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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- Date Issued: 2019
A framework for scoring and tagging NetFlow data
- Authors: Sweeney, Michael John
- Date: 2019
- Subjects: NetFlow , Big data , High performance computing , Event processing (Computer science)
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/65022 , vital:28654
- Description: With the increase in link speeds and the growth of the Internet, the volume of NetFlow data generated has increased significantly over time and processing these volumes has become a challenge, more specifically a Big Data challenge. With the advent of technologies and architectures designed to handle Big Data volumes, researchers have investigated their application to the processing of NetFlow data. This work builds on prior work wherein a scoring methodology was proposed for identifying anomalies in NetFlow by proposing and implementing a system that allows for automatic, real-time scoring through the adoption of Big Data stream processing architectures. The first part of the research looks at the means of event detection using the scoring approach and implementing as a number of individual, standalone components, each responsible for detecting and scoring a single type of flow trait. The second part is the implementation of these scoring components in a framework, named Themis1, capable of handling high volumes of data with low latency processing times. This was tackled using tools, technologies and architectural elements from the world of Big Data stream processing. The performance of the framework on the stream processing architecture was shown to demonstrate good flow throughput at low processing latencies on a single low end host. The successful demonstration of the framework on a single host opens the way to leverage the scaling capabilities afforded by the architectures and technologies used. This gives weight to the possibility of using this framework for real time threat detection using NetFlow data from larger networked environments.
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- Date Issued: 2019
A study of malicious software on the macOS operating system
- Authors: Regensberg, Mark Alan
- Date: 2019
- Subjects: Malware (Computer software) , Computer security , Computer viruses , Mac OS
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/92302 , vital:30701
- Description: Much of the published malware research begins with a common refrain: the cost, quantum and complexity of threats are increasing, and research and practice should prioritise efforts to automate and reduce times to detect and prevent malware, while improving the consistency of categories and taxonomies applied to modern malware. Existing work related to malware targeting Apple's macOS platform has not been spared this approach, although limited research has been conducted on the true nature of threats faced by users of the operating system. While macOS focused research available consistently notes an increase in macOS users, devices and ultimately in threats, an opportunity exists to understand the real nature of threats faced by macOS users and suggest potential avenues for future work. This research provides a view of the current state of macOS malware by analysing and exploring a dataset of malware detections on macOS endpoints captured over a period of eleven months by an anti-malware software vendor. The dataset is augmented with malware information provided by the widely used Virus. Total service, as well as the application of prior automated malware categorisation work, AVClass to categorise and SSDeep to cluster and report on observed data. With Windows and Android platforms frequently in the spotlight as targets for highly disruptive malware like botnets, ransomware and cryptominers, research and intuition seem to suggest the threat of malware on this increasingly popular platform should be growing and evolving accordingly. Findings suggests that the direction and nature of growth and evolution may not be entirely as clear as industry reports suggest. Adware and Potentially Unwanted Applications (PUAs) make up the vast majority of the detected threats, with remote access trojans (RATs), ransomware and cryptocurrency miners comprising a relatively small proportion of the detected malware. This provides a number of avenues for potential future work to compare and contrast with research on other platforms, as well as identification of key factors that may influence its growth in the future.
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- Date Issued: 2019
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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- Date Issued: 2019
Modernisation and extension of InetVis: a network security data visualisation tool
- Authors: Johnson, Yestin
- Date: 2019
- Subjects: Data visualization , InetVis (Application software)
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/69223 , vital:29447
- Description: This research undertook an investigation in digital archaeology, modernisation, and revitalisation of the InetVis software application, developed at Rhodes University in 2007. InetVis allows users to visualise network traffic in an interactive 3D scatter plot. This software is based on the idea of the Spinning Cube of Potential Doom, introduced by Stephen Lau. The original InetVis research project aimed to extend this concept and implementation, specifically for use in analysing network telescope traffic. The InetVis source code was examined and ported to run on modern operating systems. The porting process involved updating the UI framework, Qt, from version 3 to 5, as well as adding support for 64-bit compilation. This research extended its usefulness with the implementation of new, high-value, features and improvements. The most notable new features include the addition of a general settings framework, improved screenshot generation, automated visualisation modes, new keyboard shortcuts, and support for building and running InetVis on macOS. Additional features and improvements were identified for future work. These consist of support for a plug-in architecture and an extended heads-up display. A user survey was then conducted, determining that respondents found InetVis to be easy to use and useful. The user survey also allowed the identification of new and proposed features that the respondents found to be most useful. At this point, no other tool offers the simplicity and user-friendliness of InetVis when it comes to the analysis of network packet captures, especially those from network telescopes.
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- Date Issued: 2019