An investigation into the current state of web based cryptominers and cryptojacking
- Authors: Len, Robert
- Date: 2021-04
- Subjects: Cryptocurrencies , Malware (Computer software) , Computer networks -- Security measures , Computer networks -- Monitoring , Cryptomining , Coinhive , Cryptojacking
- Language: English
- Type: thesis , text , Masters , MSc
- Identifier: http://hdl.handle.net/10962/178248 , vital:42924
- Description: The aim of this research was to conduct a review of the current state and extent of surreptitious crypto mining software and its prevalence as a means for income generation. Income is generated through the use of a viewer's browser to execute custom JavaScript code to mine cryptocurrencies such as Monero and Bitcoin. The research aimed to measure the prevalence of illicit mining scripts being utilised for “in-browser" cryptojacking while further analysing the ecosystems that support the cryptomining environment. The extent of the research covers aspects such as the content (or type) of the sites hosting malicious “in-browser" cryptomining software as well as the occurrences of currencies utilised in the cryptographic mining and the analysis of cryptographic mining code samples. This research aims to compare the results of previous work with the current state of affairs since the closure of Coinhive in March 2018. Coinhive were at the time the market leader in such web based mining services. Beyond the analysis of the prevalence of cryptomining on the web today, research into the methodologies and techniques used to detect and counteract cryptomining are also conducted. This includes the most recent developments in malicious JavaScript de-obfuscation as well as cryptomining signature creation and detection. Methodologies for heuristic JavaScript behaviour identification and subsequent identification of potential malicious out-liars are also included within the research of the countermeasure analysis. The research revealed that although no longer functional, Coinhive remained as the most prevalent script being used for “in-browser" cryptomining services. While remaining the most prevalent, there was however a significant decline in overall occurrences compared to when coinhive.com was operational. Analysis of the ecosystem hosting \in-browser" mining websites was found to be distributed both geographically as well as in terms of domain categorisations. , Thesis (MSc) -- Faculty of Science, Computer Science, 2021
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- Authors: Len, Robert
- Date: 2021-04
- Subjects: Cryptocurrencies , Malware (Computer software) , Computer networks -- Security measures , Computer networks -- Monitoring , Cryptomining , Coinhive , Cryptojacking
- Language: English
- Type: thesis , text , Masters , MSc
- Identifier: http://hdl.handle.net/10962/178248 , vital:42924
- Description: The aim of this research was to conduct a review of the current state and extent of surreptitious crypto mining software and its prevalence as a means for income generation. Income is generated through the use of a viewer's browser to execute custom JavaScript code to mine cryptocurrencies such as Monero and Bitcoin. The research aimed to measure the prevalence of illicit mining scripts being utilised for “in-browser" cryptojacking while further analysing the ecosystems that support the cryptomining environment. The extent of the research covers aspects such as the content (or type) of the sites hosting malicious “in-browser" cryptomining software as well as the occurrences of currencies utilised in the cryptographic mining and the analysis of cryptographic mining code samples. This research aims to compare the results of previous work with the current state of affairs since the closure of Coinhive in March 2018. Coinhive were at the time the market leader in such web based mining services. Beyond the analysis of the prevalence of cryptomining on the web today, research into the methodologies and techniques used to detect and counteract cryptomining are also conducted. This includes the most recent developments in malicious JavaScript de-obfuscation as well as cryptomining signature creation and detection. Methodologies for heuristic JavaScript behaviour identification and subsequent identification of potential malicious out-liars are also included within the research of the countermeasure analysis. The research revealed that although no longer functional, Coinhive remained as the most prevalent script being used for “in-browser" cryptomining services. While remaining the most prevalent, there was however a significant decline in overall occurrences compared to when coinhive.com was operational. Analysis of the ecosystem hosting \in-browser" mining websites was found to be distributed both geographically as well as in terms of domain categorisations. , Thesis (MSc) -- Faculty of Science, Computer Science, 2021
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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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- 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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Bolvedere: a scalable network flow threat analysis system
- Authors: Herbert, Alan
- Date: 2019
- Subjects: Bolvedere (Computer network analysis system) , Computer networks -- Scalability , Computer networks -- Measurement , Computer networks -- Security measures , Telecommunication -- Traffic -- Measurement
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/71557 , vital:29873
