Spambase Dataset Research Articles

Robust Email Spam Filtering Using a Hybrid of Grey Wolf Optimiser and Naive Bayes Classifier

Abstract Effective spam filtering plays a crucial role in enhancing user experience by sparing them from unwanted messages. This imperative underscores the importance of safeguarding email systems, prompting scholars across diverse fields to delve deeper into this subject. The primary objective of this research is to mitigate the disruptive effects of spam on email usage by introducing improved security measures compared to existing methods. This goal can be accomplished through the development of a novel spam filtering technique designed to prevent spam from infiltrating users’ inboxes. Consequently, a hybrid filtering approach that combines an information gain philter and a Wrapper Grey Wolf Optimizer feature selection algorithm with a Naive Bayes Classifier, is proposed, denoted as GWO-NBC. This research is rigorously tested using the WEKA software and the SPAMBASE dataset. Thorough performance evaluations demonstrated that the proposed approach surpasses existing solutions in terms of both security and accuracy.

An Improved Dandelion Optimizer Algorithm for Spam Detection: Next-Generation Email Filtering System

Spam emails have become a pervasive issue in recent years, as internet users receive increasing amounts of unwanted or fake emails. To combat this issue, automatic spam detection methods have been proposed, which aim to classify emails into spam and non-spam categories. Machine learning techniques have been utilized for this task with considerable success. In this paper, we introduce a novel approach to spam email detection by presenting significant advancements to the Dandelion Optimizer (DO) algorithm. The DO is a relatively new nature-inspired optimization algorithm inspired by the flight of dandelion seeds. While the DO shows promise, it faces challenges, especially in high-dimensional problems such as feature selection for spam detection. Our primary contributions focus on enhancing the DO algorithm. Firstly, we introduce a new local search algorithm based on flipping (LSAF), designed to improve the DO’s ability to find the best solutions. Secondly, we propose a reduction equation that streamlines the population size during algorithm execution, reducing computational complexity. To showcase the effectiveness of our modified DO algorithm, which we refer to as the Improved DO (IDO), we conduct a comprehensive evaluation using the Spam base dataset from the UCI repository. However, we emphasize that our primary objective is to advance the DO algorithm, with spam email detection serving as a case study application. Comparative analysis against several popular algorithms, including Particle Swarm Optimization (PSO), the Genetic Algorithm (GA), Generalized Normal Distribution Optimization (GNDO), the Chimp Optimization Algorithm (ChOA), the Grasshopper Optimization Algorithm (GOA), Ant Lion Optimizer (ALO), and the Dragonfly Algorithm (DA), demonstrates the superior performance of our proposed IDO algorithm. It excels in accuracy, fitness, and the number of selected features, among other metrics. Our results clearly indicate that the IDO overcomes the local optima problem commonly associated with the standard DO algorithm, owing to the incorporation of LSAF and the reduction in equation methods. In summary, our paper underscores the significant advancement made in the form of the IDO algorithm, which represents a promising approach for solving high-dimensional optimization problems, with a keen focus on practical applications in real-world systems. While we employ spam email detection as a case study, our primary contribution lies in the improved DO algorithm, which is efficient, accurate, and outperforms several state-of-the-art algorithms in various metrics. This work opens avenues for enhancing optimization techniques and their applications in machine learning.

A Lightweight Machine Learning-Based Email Spam Detection Model Using Word Frequency Pattern

This Spam emails have become a severe challenge that irritates and consumes recipients' time. On the one hand, existing spam detection techniques have low detection rates and cannot tolerate high-dimensional data. Moreover, due to the machine learning algorithm's effectiveness in identifying mail as solicited or unsolicited, their approaches have become common in spam detection systems. This paper proposes a lightweight machine learning-based spam detection model based on Random Forest (RF) algorithm. According to the empirical results, the proposed model achieved a 97% accuracy on the spambase dataset. Furthermore, the performance of the proposed model was evaluated using standard classification metrics such as Fscore, Recall, Precision, and Accuracy. The comparison of Our model with state-of-the-art works investigated in this paper showed the model performs better, with an improvement of 6% for all metrics.

