A Meta-Classification Model for Optimized ZBot Malware Prediction Using Learning Algorithms

Abstract

Botnets pose a real threat to cybersecurity by facilitating criminal activities like malware distribution, attacks involving distributed denial of service, fraud, click fraud, phishing, and theft identification. The methods currently used for botnet detection are only appropriate for specific botnet commands and control protocols; they do not endorse botnet identification in early phases. Security guards have used honeypots successfully in several computer security defence systems. Honeypots are frequently utilised in botnet defence because they can draw botnet compromises, reveal spies in botnet membership, and deter attacker behaviour. Attackers who build and maintain botnets must devise ways to avoid honeypot traps. Machine learning methods support identification and inhibit bot threats to address the problems associated with botnet attacks. To choose the best features to feed as input to the machine learning classifiers to estimate the performance of botnet detection, a Kernel-based Ensemble Meta Classifier (KEMC) Strategy is suggested in this work. And particle swarm optimization (PSO) and genetic algorithm (GA) intelligent optimization algorithms are used to establish the ideal order. The model covered in this paper is employed to forecast Internet cyber security circumstances. The Binary Cross-Entropy (loss), the GA-PSO optimizer, the Softsign activation functions and ensembles were used in the experiment to produce the best results. The model succeeded because Forfileless malware, gathered from well-known datasets, achieved a total accuracy of 93.3% with a True Positive (TP) Range of 87.45% at zero False Positive (FP).

Predictive modelling and identification of key risk factors for stroke using machine learning

“…This model is skilled at blending the predictions of other models in a way that optimizes their collective predictive strength. The Blending ensemble essentially learns how to combine the different model outputs best, capitalizing on the unique strengths of each model to enhance the overall predictive accuracy of the DSE model 46 .…”