Email Filtering Using Hybrid Feature Selection Model

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.

Bio-inspired Hybrid Feature Selection Model for Intrusion Detection

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.

An Optimal Scheme for WSN Based on Compressed Sensing

Wireless sensor networks(WSNs)is one of the renowned ad hoc network technology that has vast varieties of applications such as in computer networks,bio-medical engineering,agriculture,industry and many more.It has been used in the internet-of-things(IoTs)applications.A method for data collecting utilizing hybrid compressive sensing(CS)is developed in order to reduce the quantity of data transmission in the clustered sensor network and balance the network load.Candidate cluster head nodes are chosen first from each temporary cluster that is closest to the cluster centroid of the nodes,and then the cluster heads are selected in order based on the distance between the determined cluster head node and the undetermined candidate cluster head node.Then,each ordinary node joins the cluster that is nearest to it.The greedy CS is used to compress data transmission for nodes whose data transmission volume is greater than the threshold in a data transmission tree with the Sink node as the root node and linking all cluster head nodes.The simulation results demonstrate that when the compression ratio is set to ten,the data transfer volume is reduced by a factor of ten.When compared to clustering and SPT without CS,it is reduced by 75%and 65%,respectively.When compared to SPT with Hybrid CS and Clustering with hybrid CS,it is reduced by 35%and 20%,respectively.Clustering and SPT without CS are compared in terms of node data transfer volume standard deviation.SPT with Hybrid CS and clustering with Hybrid CS were both reduced by 62%and 80%,respectively.When compared to SPT with hybrid CS and clustering with hybrid CS,the latter two were reduced by 41%and 19%,respectively.