Highly Accurate Spam Detection with the Help of Feature Selection and Data Transformation

The amount of spam is increasing rapidly while the popularity of emails is increasing. This situation has led to the need to filter spam emails. To date, many knowledge-based, learning-based, and clustering-based methods have been developed for filtering spam emails. In this study, machine-learning-based spam detection was targeted, and C4.5, ID3, RndTree, C-Support Vector Classification (C-SVC), and Naïve Bayes algorithms were used for email spam detection. In addition, feature selection and data transformation methods were used to increase spam detection success. Experiments were performed on the UC Irvine Machine Learning Repository (UCI) spambase dataset, and the results were compared for accuracy, Receiver Operating Characteristic (ROC) analysis, and classification speed. According to the accuracy comparison, the C-SVC algorithm gave the highest accuracy with 93.13%, followed by the RndTree algorithm. According to the ROC analysis, the RndTree algorithm gave the best Area Under Curve (AUC) value of 0.999, while the C4.5 algorithm gave the second-best result. The most successful methods in terms of classification speed are Naïve Bayes and RndTree algorithms. In the experiments, it was seen that feature selection and data transformation methods increased spam detection success. The binary transformation that increased the classification success the most and the feature selection method was forward selection.

A Multi-Layer Perceptron Model for Classification of E-mail Fraud

This study was developed an e-mail classification model to preempt fraudulent activities. The e-mail has such a predominant nature that makes it suitable for adoption by cyber-fraudsters. This research used a combination of two databases: CLAIR fraudulent and Spambase datasets for creating the training and testing dataset. The CLAIR dataset consists of raw e-mails from users’ inbox which were pre-processed into structured form using Natural Language Processing (NLP) techniques. This dataset was then consolidated with the Spambase dataset as a single dataset. The study deployed the Multi-Layer Perceptron (MLP) architecture which used a back-propagation algorithm for training the fraud detection model. The model was simulated using 70% and 80% for training while 30% and 20% of datasets were used for testing respectively. The results of the performance of the models were compared using a number of evaluation metrics. The study concluded that using the MLP, an effective model for fraud detection among e-mail dataset was proposed.

A Clustering Algorithm in Stream Data Using Strong Coreset

There are various applications of clustering in the fields of machine learning, data mining, data compression along with pattern recognition. The existent techniques like the Llyods algorithm (sometimes called k-means) were affected by the issue of the algorithm which converges to a local optimum along with no approximation guarantee. For overcoming these shortcomings, an efficient k-means clustering approach is offered by this paper for stream data mining. Coreset is a popular and fundamental concept for k-means clustering in stream data. In each step, reduction determines a coreset of inputs, and represents the error, where P represents number of input points according to nested property of coreset. Hence, a bit reduction in error of final coreset gets n times more accurate. Therefore, this motivated the author to propose a new coreset-reduction algorithm. The proposed algorithm executed on the Covertype dataset, Spambase dataset, Census 1990 dataset, Bigcross dataset, and Tower dataset. Our algorithm outperforms with competitive algorithms like Streamkm++, BICO (BIRCH meets Coresets for k-means clustering), and BIRCH (Balance Iterative Reducing and Clustering using Hierarchies.

An Efficient feature selection algorithm for the spam email classification

The existing spam email classification systems are suffering from the problems of low accuracy due to the high dimensionality of the associated feature selection (FS) process. But being a global optimization process in machine learning, FS is mainly aimed at reducing the redundancy of dataset to create a set of acceptable and accurate results. This study presents the combination of Chaotic Particle Swarm Optimization (PSO) algorithm with Artificial Bees Colony (ABC) for the reduction of features dimensionality in a bid to improve spam emails classification accuracy. The features for each particle in this work were represented in a binary form, meaning that they were transformed into binary using a sigmoid function. The features selection was based on a fitness function that depended on the obtained accuracy using SVM. The proposed system was evaluated for performance by considering the performance of the classifier and the selected features vectors dimension which served as the input to the classifier; this evaluation was done using the Spam Base dataset and from the results, the PSO-ABC classifier performed well in terms of FS even with a small set of selected features.

Association Rule Mining on Spambase Dataset using Tanagra

There is huge amount of data being generated every minute on internet. This data is of no use until we cannot extract useful information from it. Data mining is the process of extracting useful information or knowledge from this huge amount of data that can be further used for various purposes. Discovering Association rules is one of the most important tasks among all other data mining tasks. Association rules contain the rules in the form of IF then THAN form. The leftmost part of the rule i.e. IF is called as the Antecedent which defines the condition and the rightmost part i.e. ELSE is called as the Consequent which defines the result. In this paper, we present the overview and comparison of Apriori, Apriori PT and Frequent Itemsets algorithm of association component in Tanagra Tool. We analyzed the performance based on the execution time and memory used for different number of instances, support and Rule Length in Spambase Dataset. The results show that when we increase the support value the Apriori PT takes the less execution time and Apriori takes less memory space. When numbers of instances are reduced Frequent Itemsets outperforms well both in case of memory and execution time. When rule length is increased the Apriori algorithm performs better than Apriori PT and Frequent Itemsets.

