A hybrid method of genetic algorithm and support vector machine for DNS tunneling detection

Abstract

With the expansion of the business over the internet, corporations nowadays are investing numerous amounts of money in the web applications. However, there are different threats could make the corporations vulnerable for potential attacks. One of these threats is harnessing the domain name protocol for passing harmful information, this kind of threats is known as DNS tunneling. As a result, confidential information would be exposed and violated. Several studies have investigated the machine learning in order to propose a detection approach. In their approaches, authors have used different and numerous types of features such as domain length, number of bytes, content, volume of DNS traffic, number of hostnames per domain, geographic location and domain history. Apparently, there is a vital demand to accommodate feature selection task in order to identify the best features. This paper proposes a hybrid method of genetic algorithm feature selection approach with the support vector machine classifier for the sake of identifying the best features that have the ability to optimize the detection of DNS tunneling. To evaluate the proposed method, a benchmark dataset of DNS tunneling has been used. Results showed that the proposed method has outperformed the conventional SVM by achieving 0.946 of f-measure

An extreme gradient boost based classification and regression tree for network intrusion detection in IoT

“…However, current techniques for detecting attacks in network systems, particularly those that depend on periodic models and extensive training data, resulting in challenges in achieving effective intrusion detection. Then, need to develop a system that can accurately classify real-time data and assess its effectiveness in achieving consistently high accuracy rates [17], [18]. The study of network intrusion detection is essential to support cybersecurity defenses.…”