URL String Research Articles

Automatic phishing website detection and prevention model using transformer deep belief network

In the digitally connected world cybersecurity is paramount, phishing where attackers pose as trusted entities to steal sensitive data, looms large. The proliferation of phishing attacks on the internet poses a substantial threat to individuals and organizations, compromising sensitive information and causing financial and reputational damage. This study's goal is to establish an automated system for the early detection and prevention of phishing websites, thereby enhancing online security and protecting users from cyber threats. This research initially employs One Hot Encoding (OHE) mechanism-based pre-processing mechanism that converts every URL string into a numerical vector with a particular dimension. This study utilizes two feature selection techniques which are transfer learning-based feature extraction using DarkNet19 and Variational Autoencoder (VAE) to select the value of the most important feature. The robust security mechanisms are presented to prevent phishing attacks and safeguard personal information on websites. List-based deep learning-based systems to prevent and detect phishing URLs more efficiently. The study proposes a transformer-based Deep Belief Network (TB-DBN), a veritable pre-trained deep transformer network model for phishing behaviour detection. A cross-validation technique with grid search hyper-parameter tuning based on the Intelligence Binary Bat Algorithm (IBBA) was designed using the proposed hybrid model. Predictions were made to classify the phishing URLs using a probabilistic estimation guided boosting classifier model and evaluate their performance in terms of accuracy, precision, recall, specificity, and F1- score. The risk level associated with the URL will be assessed based on various factors, such as the source's reputation, content analysis results, and behavioural anomalies. The computational complexity of DL model training is influenced by various factors, such as the model's complexity, the training data's size, and the optimization algorithm exploited, for training. The outcome demonstrates that tweaking variables increases the effectiveness of Python-based deep learning systems. The findings of the proposed method excel, achieving an accuracy of 99.4 %, precision of 99.2 %, recall of 99.3 %, and an F1-score of 99.2 %. This innovative automatic phishing website detection and prevention model, based on a Transformer-based Deep Belief Network, offers advanced accuracy and adaptability, strengthening cybersecurity measures to safeguard sensitive user information and mitigate the substantial threat of phishing attacks in the digitally connected world.

Graph-based phishing detection: URLGBM model driven by machine learning

Phishing attacks are a form of social engineering that involves the transmission of deceptive communications imitating a trustworthy source to deceive users into revealing confidential information. Antiphishing systems have made significant strides in recent years, allowing internet users to secure confidential and private information against such attacks. In this study, we propose a URL graph representation based on a random walk algorithm, specifically PageRank, for weighting URL tokens. To create the graph, an imaginary walker visits the URL tokens one at a time and assigns a value to each token based on the probability of encountering the target URL during the walk. We studied different random walk (rw) variations and their effects on the URL string. The BM25 algorithm was employed to produce a sparse matrix for the classification task from the token scores obtained. Experiments conducted with logistic regression revealed that the proposed model achieved an accuracy of 98.98%, a false alarm rate of 1.72%, and a missing alarm rate of 0.302%. The model also attained a 97.17% accuracy on a benchmark dataset.

An Investigation of Privacy and Security in VR APPs through URL String Analysis

An Investigation of Privacy and Security in VR APPs through URL String Analysis

An Adversarial Attack Analysis on Malicious Advertisement URL Detection Framework

Malicious advertisement URLs pose a security risk since they are the source of cyber-attacks, and the need to address this issue is growing in both industry and academia. Several attempts have been made in recent years for malicious URL detection using machine learning (ML). The most widely used techniques extract linguistic features of URL string to extract features like bag-of-words (BoW) before applying ML model. Existing malicious URL detection techniques require effective manual feature engineering that can handle unseen features and generalise to test data. In this study, we extract a novel set of lexical and web-scrapped features and employ ML techniques for fraudulent advertisement URL detection. The combination set of six different kinds of features precisely overcomes the obfuscation in fraudulent URL classification. Based on distinct statistical properties, we use twelve differently formatted datasets for detection, prediction and classification task. We extend our prediction analysis for mismatched and unlabelled datasets. For this framework, we analyze the performance of four ML techniques: Random Forest, Gradient Boost, XGBoost and AdaBoost in the detection part. With our proposed method, we achieve a false negative rate up to 0.0037 while maintaining high detection accuracy of 99.63%. Moreover, we employ an unsupervised learning technique for data clustering using the K-Means algorithm for the visual analysis. This paper analyses the vulnerability of decision tree-based models using the limited knowledge attack scenario. We considered the exploratory attack during the test phase and implemented Zeroth Order Optimization adversarial attack on the detection models.