Random forest

World Wide Web

Biology

Support vector machine

Computer security

Network security

The Internet

Anomaly detection

Data mining

Computer science

Botnet

Immunology

Signature (topology)

Immune system

Mathematics

Evasion (ethics)

Artificial intelligence

Geometry

Intrusion detection system

Machine learning

Future Internet

In recent years, botnets have become one of the major threats to information security because they have been constantly evolving in both size and sophistication. A number of botnet detection measures, such as honeynet-based and Intrusion Detection System (IDS)-based, have been proposed. However, IDS-based solutions that use signatures seem to be ineffective because recent botnets are equipped with sophisticated code update and evasion techniques. A number of studies have shown that abnormal botnet detection methods are more effective than signature-based methods because anomaly-based botnet detection methods do not require pre-built botnet signatures and hence they have the capability to detect new or unknown botnets. In this direction, this paper proposes a botnet detection model based on machine learning using Domain Name Service query data and evaluates its effectiveness using popular machine learning techniques. Experimental results show that machine learning algorithms can be used effectively in botnet detection and the random forest algorithm produces the best overall detection accuracy of over 90%.

Botnet Detection Based On Machine Learning Techniques Using DNS Query Data