Phishing is the easiest way to use cybercrime with the aim of enticing people to give accurate information such as account IDs, bank details, and passwords. This type of cyberattack is usually triggered by emails, instant messages, or phone calls. The existing anti-phishing techniques are mainly based on source code features, which require to scrape the content of web pages, and on third-party services which retard the classification process of phishing URLs. Although the machine learning techniques have lately been used to detect phishing, they require essential manual feature engineering and are not an expert at detecting emerging phishing offenses. Due to the recent rapid development of deep learning techniques, many deep learning-based methods have also been introduced to enhance the classification performance. In this paper, a fast deep learning-based solution model, which uses character-level convolutional neural network (CNN) for phishing detection based on the URL of the website, is proposed. The proposed model does not require the retrieval of target website content or the use of any third-party services. It captures information and sequential patterns of URL strings without requiring a prior knowledge about phishing, and then uses the sequential pattern features for fast classification of the actual URL. For evaluations, comparisons are provided between different traditional machine learning models and deep learning models using various feature sets such as hand-crafted, character embedding, character level TF-IDF, and character level count vectors features. According to the experiments, the proposed model achieved an accuracy of 95.02% on our dataset and an accuracy of 98.58%, 95.46%, and 95.22% on benchmark datasets which outperform the existing phishing URL models.

Blockchain technology has allowed many abnormal schemes to hide behind smart contracts. This causes serious financial losses, which adversely affects the blockchain. Machine learning technology has mainly been utilized to enable automatic detection of abnormal contract accounts in recent years. In spite of this, previous machine learning methods have suffered from a number of disadvantages: first, it is extremely difficult to identify features that enable accurate detection of abnormal contracts, and based on these features, statistical analysis is also ineffective. Second, they ignore the imbalances and repeatability of smart contract accounts, which often results in overfitting of the model. In this paper, we propose a data-driven robust method for detecting abnormal contract accounts over the Ethereum Blockchain. This method comprises hybrid features set by integrating opcode n-grams, transaction features, and term frequency-inverse document frequency source code features to train an ensemble classifier. The extra-trees and gradient boosting algorithms based on weighted soft voting are used to create an ensemble classifier that balances the weaknesses of individual classifiers in a given dataset. The abnormal and normal contract data are collected by analyzing the open source etherscan.io, and the problem of the imbalanced dataset is solved by performing the adaptive synthetic sampling. The empirical results demonstrate that the proposed individual feature sets are useful for detecting abnormal contract accounts. Meanwhile, combining all the features enhances the detection of abnormal contracts with significant accuracy. The experimental and comparative results show that the proposed method can distinguish abnormal contract accounts for the data-driven security of blockchain Ethereum with satisfactory performance metrics.

Phishing URL detection has become a critical challenge in cybersecurity, with existing methods often struggling to maintain high accuracy while generalizing across diverse datasets. In this paper, we introduce BERT-PhishFinder, a novel and efficient transformer-based model designed to tackle this problem. While most traditional approaches rely heavily on lexical features or complex convolutional architectures, BERT-PhishFinder leverages the power of DistilBERT, a lightweight yet highly effective transformer, to capture rich contextual representations of URL sequences. To enhance the model’s robustness and reduce overfitting, we strategically incorporate SpatialDropout1D in the embedding layers, along with global average pooling and global max pooling techniques to extract both comprehensive and key discriminative features. The pooled representations are thoughtfully concatenated to form a comprehensive feature representation. Through this carefully crafted design, our model adopts ensemble learning, as it undergoes multiple parallel dense layers, each with distinct parameters and dropout regularization. This facilitates learning diverse patterns and features from the input URL sequence, culminating in exceptional phishing URL detection performance. Extensive evaluations against conventional deep learning algorithms, transformer models (XLNet, RoBERTa, ALBERT), and other existing methods on five benchmark datasets show that BERT-PhishFinder not only achieves the state of-the-art real phishing URL detection but also accomplishes this with reduced label dependency.