- Title
- Reanimating historic malware samples
- Creator
- Black, Paul; Gondal, Iqbal; Vamplew, Peter; Lakhotia, Arun
- Date
- 2021
- Type
- Text; Book chapter
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/187116
- Identifier
- vital:16993
- Identifier
- URL https://doi.org/10.1007/978-3-030-62582-5_13
- Identifier
- ISBN:9783030625825
- Abstract
- Many types of malicious software are controlled from an attacker’s command and control (C2) servers. Anti-virus organizations seek to defeat malware attacks by requesting removal of C2 server Domain Name Server (DNS) records. As a result, the life span of most malware samples is relatively short. Large datasets of historical malware samples are available for countermeasures research. However, due to the age of these malware samples, their C2 servers are no longer available. To cope with high volumes of malware production, malware analysis is increasingly performed using machine learning techniques. Dynamic analysis is commonly used for feature extraction. However, due to the absence of their C2 servers, after initialization, malware samples may exit or loop attempting to establish C2 server connections and, as a result, no longer exhibit their original capabilities. Therefore, partial execution of historical malware samples in a sandbox results in features that differ from those that would be extracted in-the-wild, thus invalidating the results of any machine learning research based on these features. One approach to extracting accurate features is to build an emulated C2 server to provide an environment that allows control of the full capabilities of the malware in an isolated environment. To illustrate the benefits of building C2 server emulators, this chapter provides examples of techniques for the creation of C2 server emulators for three malware families (Zeus, CryptoWall, and CryptoLocker) using manual reverse engineering techniques and a review of semi-automated techniques for the construction of C2 server emulators.
- Publisher
- Springer
- Relation
- Malware Analysis Using Artificial Intelligence and Deep Learning p. 345-360
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ The Editor(s) if applicable and The Author(s) under exclusive licence to Springer Nature Switzerland AG 2021
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