Spam detection through feature selection using artificial neural network and sine–cosine algorithm

Detection of spam and non-spam emails is considered a great challenge for email service providers and users alike. Spam emails waste the Internet traffic and also contain malicious links that mostly direct users to phishing webpages. Another challenge of spams is their role in spreading malware on the network, further emphasizing the need for their detection. Despite the application of data mining methods such as artificial neural networks (ANNs) in spam detection, these methods are prone to a significant error in their output mostly due to including all the spam features in their training stage. To reduce the spam detection error, a feature selection-based method was provided in this paper using the sine–cosine algorithm (SCA). In the proposed method, feature vectors are updated by the SCA to select the optimal features for training the ANN. Implementation of the proposed method of the Spambase dataset in MATLAB indicated a precision, accuracy and sensitivity of 98.64%, 97.92% and 98.36%, respectively. In other words, the proposed method outperformed the multilayer perceptron (MLP) neural network, Bayesian network, decision tree and random forest classifiers in terms of spam detection. According to the test results, the feature selection error in the MLP neural network decreased by approximately 2.18% using the SCA.

Optimized Mahalanobis-Taguchi System for High-Dimensional Small Sample Data Classification.

The Mahalanobis–Taguchi system (MTS) is a multivariate data diagnosis and prediction technology, which is widely used to optimize large sample data or unbalanced data, but it is rarely used for high-dimensional small sample data. In this paper, the optimized MTS for the classification of high-dimensional small sample data is discussed from two aspects, namely, the inverse matrix instability of the covariance matrix and the instability of feature selection. Firstly, based on regularization and smoothing techniques, this paper proposes a modified Mahalanobis metric to calculate the Mahalanobis distance, which is aimed at reducing the influence of the inverse matrix instability under small sample conditions. Secondly, the minimum redundancy-maximum relevance (mRMR) algorithm is introduced into the MTS for the instability problem of feature selection. By using the mRMR algorithm and signal-to-noise ratio (SNR), a two-stage feature selection method is proposed: the mRMR algorithm is first used to remove noise and redundant variables; the orthogonal table and SNR are then used to screen the combination of variables that make great contribution to classification. Then, the feasibility and simplicity of the optimized MTS are shown in five datasets from the UCI database. The Mahalanobis distance based on regularization and smoothing techniques (RS-MD) is more robust than the traditional Mahalanobis distance. The two-stage feature selection method improves the effectiveness of feature selection for MTS. Finally, the optimized MTS is applied to email classification of the Spambase dataset. The results show that the optimized MTS outperforms the classical MTS and the other 3 machine learning algorithms.

To Enhance Phishing Emails Classification using Machine Learning Algorithm

Phishing email becomes more dangers problem in online bank truncation processing problem as well as social networking sites like Facebook, twitter, Instagram. Normally phishing is carrying out by mocking of email or text embedded in email body, which will provoke users to enter their credential. Training on phishing approach is not so much effective because users are not permanently remember their training tricks, warning messages.it is totally depend on the user action which will be performed on certain time on warning messages given by software while operating any URL. In this paper, phishing email classification is enhanced using J48, Naïve Bayes and decision tree on Spam base dataset. J48 does best classification on spam base which is 97%for true positive and 0.025% false negative. Random forest work best on small dataset that is up to 5000 and number of feature are 34.but increase dataset size and reduce feature Naïve Bayes work faster.

Detection of Outlier s in High Dimensional Data with Lasso Regression

Detecting Outliers has become a significant research area in data mining in last few years. The focus of this research has been to identify patterns or objects in huge data sets of a database that are exceptional from normal pattern, specifically dissimilar, and unpredictable with reference to the most of the datasets. As billions of personal computers, and internet users rose phenomenally, huge data sets of real life applications have been created for new challenges as well as explorations in research for Outlier detection. Many traditional techniques to detect outliers have unable to yield good results in such environments. So, developing a method to detect Outliers has become a critical task. A method to identify anomalies in high dimensional data based on Lasso Regression has been study in this research. This framework has been implemented in the open source JMP software. The parameters such as RSquare 0.001162, RMSE 0.031806 and Mean Response 0.007889 are calculated using Spambase dataset. The results from the experiments have shown that the proposed method detects Outliers in high dimensional data with potentially higher accuracy.

