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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. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) and wireless sensor networks (WSNs) have evolved rapidly due to technological breakthroughs. WSNs generate high traffic due to the growing number of sensor nodes. Congestion is one of several problems caused by the huge amount of data in WSNs. When wireless network resources are limited and IoT devices require more and more resources, congestion occurs in extremely dense WSN-based IoT networks. Reduced throughput, reduced network capacity, and reduced energy efficiency within WSNs are all effects of congestion. These consequences eventually lead to network outages due to underutilized network resources, increased network operating costs, and significantly degraded quality of service (QoS). Therefore, it is critical to deal with congestion in WSN-based IoT networks. Researchers have developed a number of approaches to address this problem, with new solutions based on artificial intelligence (AI) standing out. This research examines how new AI-based algorithms contribute to congestion mitigation in WSN-based IoT networks and the various congestion mitigation strategies that have helped reduce congestion. This study also highlights the limitations of AI-based solutions, including where and why they are used in WSNs, and a comparative study of the current literature that makes this study novel. The study concludes with a discussion of its significance and potential future study topics. The topic of congestion reduction in ultra-dense WSN-based IoT networks, as well as the current state of the art and emerging future solutions, demonstrates their significant expertise in reducing WSN congestion. These solutions contribute to network optimization, throughput enhancement, quality of service improvement, network capacity expansion, and overall WSN efficiency improvement. [ABSTRACT FROM AUTHOR]

SUMMARY Recent literature characterizes future wireless sensor networks (WSN) with dynamic spectrum capabilities. When cognitive radio is introduced as a main component of a network, a network management protocol is needed to ensure network connectivity and stability especially in highly dynamic environments. Implementing such protocols in WSN opens more challenges because of the resource constraints in sensor networks. We propose a distributed lightweight solution that fulfills this need for WSN. With this protocol, a node in a multichannel environment is quickly able to establish a control channel with neighboring nodes. Lightweight distributed geographical either increases or reduces the coverage area of the control channel based on perceived interference and adequately takes care of intersecting nodes with minimal overhead. By identifying local minima nodes, it also has the potentiality of reducing route failure by 70% further reducing the time and energy overhead incurred by switching to angle routing or maximum power transmission schemes usually used to solve the local minima issue. The work shows best operating values in terms of duty cycle and signal to noise ratio threshold frequencies and the lightweight nature of lightweight distributed geographical in terms of energy and communication overhead, which suits network management protocols for cognitive radio sensor networks. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]