Research on DGA Malicious Domain Detection Methods

 

As technology advances and the complexity of network environments grows, researchers are increasingly focused on improving the accuracy and efficiency of malicious domain detection to tackle the rising challenges in cybersecurity.

Research on DGA Malicious Domain Detection Methods

1. Blacklist-Based Detection of DGA Domains

In the early days of malicious domain detection, researchers commonly relied on blacklist-based methods to detect domains generated by Domain Generation Algorithms (DGA). Security teams would compile a list of known malicious domains, frequently updating it to ensure it remained current. This list would then be used to block malicious domains or alert users to potential threats. As network technology evolved, these blacklists expanded significantly. Prominent cybersecurity organizations, such as the 360 Network Security Lab, began offering and maintaining publicly accessible domain blacklists. In addition to traditional blacklist approaches, early methods also incorporated DNS flagging techniques to detect suspicious domains.

2. Machine Learning-Based Detection of DGA Domains

Machine learning has proven effective at identifying patterns in large datasets of domain names, significantly improving the detection and classification of malicious domains. As a result, the use of machine learning techniques to detect DGA-based domains has become a prominent focus in this field.

Machine learning approaches generally follow four key steps: data collection, data preprocessing, algorithm development, and evaluation. Early research primarily used static lexical features, which were well-suited for machine learning models. Initial DGA detection methods depended on manually selected domain features, such as domain length, character frequency, and the number of subdomains. As the field progressed, researchers introduced more sophisticated feature extraction techniques, such as entropy-based feature selection and frequency-based feature representation, to improve model accuracy and generalization.

As traditional machine learning techniques evolved, researchers started employing widely recognized algorithms like Naïve Bayes (NB), Random Forest (RF), and Support Vector Machines (SVM) to classify and detect malicious domains.

3. Deep Learning-Based Detection of DGA Domains

In addition to machine learning, deep learning has been increasingly applied to the detection of malicious domains. Traditional detection methods often struggle when faced with complex, high-dimensional domain data, but the emergence of deep learning techniques has provided a more robust solution. Deep learning models have strong feature-learning capabilities, allowing them to automatically extract higher-level, abstract features from large-scale data. This has led to improvements in both the accuracy and reliability of malicious domain detection.

Popular deep learning models used in DGA detection include Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM), and Generative Adversarial Networks (GAN).