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Email Filtering Using Hybrid Feature Selection Model

Computer Modeling in Engineering & Sciences 2022年第8期131卷 435-450页

作者： adel hamdan mohammad Sami Smadi Tariq Alwada’n Computer Science Department The World Islamic Sciences and Education UniversityAmmanJordan Information System and Networks Department The World Islamic Sciences and Education UniversityAmmanJordan Network and Cybersecurity Department Teesside UniversityMiddlesbroughUK

Undoubtedly,spam is a serious problem,and the number of spam emails is increased rapidly.Besides,the massive number of spam emails prompts the need for spam detection techniques.Several methods and algorithms are used for spam filtering.Also,some emergent spam detection techniques use machine learning methods and feature extraction.Some methods and algorithms have been introduced for spam detecting and filtering.This research proposes two models for spam detection and feature selection.The first model is evaluated with the email spam classification dataset,which is based on reducing the number of keywords to its minimum.The results of this model are promising and highly acceptable.The second proposed model is based on creating features for spam detection as a first stage.Then,the number of features is reduced using three well-known metaheuristic algorithms at the second stage.The algorithms used in the second model are Artificial Bee Colony(ABC),Ant Colony Optimization(ACO),and Particle Swarm Optimization(PSO),and these three algorithms are adapted to fit the proposed model.Also,the authors give it the names AABC,AACO,and APSO,respectively.The dataset used for the evaluation of this model is Enron.Finally,well-known criteria are used for the evaluation purposes of this model,such as true positive,false positive,false negative,precision,recall,and F-Measure.The outcomes of the second proposed model are highly significant compared to the first one.

关键词： Feature selection artificial bee colony ant colony optimization particle swarm optimization spam detection emails filtering

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Bio-inspired Hybrid Feature Selection Model for Intrusion Detection

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Computers, Materials & Continua 2022年第10期73卷 133-150页

作者： adel hamdan mohammad Tariq Alwada’n Omar Almomani Sami Smadi Nidhal ElOmari Computer Science Department The World Islamic Sciences and Education UniversityAmmanJordan Network and Cybersecurity Department Teesside UniversityMiddlesbroughUnited Kingdom Information and Network Security The World Islamic Sciences and Education UniversityAmmanJordan Software Engineering The World Islamic Sciences and Education UniversityAmmanJordan

Intrusion detection is a serious and complex problem.Undoubtedly due to a large number of attacks around the world,the concept of intrusion detection has become very important.This research proposes a multilayer bioinspired feature selection model for intrusion detection using an optimized genetic algorithm.Furthermore,the proposed multilayer model consists of two layers(layers 1 and 2).At layer 1,three algorithms are used for the feature selection.The algorithms used are Particle Swarm Optimization(PSO),Grey Wolf Optimization(GWO),and Firefly Optimization Algorithm(FFA).At the end of layer 1,a priority value will be assigned for each feature set.At layer 2 of the proposed model,the Optimized Genetic Algorithm(GA)is used to select one feature set based on the priority value.Modifications are done on standard GA to perform optimization and to fit the proposed model.The Optimized GA is used in the training phase to assign a priority value for each feature set.Also,the priority values are categorized into three categories:high,medium,and low.Besides,the Optimized GA is used in the testing phase to select a feature set based on its priority.The feature set with a high priority will be given a high priority to be selected.At the end of phase 2,an update for feature set priority may occur based on the selected features priority and the calculated F-Measures.The proposed model can learn and modify feature sets priority,which will be reflected in selecting features.For evaluation purposes,two well-known datasets are used in these experiments.The first dataset is UNSW-NB15,the other dataset is the NSL-KDD.Several evaluation criteria are used,such as precision,recall,and F-Measure.The experiments in this research suggest that the proposed model has a powerful and promising mechanism for the intrusion detection system.

关键词： Intrusion detection Machine learning Optimized Genetic Algorithm(GA) Particle Swarm Optimization algorithms(PSO) Grey Wolf Optimization algorithms(GWO) FireFly Optimization Algorithms(FFA) Genetic Algorithm(GA)

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