A novel robust convolutional neural network for uniform resource locator classification from the view of cyber security | Semantic Scholar (2024)

The proposed CGAN‐IWSO‐ResNet50 method is more accurate in detecting phishing attacks than the VGG19, AlexNet, RCNN, DNN + LSTM, and DNN + BiLSTM learning methods.

This research investigates the potential of leveraging artificial neural networks (ANNs) to identify and classify network intrusions, and highlights the role of ANNs in enhancing the precision of network intrusion detection systems, contributing to the broader field of cybersecurity.

The proposed method is more accurate in detecting attacks than methods such as GTO, PSO, HHO, WOA, and JSO and uses the most important features of the network traffic to use the artificial neural network classifier.

A deep learning-based phishing detection solution that leveraged the universal resource locator and website content such as images, text and frames and the combination of both CNN and LSTM was used to resolve the problem of a large data set and higher classifier prediction performance.

This work proposes the combination of a convolution operation to model the character-level URL features and a deep convolutional autoencoder (CAE) to consider the nature of zero-day attacks and demonstrates the superiority of the proposed method by receiver-operating characteristic (ROC) curve analysis.

Boosting algorithms such as LGBM, XGBoost and Gradient Boosting are used for predicting phishing URL and Random Forest Classifier is exploited to study URL detection as malicious or benign.

Different machine learning algorithms, namely, support vector machine, naive Bayes, linear regression, artificial neural network, decision tree, random forest, the fuzzy classifier, K‐nearest neighbor, adaptive boosting, gradient boosting, and tree ensemble have been implemented for botnet attack detection.

A novel approach based on deep reinforcement learning to model and detect malicious URLs and is capable of adapting to the dynamic behavior of the phishing websites and thus learn the features associated with phishing website detection.

A deep learning-based framework for detecting phishing websites that is implemented as a browser plug-in capable of determining whether there is a phishing risk in real-time when the user visits a web page and gives a warning message and results demonstrate the feasibility of the proposed solution.

A lightweight approach to detection and categorization of the malicious URLs according to their attack type is explored and it is shown that lexical analysis is effective and efficient for proactive detection of these URLs.

This work reconstructs the high-dimensional features of Android applications (apps) and employ multiple CNN to detect Android malware and proposes a hybrid model based on deep autoencoder (DAE) and convolutional neural network (CNN), which shows powerful ability in feature extraction and malware detection.

Two cyber data sets are investigated, both being five-class data sets, for which the Random Forest Classifier, XGBoost Classification, and the Keras Sequential algorithms are applied, and results confirm that applying the classifiers to generate the models are good choices to detect cybersecurity threats.

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