  The Domain Name System (DNS) protocol plays a major role in today's Internet
as it translates between website names and corresponding IP addresses. However,
due to the lack of processes for data integrity and origin authentication, the
DNS protocol has several security vulnerabilities. This often leads to a
variety of cyber-attacks, including botnet network attacks. One promising
solution to detect DNS-based botnet attacks is adopting machine learning (ML)
based solutions. To that end, this paper proposes a novel optimized ML-based
framework to detect botnets based on their corresponding DNS queries. More
specifically, the framework consists of using information gain as a feature
selection method and genetic algorithm (GA) as a hyper-parameter optimization
model to tune the parameters of a random forest (RF) classifier. The proposed
framework is evaluated using a state-of-the-art TI-2016 DNS dataset.
Experimental results show that the proposed optimized framework reduced the
feature set size by up to 60%. Moreover, it achieved a high detection accuracy,
precision, recall, and F-score compared to the default classifier. This
highlights the effectiveness and robustness of the proposed framework in
detecting botnet attacks.
