently,research on automated intrusion detection using machine learning(ML)for 5G environments has been actively conducted.However,5G traffic has insufficient data due to privacy protection problems and imbalance problems with significantly fewer attack data.If this data is used to train an ML model,it will likely suffer from generalization errors due to not training enough different features on the attack data.Therefore,this paper aims to study a training method to mitigate the generalization error problem of the ML model that classifies IoT DDoS attacks even under conditions of insufficient and imbalanced 5G traffic.We built a 5G testbed to construct a 5G dataset for training to solve the problem of insufficient data.To solve the imbalance problem,synthetic minority oversampling technique(SMOTE)and generative adversarial network(GAN)-based conditional tabular GAN(CTGAN)of data augmentation were used.The performance of the trained ML models was compared and meaningfully analyzed regarding the generalization error problem.The experimental results showed that CTGAN decreased the accuracy and f1-score compared to the Baseline.Still,regarding the generalization error,the difference between the validation and test results was reduced by at least 1.7 and up to 22.88 times,indicating an improvement in the problem.This result suggests that the ML model training method that utilizes CTGANs to augment attack data for training data in the 5G environment mitigates the generalization error problem.

Detecting IoT Botnet in 5G Core Network Using Machine Learning

As Internet of Things(IoT)devices with security issues are connected to 5G mobile networks,the importance of IoT Botnet detection research in mobile network environments is increasing.However,the existing research focused on AI-based IoT Botnet detection research in wired network environments.In addition,the existing research related to IoT Botnet detection in ML-based mobile network environments have been conducted up to 4G.Therefore,this paper conducts a study on ML-based IoT Botnet traffic detection in the 5G core network.The binary and multiclass classification was performed to compare simple normal/malicious detection and normal/threetype IoT Botnet malware detection.In both classification methods,the IoT Botnet detection performance using only 5GC’s GTP-U packets decreased by at least 22.99%of accuracy compared to detection in wired network environment.In addition,by conducting a feature importance experiment,the importance of feature study for IoT Botnet detection considering 5GC network characteristics was confirmed.Since this paper analyzed IoT botnet traffic passing through the 5GC network using ML and presented detection results,think it will be meaningful as a reference for research to link AI-based security to the 5GC network.