Robust Botnet Detection Techniques for Mobile and Network Environments

Cybercrime costs large amounts of money and resources every year. This is because it is usually carried out using different methods and at different scales. The use of botnets is one of the most common successful cybercrime methods. A botnet is a group of devices that are used together to carry out malicious attacks (they are connected via a network). With the widespread usage of handheld devices such as smartphones and tablets, networked devices are no longer limited to personal computers and laptops. Therefore, the size of networks (and therefore botnets) can be large. This means it is not surprising for malicious users to target different types of devices and platforms as cyber-attack victims or use them to launch cyber-attacks. Thus, robust automatic methods of botnet detection on different platforms are required. This thesis addresses this problem by introducing robust methods for botnet family detection on Android devices as well as by generally analysing network traffic. As for botnet detection on Android, this thesis proposes an approach to identify botnet Android botnet apps by means of source code mining. The approach analyses the source code via reverse engineering and data mining techniques for several examples of malicious and non-malicious apps. Two methods are used to build datasets. In the first, text mining is performed on the source code and several datasets are constructed, and in the second, one dataset is created by extracting source code metrics using an open-source tool. Additionally, this thesis introduces a novel transfer learning approach for the detection of botnet families by means of network traffic analysis. This approach is a key contribution to knowledge because it adds insight into how similar instances can exist in datasets that belong to different botnet families and that these instances can be leveraged to enhance model quality (especially for botnet families with small datasets). This novel approach is denoted Similarity Based Instance Transfer, or SBIT. Furthermore, the thesis presents a proposed extended version designed to overcome a weakness in the original algorithm. The extended version is called CB-SBIT (Class Balanced Similarity Based Instance Transfer).

Name dc:type.qualificationname

PhD

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

De Montfort University

Year dc:date.issued

2019

Author dc:creator

• Alothman, Basil

Licence

• https://dora.dmu.ac.uk/bitstreams/8a69eeac-3d08-40b8-8c1d-01137365fdf5/download