- Description: Since the advent of the Internet, and its public availability in the late 90’s, there have been significant advancements to network technologies and thus a significant increase of the bandwidth available to network users, both human and automated. Although this growth is of great value to network users, it has led to an increase in malicious network-based activities and it is theorized that, as more services become available on the Internet, the volume of such activities will continue to grow. Because of this, there is a need to monitor, comprehend, discern, understand and (where needed) respond to events on networks worldwide. Although this line of thought is simple in its reasoning, undertaking such a task is no small feat. Full packet analysis is a method of network surveillance that seeks out specific characteristics within network traffic that may tell of malicious activity or anomalies in regular network usage. It is carried out within firewalls and implemented through packet classification. In the context of the networks that make up the Internet, this form of packet analysis has become infeasible, as the volume of traffic introduced onto these networks every day is so large that there are simply not enough processing resources to perform such a task on every packet in real time. One could combat this problem by performing post-incident forensics; archiving packets and processing them later. However, as one cannot process all incoming packets, the archive will eventually run out of space. Full packet analysis is also hindered by the fact that some existing, commonly-used solutions are designed around a single host and single thread of execution, an outdated approach that is far slower than necessary on current computing technology. This research explores the conceptual design and implementation of a scalable network traffic analysis system named Bolvedere. Analysis performed by Bolvedere simply asks whether the existence of a connection, coupled with its associated metadata, is enough to conclude something meaningful about that connection. This idea draws away from the traditional processing of every single byte in every single packet monitored on a network link (Deep Packet Inspection) through the concept of working with connection flows. Bolvedere performs its work by leveraging the NetFlow version 9 and IPFIX protocols, but is not limited to these. It is implemented using a modular approach that allows for either complete execution of the system on a single host or the horizontal scaling out of subsystems on multiple hosts. The use of multiple hosts is achieved through the implementation of Zero Message Queue (ZMQ). This allows for Bolvedre to horizontally scale out, which results in an increase in processing resources and thus an increase in analysis throughput. This is due to ease of interprocess communications provided by ZMQ. Many underlying mechanisms in Bolvedere have been automated. This is intended to make the system more userfriendly, as the user need only tell Bolvedere what information they wish to analyse, and the system will then rebuild itself in order to achieve this required task. Bolvedere has also been hardware-accelerated through the use of Field-Programmable Gate Array (FPGA) technologies, which more than doubled the total throughput of the system.
- Full Text:
- Authors: Herbert, Alan
- Date: 2019
- Subjects: Bolvedere (Computer network analysis system) , Computer networks -- Scalability , Computer networks -- Measurement , Computer networks -- Security measures , Telecommunication -- Traffic -- Measurement
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/71557 , vital:29873
- Description: Since the advent of the Internet, and its public availability in the late 90’s, there have been significant advancements to network technologies and thus a significant increase of the bandwidth available to network users, both human and automated. Although this growth is of great value to network users, it has led to an increase in malicious network-based activities and it is theorized that, as more services become available on the Internet, the volume of such activities will continue to grow. Because of this, there is a need to monitor, comprehend, discern, understand and (where needed) respond to events on networks worldwide. Although this line of thought is simple in its reasoning, undertaking such a task is no small feat. Full packet analysis is a method of network surveillance that seeks out specific characteristics within network traffic that may tell of malicious activity or anomalies in regular network usage. It is carried out within firewalls and implemented through packet classification. In the context of the networks that make up the Internet, this form of packet analysis has become infeasible, as the volume of traffic introduced onto these networks every day is so large that there are simply not enough processing resources to perform such a task on every packet in real time. One could combat this problem by performing post-incident forensics; archiving packets and processing them later. However, as one cannot process all incoming packets, the archive will eventually run out of space. Full packet analysis is also hindered by the fact that some existing, commonly-used solutions are designed around a single host and single thread of execution, an outdated approach that is far slower than necessary on current computing technology. This research explores the conceptual design and implementation of a scalable network traffic analysis system named Bolvedere. Analysis performed by Bolvedere simply asks whether the existence of a connection, coupled with its associated metadata, is enough to conclude something meaningful about that connection. This idea draws away from the traditional processing of every single byte in every single packet monitored on a network link (Deep Packet Inspection) through the concept of working with connection flows. Bolvedere performs its work by leveraging the NetFlow version 9 and IPFIX protocols, but is not limited to these. It is implemented using a modular approach that allows for either complete execution of the system on a single host or the horizontal scaling out of subsystems on multiple hosts. The use of multiple hosts is achieved through the implementation of Zero Message Queue (ZMQ). This allows for Bolvedre to horizontally scale out, which results in an increase in processing resources and thus an increase in analysis throughput. This is due to ease of interprocess communications provided by ZMQ. Many underlying mechanisms in Bolvedere have been automated. This is intended to make the system more userfriendly, as the user need only tell Bolvedere what information they wish to analyse, and the system will then rebuild itself in order to achieve this required task. Bolvedere has also been hardware-accelerated through the use of Field-Programmable Gate Array (FPGA) technologies, which more than doubled the total throughput of the system.