Hybrid Particle Swarm Optimization and Pegasos Algorithm for Spam Email Detection

Email is one of the most popular communication tools for most internet usersnowadays. It has become fast and an effective method to share and exchange information allover the world. Despite the great advantages of emails, its usage is facing problem which is spamemails. Spam emails are the huge presence of bulk and unsolicited emails which are expensive forthe companies, consume a huge amount of mail servers, network bandwidth and waste of time.Isolating and detecting these emails is known as spam detection. Many spam detection methodshave been proposed but there is still need to detect the email spam effectively with high accuracy.In this paper, hybrid particle swarm optimization and Pegasos algorithm, which is called (PSOPegasos) is proposed for spam email detection. Particle swarm optimization is employed as asearch strategy to determine the optimal parameters for Pegasos algorithm in order to achievehigher performance. The proposed algorithm has been applied on spambase dataset downloadedfrom UCI Machine Learning Repository. Experimental results demonstrate that the proposedalgorithm outperforms the performance of all the earlier proposed algorithms, considering theaccuracy, recall, precision and F-measure on the same dataset.

A Recursive General Regression Neural Network (R-GRNN) Oracle for classification problems

This research introduces the Recursive General Regression Neural Network Oracle (R-GRNN Oracle) and is demonstrated on several binary classification datasets. The traditional GRNN Oracle classifier (Masters et al., 1998) combines the predictive powers of several machine learning classifiers by weighing the amount of error each classifier has on the final predictions. Each classifier is assigned a weight based on the percentage of errors it contributes to the final predictions as the classifiers evaluate the dataset. The proposed R-GRNN Oracle is an enhancement to the GRNN Oracle in which the proposed algorithm consists of an oracle within an oracle – where the inner oracle acts as a classifier with its own predictions and error contribution. By combining the inner oracle with other classifiers, the R-GRNN Oracle produces superior results. The classifiers considered in this study are: Support Vector Machine (SVM), Multilayer Perceptron (MLP), Probabilistic Neural Network (PNN), Gaussian Naïve Bayes (GNB), K-Nearest Neighbor (KNN), and Random Forest (RF). To demonstrate the effectiveness of the proposed approach, several datasets were used, with the primary one being the publicly available Spambase dataset. The predictions of SVM, MLP, KNN, and RF were used to create the first GRNN Oracle, which was then enhanced with the high performances of SVM and RF to create the second oracle, the R-GRNN Oracle. The combined recursive model was 93.24% accurate using 10-fold cross validation, higher than the 91.94% of the inner GRNN Oracle and the 91.29% achieved by RF, the highest performance by a stand-alone classifier. The R-GRNN Oracle was not only the most accurate, but it also had the highest AUC, sensitivity, specificity, precision, and F1-score (97.99%, 91.86%, 94.40%, 93.28%, and 92.57%, respectively). The research contribution of this paper is introducing the concept of recursion (a concept not fully explored in machine learning models and applications) and testing this structure's ability on further enhancing the performance of the traditional oracle. The recursive model has also been applied to several other datasets: The Human Resources, Bank Marketing, and Monoclonal Gammopathy of Undetermined Significance (MGUS) datasets. The results of these implementations are summarized in this paper.

Research of Email Classification based on Deep Neural Network

Abstract The effective distinction between normal email and spam, so as to maximize the possible of filtering spam has become a research hotspot currently. Naive bayes algorithm is a kind of frequently-used email classification and it is a statistical-based classification algorithm. It assumes that the attributes are independent of each other when given the target value. This hypothesis is apparently impossible in the email classification, so the accuracy of email classification based on naive bayes algorithm is low. In allusion to the problem of poor accuracy of email classification based on naive bayes algorithm, scholars have proposed some new email classification algorithms. The email classification algorithm based on deep neural network is one kind of them. The deep neural network is an artificial neural network with full connection between layer and layer. The algorithm extracted the email feature from the training email samples and constructed a DNN with multiple hidden layers, the DNN classifier was generated by training samples, and finally the testing emails were classified, and they were marked whether they were spam or not. In order to verify the effect of the email classification algorithm based on DNN, in this paper we constructed a DNN with 2 hidden layers. The number of nodes in each hidden layer was 30. When the training set was trained, we set up 2000 batches, and each batch has 3 trained data. We used the famous Spam Base dataset as the data set. The experiment result showed that DNN was higher than naive Bayes in the accuracy of email classification when the proportion of the training set was 10%, 20%, 30%, 40% and 50% respectively, and DNN showed a good classification effect. With the development of science and technology, spam manifests in many forms and the damage of it is more serious, this puts forward higher requirements for the accuracy of spam recognition. The focus of next research will be combining various algorithms to further improve the effect of email classification.

