A comparative study of CERBER, MAKTUB and LOCKY Ransomware using a Hybridised-Malware analysis
- Authors: Schmitt, Veronica
- Date: 2019
- Subjects: Microsoft Windows (Computer file) , Data protection , Computer crimes -- Prevention , Computer security , Computer networks -- Security measures , Computers -- Access control , Malware (Computer software)
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/92313 , vital:30702
- Description: There has been a significant increase in the prevalence of Ransomware attacks in the preceding four years to date. This indicates that the battle has not yet been won defending against this class of malware. This research proposes that by identifying the similarities within the operational framework of Ransomware strains, a better overall understanding of their operation and function can be achieved. This, in turn, will aid in a quicker response to future attacks. With the average Ransomware attack taking two hours to be identified, it shows that there is not yet a clear understanding as to why these attacks are so successful. Research into Ransomware is limited by what is currently known on the topic. Due to the limitations of the research the decision was taken to only examined three samples of Ransomware from different families. This was decided due to the complexities and comprehensive nature of the research. The in depth nature of the research and the time constraints associated with it did not allow for proof of concept of this framework to be tested on more than three families, but the exploratory work was promising and should be further explored in future research. The aim of the research is to follow the Hybrid-Malware analysis framework which consists of both static and the dynamic analysis phases, in addition to the digital forensic examination of the infected system. This allows for signature-based findings, along with behavioural and forensic findings all in one. This information allows for a better understanding of how this malware is designed and how it infects and remains persistent on a system. The operating system which has been chosen is the Microsoft Window 7 operating system which is still utilised by a significant proportion of Windows users especially in the corporate environment. The experiment process was designed to enable the researcher the ability to collect information regarding the Ransomware and every aspect of its behaviour and communication on a target system. The results can be compared across the three strains to identify the commonalities. The initial hypothesis was that Ransomware variants are all much like an instant cake box consists of specific building blocks which remain the same with the flavouring of the cake mix being the unique feature.
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Categorising Network Telescope data using big data enrichment techniques
- Authors: Davis, Michael Reginald
- Date: 2019
- Subjects: Denial of service attacks , Big data , Computer networks -- Security measures
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/92941 , vital:30766
- Description: Network Telescopes, Internet backbone sampling, IDS and other forms of network-sourced Threat Intelligence provide researchers with insight into the methods and intent of remote entities by capturing network traffic and analysing the resulting data. This analysis and determination of intent is made difficult by the large amounts of potentially malicious traffic, coupled with limited amount of knowledge that can be attributed to the source of the incoming data, as the source is known only by its IP address. Due to the lack of commonly available tooling, many researchers start this analysis from the beginning and so repeat and re-iterate previous research as the bulk of their work. As a result new insight into methods and approaches of analysis is gained at a high cost. Our research approaches this problem by using additional knowledge about the source IP address such as open ports, reverse and forward DNS, BGP routing tables and more, to enhance the researcher's ability to understand the traffic source. The research is a BigData experiment, where large (hundreds of GB) datasets are merged with a two month section of Network Telescope data using a set of Python scripts. The result are written to a Google BigQuery database table. Analysis of the network data is greatly simplified, with questions about the nature of the source, such as its device class (home routing device or server), potential vulnerabilities (open telnet ports or databases) and location becoming relatively easy to answer. Using this approach, researchers can focus on the questions that need answering and efficiently address them. This research could be taken further by using additional data sources such as Geo-location, WHOIS lookups, Threat Intelligence feeds and many others. Other potential areas of research include real-time categorisation of incoming packets, in order to better inform alerting and reporting systems' configuration. In conclusion, categorising Network Telescope data in this way provides insight into the intent of the (apparent) originator and as such is a valuable tool for those seeking to understand the purpose and intent of arriving packets. In particular, the ability to remove packets categorised as non-malicious (e.g. those in the Research category) from the data eliminates a known source of `noise' from the data. This allows the researcher to focus their efforts in a more productive manner.
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