The phishing attack is one of the most complex threats that have put internet users and legitimate web resource owners at risk. The recent rise in the number of phishing attacks has instilled distrust in legitimate internet users, making them feel less safe even in the presence of powerful antivirus apps. Reports of a rise in financial damages as a result of phishing website attacks have caused grave concern. Several methods, including blacklists and machine learning-based models, have been proposed to combat phishing website attacks. The blacklist antiphishing method has been faulted for failure to detect new phishing URLs due to its reliance on compiled blacklisted phishing URLs. Many ML methods for detecting phishing websites have been reported with relatively low detection accuracy and high false alarm. Hence, this research proposed a Functional Tree (FT) based metalearning models for detecting phishing websites. That is, this study investigated improving the phishing website detection using empirical analysis of FT and its variants. The proposed models outperformed baseline classifiers, meta-learners and hybrid models that are used for phishing websites detection in existing studies. Besides, the proposed FT based meta-learners are effective for detecting legitimate and phishing websites with accuracy as high as 98.51% and a false positive rate as low as 0.015. Hence, the deployment and adoption of FT and its metalearner variants for phishing website detection and applicable cybersecurity attacks are recommended.

Wireless Sensor Networks (WSNs) consist of huge number of sensor nodes dispersed in a domain of enthusiasm with at least one sink for watching the environment and physical situation. These sensor hubs are circulated in threatening conditions and are unprotected to deficiencies, for example, power dissemination, equipment glitches, communication link errors and malicious attacks, among others. It has been established that essentialness, speed and unwavering quality are the chief test in the usefulness of WSNs as they are controlled with compelled imperativeness and restricted equipment assets. Accordingly, it is necessary to structure vitality proficient steering conventions for WSNs applications. Chinese Remainder Theorem (CRT)- based packet splitting integrated with Low-Energy Adaptive Clustering Hierarchy (LEACH) algorithm routing protocol was proposed so as to decrease vitality utilization during correspondence and improve message dependability in WSNs. The consequences of exploratory reproductions show that the proposed structure delivered powerful directing convention for WSNs when contrasted with existing routing protocols to the extent essentialness usage, speed, equipment necessities and transformation delay continuously WSNs.

Fused biometrics systems have proven to solve some problems associated with unimodal systems but also face challenges in various aspects of their implementation such as difficulty in design, user acceptance is quite low, and the performance tradeoff. This framework tends to address some of these implementation challenges by using an enhanced mayfly algorithm, a modification of the existing mayfly algorithm that was recently proposed, as feature selection. Mayfly algorithm combines advantages of particle swarm optimization, genetic algorithm, and firefly algorithm, simulated in different experiments using varied benchmark function on conventional mayfly algorithm all tested to be capable of optimization, but despite its capabilities, some limitations such as slow convergent or premature convergent rate and possible imbalance between exploration and exploitation still remain unresolved, which necessitated enhancement for better performance. This framework will enhance the existing mayfly algorithm by expanding the search space which limited the ability of the conventional mayfly algorithm to be used to solve high-dimensional problem spaces such as feature selection and modify the selection procedure to model the attraction process as a deterministic process, that will be used for the feature selection procedure on fused face –iris recognition system. This will increase the capabilities of the mayfly algorithm and in turn, increase the recognition accuracy, and reduced the false acceptance rate, false rejection rate, and time complexity of the fused face–iris recognition system.

No abstract

No abstract