Application of ensemble Machine Learning models for phishing detection on web networks

Puri, Navyah; Saggar, Pranay; Kaur, Amandeep; Garg, Puneet

doi:10.1109/ccict56684.2022.00062

Cited by 14 publications

(5 citation statements)

References 19 publications

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“…Working Description KNC K-Neighbors Classifier (KNC) method [25], [26] operates by classifying data points based on the majority class among their k-nearest neighbors in a feature space. In the context of SQL injection attack detection, it analyzes the characteristics of queries and compares them to a labeled dataset.…”

Section: Methodsmentioning

confidence: 99%

AE-Net: Novel Autoencoder-Based Deep Features for SQL Injection Attack Detection

Thalji,

Raza,

Islam

et al. 2023

IEEE Access

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Structured Query Language (SQL) injection attacks represent a critical threat to databasedriven applications and systems, exploiting vulnerabilities in input fields to inject malicious SQL code into database queries. This unauthorized access enables attackers to manipulate, retrieve, or even delete sensitive data. The unauthorized access through SQL injection attacks underscores the critical importance of robust Artificial Intelligence (AI) based security measures to safeguard against SQL injection attacks. This study's primary objective is the automated and timely detection of SQL injection attacks through AI without human intervention. Utilizing a preprocessed database of 46,392 SQL queries, we introduce a novel optimized approach, the Autoencoder network (AE-Net), for automatic feature engineering. The proposed AE-Net extracts new high-level deep features from SQL textual data, subsequently input into machine learning models for performance evaluations. Extensive experimental evaluation reveals that the extreme gradient boosting classifier outperforms existing studies with an impressive k-fold accuracy score of 0.99 for SQL injection detection. Each applied learning approach's performance is further enhanced through hyperparameter tuning and validated via k-fold cross-validation. Additionally, statistical t-test analysis is applied to assess performance variations. Our innovative research has the potential to revolutionize the timely detection of SQL injection attacks, benefiting security specialists and organizations.

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Section: Methodsmentioning

confidence: 99%

AE-Net: Novel Autoencoder-Based Deep Features for SQL Injection Attack Detection

Thalji,

Raza,

Islam

et al. 2023

IEEE Access

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“…Creating efficient detection systems is aided by the ideas and models already in existence in machine learning and phishing detection [52]. However, a well-liked machine learning model for phishing detection uses decision trees [53].…”

Section: Theoretical Reviewmentioning

confidence: 99%

“…). This ensured a fair chance of accurately predicting each class by preventing the model from being biased towards the dominant class [52]. During this phase, the X_train and y_train datasets were subjected to oversampling, wherein the minority class's data points were randomly selected and duplicated until a balance was achieved, thereby fostering a more equitable learning environment during model training.…”

Section: Automatic Feature Selectionmentioning

confidence: 99%

Phishing Detection Using Machine Learning: A Model Development and Integration

Adeyemi Onih

2024

IJSMR

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This study aimed to develop a robust machine learning-based phishing detection system using algorithms such as K-nearest neighbour (KNN), artificial neural network (ANN), and random forest (RF). It utilised datasets from Ariyadasa et al. (2021) and UNB (2016) to discern patterns distinguishing legitimate from phishing websites. Furthermore, an objective was to integrate the optimal model into a Django-based web application, facilitating real-time phishing detection. A comprehensive literature review on phishing detection techniques was also undertaken. Datasets chosen underwent rigorous pre-processing to address missing values and imbalance. Feature selection was achieved manually and automatically using mutual information classification. Three machine learning algorithms, RF, KNN, and ANN, were explored. Their hyper-parameters were optimised using GridSearchCV. Performance results highlighted RF's accuracy at 99.78%, KNN's at 99.67%, and ANN's at 99.11%. While RF and KNN models perfectly identified legitimate websites, ANN showcased an impeccable detection of phishing websites. The RF model, with the highest accuracy, was integrated into a Django application, providing a user interface for real-time phishing detection. All models exhibited high accuracy rates, demonstrating their efficacy in phishing detection. While RF was integrated into the web application for this study, the choice between models depends on specific user or business requirements and priorities. Feedback mechanisms within the Django application further promise refinement in future recommendations. The study provides a foundational step toward enhancing web safety through effective phishing detection.

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“…The model's accuracy could be much higher due to the proposed method utilizing only 1%, 5%, and 10% of the dataset, which might cause a substantial loss of information. The voting classifier method was also implemented by Puri et al [28]. The research utilized the SMOTE algorithm to normalize the dataset, which resulted in more extensive data.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Weight-Based Voting Classifier for Intrusion Detection System

2024

IJIES

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The evolution of technology and the internet has accelerated the pace of communication and information exchange. Despite technological advancements, the significant weakness lies in the persistent threat of cybercrime, manifesting in various forms like malware, phishing, and ransomware. To solve the cybercrime problems, this research aims to create an intrusion detection system model using a novel framework. In general, the proposed method consists of 3 stages: Data preprocessing, feature selection using ANOVA F-value combined with cross validation, and classification using weight-based voting classifier. Some machine learning methods used in the weight-based voting classifier are random forest, K-nearest neighbour, and logistic regression. The experiment results show that weight order and weight combination affect the detection performance. The proposed method produces an excellent precision value of 98.66%, higher than the single voting classifier.

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Application of ensemble Machine Learning models for phishing detection on web networks

Cited by 14 publications

References 19 publications

AE-Net: Novel Autoencoder-Based Deep Features for SQL Injection Attack Detection

AE-Net: Novel Autoencoder-Based Deep Features for SQL Injection Attack Detection

Phishing Detection Using Machine Learning: A Model Development and Integration

Analysis of Weight-Based Voting Classifier for Intrusion Detection System

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