Detection of Spam Email by Combining Harmony Search Algorithm and Decision Tree

Spam emails is probable the main problem faced by most e-mail users. There are many features in spam email detection and some of these features have little effect on detection and cause skew detection and classification of spam email. Thus, Feature Selection (FS) is one of the key topics in spam email detection systems. With choosing the important and effective features in classification, its performance can be optimized. Selector features has the task of finding a subset of features to improve the accuracy of its predictions. In this paper, a hybrid of Harmony Search Algorithm (HSA) and decision tree is used for selecting the best features and classification. The obtained results on Spam-base dataset show that the rate of recognition accuracy in the proposed model is 95.25% which is high in comparison with models such as SVM, NB, J48 and MLP. Also, the accuracy of the proposed model on the datasets of Ling-spam and PU1 is high in comparison with models such as NB, SVM and LR.

Propose Hybrid ACO and NB to Enhance Spam Filtering System

Unwanted e-mails became one of the most risk experienced by e-mail users, which may be either harmless or e-mails thatrepresenta threatto the internet.Filtering systems are used to filter e-mail messages from spam. This paper introduces a proposed hybrid system to filter the spam; the proposalhybrid Ant Colony System (ACS) and Naive Bayesian (NB) classifier. Where, ACS will dependon the Information Gain (IG) as a heuristic measure to guide the ants search to select the optimal worst features then omitting these features. The remind features will be the subset which is used to train and test NB classifier to classify whether the mail message spam or not.The proposal is experimented on spambase dataset, and the results showthat; the accuracy, precision and recall with NB which use a subset of features extracted by proposing IG-based ACS is higher than the traditional NB with all set of features.

Online Cost Sensitive Domain Knowledge Adaption

online learning, classifier is updated as records get available sequentially with the help of time. The misclassifications depends on the class which has been wrongly classified. Above both concepts are largely studied in data mining .In Cost sensitive online learning classification , cost of the misclassification is considered during online learning of the classifier. In domain knowledge adaption, Knowledge (Classifier) from source domain is adapted to target domain considering cost of misclassification. So accuracy of the system is increased. In proposed work cost of the misclassification is considered to adapt the knowledge from source domain to target domain .The proposed system uses the concept of cost sensitive online learning based knowledge transfer. In existing knowledge transfer system i.e.in OTL cost of misclassification is not considered while transferring knowledge from source domain to target domain. But in proposed system cost of misclassification is considered while updating classifier on sequentially coming data. To evaluate the approach spam base dataset is used and the implemented system gives better results.

IMPROVED SPAMBASE DATASET PREDICTION USING SVM RBF KERNEL WITH ADAPTIVE BOOST

Spam is no more garbage but risk as it includes virus attachments and spyware agents which make the recipients’ system ruined, therefore, there is an emerging need for spam detection. Many spam detection techniques based on machine learning algorithms have been proposed. As the amount of spam has been increased tremendously using bulk mailing tools, spam detection techniques should deal with it. In this paper we have proposed Hybrid classifier Adaptive boost with support vector machine RBF kernel on Spambase dataset. We have also extracted the features first by Principal component analysis. General Terms: Email Spam classification.

A novel algorithm applied to filter spam e-mails for iPhone

An iPhone is like a portable computer in arithmetic logic capability, memory capacity, and multi-media capability. Many malware targeting iPhone has already emerged and then the situation is getting worse. Accessing e-mails on the internet is not a new capability for iPhone. Other smart phones on the market also provide this capability. The spam e-mails have become a serious problem for iPhone. A novel algorithm, artificial bee-based decision tree (ABBDT) approach, is applied to filter spam e-mails for iPhone in this paper. In the proposed ABBDT approach, decision tree is used to filter spam e-mails for iPhone. In addition, artificial bee algorithm is used to increase the testing accuracy of decision tree. There are total 504 collected e-mails in iPhone dataset and they are categorized into 12 attributes. Another spambase dataset, obtained from UCI repository of machine learning databases, is also used to evaluate the performance of the proposed ABBDT approach. From simulation results, the proposed ABBDT approach outperforms other existing algorithms.