MoBShield:A Novel XML Approach for Securing Mobile Banking

Mobile banking security has witnessed significant R&D attention from both financial institutions and academia.This is due to the growing number of mobile baking applications and their reachability and usefulness to society.However,these applications are also attractive prey for cybercriminals,who use a variety of malware to steal personal banking information.Related literature in mobile banking security requiresmany permissions that are not necessary for the application’s intended security functionality.In this context,this paper presents a novel efficient permission identification approach for securing mobile banking(MoBShield)to detect and prevent malware.A permission-based dataset is generated for mobile banking malware detection that consists large number of malicious adware apps and benign apps to use as training datasets.The dataset is generated from 1650 malicious banking apps of the Canadian Institute of Cybersecurity,University of New Brunswick and benign apps from Google Play.A machine learning algorithm is used to determine whether amobile banking application ismalicious based on its permission requests.Further,an eXplainable machine learning(XML)approach is developed to improve trust by explaining the reasoning behind the algorithm’s behaviour.Performance evaluation tests that the approach can effectively and practically identify mobile banking malware with high precision and reduced false positives.Specifically,the adapted artificial neural networks(ANN),convolutional neural networks(CNN)and XML approaches achieve a higher accuracy of 99.7%and the adapted deep neural networks(DNN)approach achieves 99.6%accuracy in comparison with the state-of-the-art approaches.These promising results position the proposed approach as a potential tool for real-world scenarios,offering a robustmeans of identifying and thwarting malware inmobile-based banking applications.Consequently,MoBShield has the potential to significantly enhance the security and trustworthiness of mobile banking platforms,mitigating the risks posed by cyber threats and ensuring a safer user experience.

Incorporation of 3-dimensional lycopodium with hydrophobic nature and interconnected nano-channels into polyvinylidene fluoride membranes for desalination applications by vacuum membrane distillation

In the present research,for the first time,lycopodium as a novel nanofiller was incorporated into a polyvinylidene fluoride matrix to fabricate lycopodium/polyvinylidene fluoride flat-sheet membrane for desalination applications by vacuum membrane distillation process.The prepared lycopodium/polyvinylidene fluoride membranes and lycopodium were characterized by field emission scanning electron microscopy,X-ray diffraction,Fourier transform infrared,energy dispersive X-ray,and mapping analyses.Water contact angle and liquid entry pressure measurements were also performed.Response surface methodology was applied to optimize membrane structure and performance.The optimized lycopodium/polyvinylidene fluoride membrane exhibits superior performance compared to the neat polyvinylidene fluoride membrane in terms of flux,salt rejection,water contact angle,and hydrophobicity.In vacuum membrane distillation experiments,using a 15000 ppm NaCl solution as a feed at 70℃,the neat polyvinylidene fluoride membrane,optimum membrane,and agglomerated membrane(with high lycopodium loading)demonstrated 3.80,25.20,and 14.83 LMH flux and 63.30%,99.99%,99.96%salt rejection,respectively.This improvement in flux and salt rejection of the optimized membrane was related to the presence of lycopodium with hydrophobic nature and interconnected nano-channels in membrane structure.It was found that lycopodium,as the most hydrophobic material,effectively influences the membrane performance and structure for membrane distillation applications.