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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.
- Full Text:
- 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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Towards a threat assessment framework for consumer health wearables
- Authors: Mnjama, Javan Joshua
- Date: 2018
- Subjects: Activity trackers (Wearable technology) , Computer networks -- Security measures , Data protection , Information storage and retrieval systems -- Security systems , Computer security -- Software , Consumer Health Wearable Threat Assessment Framework , Design Science Research
- Language: English
- Type: text , Thesis , Masters , MCom
- Identifier: http://hdl.handle.net/10962/62649 , vital:28225
- Description: The collection of health data such as physical activity, consumption and physiological data through the use of consumer health wearables via fitness trackers are very beneficial for the promotion of physical wellness. However, consumer health wearables and their associated applications are known to have privacy and security concerns that can potentially make the collected personal health data vulnerable to hackers. These concerns are attributed to security theoretical frameworks not sufficiently addressing the entirety of privacy and security concerns relating to the diverse technological ecosystem of consumer health wearables. The objective of this research was therefore to develop a threat assessment framework that can be used to guide the detection of vulnerabilities which affect consumer health wearables and their associated applications. To meet this objective, the Design Science Research methodology was used to develop the desired artefact (Consumer Health Wearable Threat Assessment Framework). The framework is comprised of fourteen vulnerabilities classified according to Authentication, Authorization, Availability, Confidentiality, Non-Repudiation and Integrity. Through developing the artefact, the threat assessment framework was demonstrated on two fitness trackers and their associated applications. It was discovered, that the framework was able to identify how these vulnerabilities affected, these two test cases based on the classification categories of the framework. The framework was also evaluated by four security experts who assessed the quality, utility and efficacy of the framework. Experts, supported the use of the framework as a relevant and comprehensive framework to guide the detection of vulnerabilities towards consumer health wearables and their associated applications. The implication of this research study is that the framework can be used by developers to better identify the vulnerabilities of consumer health wearables and their associated applications. This will assist in creating a more securer environment for the storage and use of health data by consumer health wearables.
- Full Text:
- Authors: Mnjama, Javan Joshua
- Date: 2018
- Subjects: Activity trackers (Wearable technology) , Computer networks -- Security measures , Data protection , Information storage and retrieval systems -- Security systems , Computer security -- Software , Consumer Health Wearable Threat Assessment Framework , Design Science Research
- Language: English
- Type: text , Thesis , Masters , MCom
- Identifier: http://hdl.handle.net/10962/62649 , vital:28225
- Description: The collection of health data such as physical activity, consumption and physiological data through the use of consumer health wearables via fitness trackers are very beneficial for the promotion of physical wellness. However, consumer health wearables and their associated applications are known to have privacy and security concerns that can potentially make the collected personal health data vulnerable to hackers. These concerns are attributed to security theoretical frameworks not sufficiently addressing the entirety of privacy and security concerns relating to the diverse technological ecosystem of consumer health wearables. The objective of this research was therefore to develop a threat assessment framework that can be used to guide the detection of vulnerabilities which affect consumer health wearables and their associated applications. To meet this objective, the Design Science Research methodology was used to develop the desired artefact (Consumer Health Wearable Threat Assessment Framework). The framework is comprised of fourteen vulnerabilities classified according to Authentication, Authorization, Availability, Confidentiality, Non-Repudiation and Integrity. Through developing the artefact, the threat assessment framework was demonstrated on two fitness trackers and their associated applications. It was discovered, that the framework was able to identify how these vulnerabilities affected, these two test cases based on the classification categories of the framework. The framework was also evaluated by four security experts who assessed the quality, utility and efficacy of the framework. Experts, supported the use of the framework as a relevant and comprehensive framework to guide the detection of vulnerabilities towards consumer health wearables and their associated applications. The implication of this research study is that the framework can be used by developers to better identify the vulnerabilities of consumer health wearables and their associated applications. This will assist in creating a more securer environment for the storage and use of health data by consumer health wearables.
- Full Text:
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