Improving the phishing website detection using empirical analysis of Function Tree and its variants

Balogun, Abdullateef Oluwagbemiga; Adewole, Kayode S.; Raheem, Muiz O.; Akande, Oluwatobi Noah; Usman-Hamza, Fatima E.; Mabayoje, Modinat A.; Akintola, Abimbola G.; Asaju-Gbolagade, Ayisat Wuraola; Jimoh, Muhammed K.; Jimoh, Rasheed Gbenga; Adeyemo, Victor Elijah

doi:10.1016/j.heliyon.2021.e07437

Cited by 26 publications

(9 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The CV technique can guard against data heterogeneity problems that can arise in defective datasets. Furthermore, the CV technique is known to generate models with low bias and variance [ 71 – 75 ]. Experimentation was carried out using the Weka machine learning library [ 76 ], R lang.…”

Section: Methodsmentioning

confidence: 99%

A Novel Rank Aggregation‐Based Hybrid Multifilter Wrapper Feature Selection Method in Software Defect Prediction

Balogun

Basri

Mahamad

et al. 2021

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

The high dimensionality of software metric features has long been noted as a data quality problem that affects the performance of software defect prediction (SDP) models. This drawback makes it necessary to apply feature selection (FS) algorithm(s) in SDP processes. FS approaches can be categorized into three types, namely, filter FS (FFS), wrapper FS (WFS), and hybrid FS (HFS). HFS has been established as superior because it combines the strength of both FFS and WFS methods. However, selecting the most appropriate FFS (filter rank selection problem) for HFS is a challenge because the performance of FFS methods depends on the choice of datasets and classifiers. In addition, the local optima stagnation and high computational costs of WFS due to large search spaces are inherited by the HFS method. Therefore, as a solution, this study proposes a novel rank aggregation-based hybrid multifilter wrapper feature selection (RAHMFWFS) method for the selection of relevant and irredundant features from software defect datasets. The proposed RAHMFWFS is divided into two stepwise stages. The first stage involves a rank aggregation-based multifilter feature selection (RMFFS) method that addresses the filter rank selection problem by aggregating individual rank lists from multiple filter methods, using a novel rank aggregation method to generate a single, robust, and non-disjoint rank list. In the second stage, the aggregated ranked features are further preprocessed by an enhanced wrapper feature selection (EWFS) method based on a dynamic reranking strategy that is used to guide the feature subset selection process of the HFS method. This, in turn, reduces the number of evaluation cycles while amplifying or maintaining its prediction performance. The feasibility of the proposed RAHMFWFS was demonstrated on benchmarked software defect datasets with Naïve Bayes and Decision Tree classifiers, based on accuracy, the area under the curve (AUC), and F-measure values. The experimental results showed the effectiveness of RAHMFWFS in addressing filter rank selection and local optima stagnation problems in HFS, as well as the ability to select optimal features from SDP datasets while maintaining or enhancing the performance of SDP models. To conclude, the proposed RAHMFWFS achieved good performance by improving the prediction performances of SDP models across the selected datasets, compared to existing state-of-the-arts HFS methods.

show abstract

Section: Methodsmentioning

confidence: 99%

A Novel Rank Aggregation‐Based Hybrid Multifilter Wrapper Feature Selection Method in Software Defect Prediction

Balogun

Basri

Mahamad

et al. 2021

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

show abstract

“…The Scott-Knott ESD statistical rank test is also used to ascertain the significant differences in the prediction performances of the various models studied. The Waikato Environment for Knowledge Analysis (WEKA) machine learning library [57], R programming language [58], and Origin Plot are used for the experimentation on an Intel(R) Core™ machine equipped with i7-6700, running at speed 3.4 GHz CPU with 16 GB RAM.…”

