Dataset Information

Hybrid rule-based botnet detection approach using machine learning for analysing DNS traffic.

ABSTRACT: Botnets can simultaneously control millions of Internet-connected devices to launch damaging cyber-attacks that pose significant threats to the Internet. In a botnet, bot-masters communicate with the command and control server using various communication protocols. One of the widely used communication protocols is the 'Domain Name System' (DNS) service, an essential Internet service. Bot-masters utilise Domain Generation Algorithms (DGA) and fast-flux techniques to avoid static blacklists and reverse engineering while remaining flexible. However, botnet's DNS communication generates anomalous DNS traffic throughout the botnet life cycle, and such anomaly is considered an indicator of DNS-based botnets presence in the network. Despite several approaches proposed to detect botnets based on DNS traffic analysis; however, the problem still exists and is challenging due to several reasons, such as not considering significant features and rules that contribute to the detection of DNS-based botnet. Therefore, this paper examines the abnormality of DNS traffic during the botnet lifecycle to extract significant enriched features. These features are further analysed using two machine learning algorithms. The union of the output of two algorithms proposes a novel hybrid rule detection model approach. Two benchmark datasets are used to evaluate the performance of the proposed approach in terms of detection accuracy and false-positive rate. The experimental results show that the proposed approach has a 99.96% accuracy and a 1.6% false-positive rate, outperforming other state-of-the-art DNS-based botnet detection approaches.

SUBMITTER: Al-Mashhadi S

PROVIDER: S-EPMC8372004 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Botnets are some of the most recurrent cyber-threats, which take advantage of the wide heterogeneity of endpoint devices at the Edge of the emerging communication environments for enabling the malicious enforcement of fraud and other adversarial tactics, including malware, data leaks or denial of service. There have been significant research advances in the development of accurate botnet detection methods underpinned on supervised analysis but assessing the accuracy and performance of such detection methods requires a clear evaluation model in the pursuit of enforcing proper defensive strategies. In order to contribute to the mitigation of botnets, this paper introduces a novel evaluation scheme grounded on supervised machine learning algorithms that enable the detection and discrimination of different botnets families on real operational environments. The proposal relies on observing, understanding and inferring the behavior of each botnet family based on network indicators measured at flow-level. The assumed evaluation methodology contemplates six phases that allow building a detection model against botnet-related malware distributed through the network, for which five supervised classifiers were instantiated were instantiated for further comparisons-Decision Tree, Random Forest, Naive Bayes Gaussian, Support Vector Machine and K-Neighbors. The experimental validation was performed on two public datasets of real botnet traffic-CIC-AWS-2018 and ISOT HTTP Botnet. Bearing the heterogeneity of the datasets, optimizing the analysis with the Grid Search algorithm led to improve the classification results of the instantiated algorithms. An exhaustive evaluation was carried out demonstrating the adequateness of our proposal which prompted that Random Forest and Decision Tree models are the most suitable for detecting different botnet specimens among the chosen algorithms. They exhibited higher precision rates whilst analyzing a large number of samples with less processing time. The variety of testing scenarios were deeply assessed and reported to set baseline results for future benchmark analysis targeted on flow-based behavioral patterns.

Dataset Information

Hybrid rule-based botnet detection approach using machine learning for analysing DNS traffic.

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