IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

Augmenting Internet of Medical Things Security:Deep Ensemble Integration and Methodological Fusion

When it comes to smart healthcare business systems,network-based intrusion detection systems are crucial for protecting the system and its networks from malicious network assaults.To protect IoMT devices and networks in healthcare and medical settings,our proposed model serves as a powerful tool for monitoring IoMT networks.This study presents a robust methodology for intrusion detection in Internet of Medical Things(IoMT)environments,integrating data augmentation,feature selection,and ensemble learning to effectively handle IoMT data complexity.Following rigorous preprocessing,including feature extraction,correlation removal,and Recursive Feature Elimi-nation(RFE),selected features are standardized and reshaped for deep learning models.Augmentation using the BAT algorithm enhances dataset variability.Three deep learning models,Transformer-based neural networks,self-attention Deep Convolutional Neural Networks(DCNNs),and Long Short-Term Memory(LSTM)networks,are trained to capture diverse data aspects.Their predictions form a meta-feature set for a subsequent meta-learner,which combines model strengths.Conventional classifiers validate meta-learner features for broad algorithm suitability.This comprehensive method demonstrates high accuracy and robustness in IoMT intrusion detection.Evaluations were conducted using two datasets:the publicly available WUSTL-EHMS-2020 dataset,which contains two distinct categories,and the CICIoMT2024 dataset,encompassing sixteen categories.Experimental results showcase the method’s exceptional performance,achieving optimal scores of 100%on the WUSTL-EHMS-2020 dataset and 99%on the CICIoMT2024.

A Novel Hybrid Ensemble Learning Approach for Enhancing Accuracy and Sustainability in Wind Power Forecasting

Accurate wind power forecasting is critical for system integration and stability as renewable energy reliance grows.Traditional approaches frequently struggle with complex data and non-linear connections. This article presentsa novel approach for hybrid ensemble learning that is based on rigorous requirements engineering concepts.The approach finds significant parameters influencing forecasting accuracy by evaluating real-time Modern-EraRetrospective Analysis for Research and Applications (MERRA2) data from several European Wind farms usingin-depth stakeholder research and requirements elicitation. Ensemble learning is used to develop a robust model,while a temporal convolutional network handles time-series complexities and data gaps. The ensemble-temporalneural network is enhanced by providing different input parameters including training layers, hidden and dropoutlayers along with activation and loss functions. The proposed framework is further analyzed by comparing stateof-the-art forecasting models in terms of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE),respectively. The energy efficiency performance indicators showed that the proposed model demonstrates errorreduction percentages of approximately 16.67%, 28.57%, and 81.92% for MAE, and 38.46%, 17.65%, and 90.78%for RMSE for MERRAWind farms 1, 2, and 3, respectively, compared to other existingmethods. These quantitativeresults show the effectiveness of our proposed model with MAE values ranging from 0.0010 to 0.0156 and RMSEvalues ranging from 0.0014 to 0.0174. This work highlights the effectiveness of requirements engineering in windpower forecasting, leading to enhanced forecast accuracy and grid stability, ultimately paving the way for moresustainable energy solutions.

Fatty Acid Profiling of Polyethylene Glycol Adapted and Un-Adapted Cell Lines of <i>Oryza sativa</i>L.cv. Swat-1 under Temperature Stress

Higher plants can adapt to abiotic stress to a certain degree. In this study, the impact of temperature stress on osmotic stress adapted and un-adapted cell lines of rice (Oryza sativa L.cv Swat-1) was observed. For the change in proline content, relative growth rate, saturated and unsaturated fatty acid were evaluated. The cell lines were incrementally adapted to 20% polyethylene glycol. The adapted lines showed significantly higher growth rate and proline content as compared to the un-adapted cell lines on temperature stress. Among saturated fatty acids palmitic acid (C16:0), stearic acid (C18:0) and myristic acid (C14:0) were the prominent fatty acids detected while among unsaturated fatty acid Oleic acid (C18:1c) and Linoleic acid (C18:2c) were the major fatty acids found. Under low temperature stress the percentage of saturated fatty acids was found to be lower (53%) in adapted cell line as compared to the un-adapted cell line (63%) while the percentage of saturation increased (83%) in adapted line under high temperature stress as compared to un-adapted line (70%). On the other hand at low temperature stress the percent level of unsaturated fatty acids in the adapted line was higher (48%) than the un-adapted cell line (37%). In conclusion, adaptation to one abiotic stress confers co-tolerance to the other abiotic stresses. Fatty acids saturation level could be a crucial factor in the plant ability to tolerate heat and cold stress.

Histone deacetylase HDA710 controls salt tolerance by regulating ABA signaling in rice

Plants have evolved numerous mechanisms that assist them in withstanding environmental stresses.Histone deacetylases(HDACs)play crucial roles in plant stress responses;however,their regulatory mechanisms remain poorly understood.Here,we explored the function of HDA710/OsHDAC2,a member of the HDAC RPD3/HDA1 family,in stress tolerance in rice(Oryza sativa).We established that HDA710 localizes to both the nucleus and cytoplasm and is involved in regulating the acetylation of histone H3 and H4,specifically targeting H4 K5 and H4 K16 under normal conditions.HDA710 transcript accumulation levels were strongly induced by abiotic stresses including drought and salinity,as well as by the phytohormones jasmonic acid(JA)and abscisic acid(ABA).hda710 knockout mutant plants showed enhanced salinity tolerance and reduced ABA sensitivity,whereas transgenic plants overexpressing HDA710 displayed the opposite phenotypes.Moreover,ABAand salt-stress-responsive genes,such as OsLEA3,OsABI5,OsbZIP72,and OsNHX1,were upregulated in hda710 compared with wild-type plants.These expression differences corresponded with higher levels of histone H4 acetylation in gene promoter regions in hda710 compared with the wild type under ABA and salt-stress treatment.Collectively,these results suggest that HDA710 is involved in regulating ABA-and salt-stress-responsive genes by altering H4 acetylation levels in their promoters.