Section: Methodsmentioning

confidence: 99%

Software Defect Prediction Using Wrapper Feature Selection Based on Dynamic Re-Ranking Strategy

et al. 2021

Self Cite

View full text Add to dashboard Cite

Finding defects early in a software system is a crucial task, as it creates adequate time for fixing such defects using available resources. Strategies such as symmetric testing have proven useful; however, its inability in differentiating incorrect implementations from correct ones is a drawback. Software defect prediction (SDP) is another feasible method that can be used for detecting defects early. Additionally, high dimensionality, a data quality problem, has a detrimental effect on the predictive capability of SDP models. Feature selection (FS) has been used as a feasible solution for solving the high dimensionality issue in SDP. According to current literature, the two basic forms of FS approaches are filter-based feature selection (FFS) and wrapper-based feature selection (WFS). Between the two, WFS approaches have been deemed to be superior. However, WFS methods have a high computational cost due to the unknown number of executions available for feature subset search, evaluation, and selection. This characteristic of WFS often leads to overfitting of classifier models due to its easy trapping in local maxima. The trapping of the WFS subset evaluator in local maxima can be overcome by using an effective search method in the evaluator process. Hence, this study proposes an enhanced WFS method that dynamically and iteratively selects features. The proposed enhanced WFS (EWFS) method is based on incrementally selecting features while considering previously selected features in its search space. The novelty of EWFS is based on the enhancement of the subset evaluation process of WFS methods by deploying a dynamic re-ranking strategy that iteratively selects germane features with a low subset evaluation cycle while not compromising the prediction performance of the ensuing model. For evaluation, EWFS was deployed with Decision Tree (DT) and Naïve Bayes classifiers on software defect datasets with varying granularities. The experimental findings revealed that EWFS outperformed existing metaheuristics and sequential search-based WFS approaches established in this work. Additionally, EWFS selected fewer features with less computational time as compared with existing metaheuristics and sequential search-based WFS methods.

show abstract

“…The links are also promoted in search engines with the use of common indexing mechanisms. If users open such links, they are directed to the specific website where scamme rs achieve their goal by gaining access to users' valuable data [10].…”

Section: Introductionmentioning

confidence: 99%

The development of a model for the threat detection system with the use of machine learning and neural network methods

Ussatova,

Zhumabekova,

Karyukin

et al. 2024

ijirss

View full text Add to dashboard Cite

This study examines the development of a model for the threat detection system with the use of machine learning and neural network methods. The fast development of Internet technologies has led to the appearance of many digital systems and platforms. However, despite the impressive technological progress, another side also emerged in the spread of a massive number of different cyber threats. Although various ways have been created to detect and prevent them, the threats are also developing and becoming more complex each year. Therefore, new system defense and data protection methods using machine and deep learning approaches have been proposed recently. The methods based on these approaches have proved to be especially effective in the wave of new Artificial Intelligence applications. In this paper, a threat detection system has been designed to disclose different kinds of threats while maintaining the security, confidentiality, and availability of the computer system. The development of machine learning models for detecting DDoS and man-in-the-middle attacks, Structured Query Language (SQL) injections, phishing, and malware was examined. The data scaling, feature selection, feature extraction, and classification steps were also thoroughly described. Naïve Bayes, Logistic Regression, Decision Tree, Random Forest, XGBoost, CatBoost, and Deep Neural Network algorithms were utilized for training the cyber threat detection models. The experimental results evaluated all the models using accuracy, precision, recall, and F1-score metrics. The best models achieved scores in the range of 0.90 to 1.00.

show abstract

Improving the phishing website detection using empirical analysis of Function Tree and its variants

Cited by 26 publications

References 54 publications

A Novel Rank Aggregation‐Based Hybrid Multifilter Wrapper Feature Selection Method in Software Defect Prediction

A Novel Rank Aggregation‐Based Hybrid Multifilter Wrapper Feature Selection Method in Software Defect Prediction

Software Defect Prediction Using Wrapper Feature Selection Based on Dynamic Re-Ranking Strategy

The development of a model for the threat detection system with the use of machine learning and neural network methods

Contact Info

Product

Resources

About