An analysis of fusing advanced malware email protection logs, malware intelligence and active directory attributes as an instrument for threat intelligence
- Authors: Vermeulen, Japie
- Date: 2018
- Subjects: Malware (Computer software) , Computer networks Security measures , Data mining , Phishing , Data logging , Quantitative research
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/63922 , vital:28506
- Description: After more than four decades email is still the most widely used electronic communication medium today. This electronic communication medium has evolved into an electronic weapon of choice for cyber criminals ranging from the novice to the elite. As cyber criminals evolve with tools, tactics and procedures, so too are technology vendors coming forward with a variety of advanced malware protection systems. However, even if an organization adopts such a system, there is still the daily challenge of interpreting the log data and understanding the type of malicious email attack, including who the target was and what the payload was. This research examines a six month data set obtained from an advanced malware email protection system from a bank in South Africa. Extensive data fusion techniques are used to provide deeper insight into the data by blending these with malware intelligence and business context. The primary data set is fused with malware intelligence to identify the different malware families associated with the samples. Active Directory attributes such as the business cluster, department and job title of users targeted by malware are also fused into the combined data. This study provides insight into malware attacks experienced in the South African financial services sector. For example, most of the malware samples identified belonged to different types of ransomware families distributed by known botnets. However, indicators of targeted attacks were observed based on particular employees targeted with exploit code and specific strains of malware. Furthermore, a short time span between newly discovered vulnerabilities and the use of malicious code to exploit such vulnerabilities through email were observed in this study. The fused data set provided the context to answer the “who”, “what”, “where” and “when”. The proposed methodology can be applied to any organization to provide insight into the malware threats identified by advanced malware email protection systems. In addition, the fused data set provides threat intelligence that could be used to strengthen the cyber defences of an organization against cyber threats.
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- Authors: Vermeulen, Japie
- Date: 2018
- Subjects: Malware (Computer software) , Computer networks Security measures , Data mining , Phishing , Data logging , Quantitative research
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/63922 , vital:28506
- Description: After more than four decades email is still the most widely used electronic communication medium today. This electronic communication medium has evolved into an electronic weapon of choice for cyber criminals ranging from the novice to the elite. As cyber criminals evolve with tools, tactics and procedures, so too are technology vendors coming forward with a variety of advanced malware protection systems. However, even if an organization adopts such a system, there is still the daily challenge of interpreting the log data and understanding the type of malicious email attack, including who the target was and what the payload was. This research examines a six month data set obtained from an advanced malware email protection system from a bank in South Africa. Extensive data fusion techniques are used to provide deeper insight into the data by blending these with malware intelligence and business context. The primary data set is fused with malware intelligence to identify the different malware families associated with the samples. Active Directory attributes such as the business cluster, department and job title of users targeted by malware are also fused into the combined data. This study provides insight into malware attacks experienced in the South African financial services sector. For example, most of the malware samples identified belonged to different types of ransomware families distributed by known botnets. However, indicators of targeted attacks were observed based on particular employees targeted with exploit code and specific strains of malware. Furthermore, a short time span between newly discovered vulnerabilities and the use of malicious code to exploit such vulnerabilities through email were observed in this study. The fused data set provided the context to answer the “who”, “what”, “where” and “when”. The proposed methodology can be applied to any organization to provide insight into the malware threats identified by advanced malware email protection systems. In addition, the fused data set provides threat intelligence that could be used to strengthen the cyber defences of an organization against cyber threats.
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NetwIOC: a framework for the automated generation of network-based IOCS for malware information sharing and defence
- Authors: Rudman, Lauren Lynne
- Date: 2018
- Subjects: Malware (Computer software) , Computer networks Security measures , Computer security , Python (Computer program language)
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/60639 , vital:27809
- Description: With the substantial number of new malware variants found each day, it is useful to have an efficient way to retrieve Indicators of Compromise (IOCs) from the malware in a format suitable for sharing and detection. In the past, these indicators were manually created after inspection of binary samples and network traffic. The Cuckoo Sandbox, is an existing dynamic malware analysis system which meets the requirements for the proposed framework and was extended by adding a few custom modules. This research explored a way to automate the generation of detailed network-based IOCs in a popular format which can be used for sharing. This was done through careful filtering and analysis of the PCAP hie generated by the sandbox, and placing these values into the correct type of STIX objects using Python, Through several evaluations, analysis of what type of network traffic can be expected for the creation of IOCs was conducted, including a brief ease study that examined the effect of analysis time on the number of IOCs created. Using the automatically generated IOCs to create defence and detection mechanisms for the network was evaluated and proved successful, A proof of concept sharing platform developed for the STIX IOCs is showcased at the end of the research.
- Full Text:
- Authors: Rudman, Lauren Lynne
- Date: 2018
- Subjects: Malware (Computer software) , Computer networks Security measures , Computer security , Python (Computer program language)
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/60639 , vital:27809
- Description: With the substantial number of new malware variants found each day, it is useful to have an efficient way to retrieve Indicators of Compromise (IOCs) from the malware in a format suitable for sharing and detection. In the past, these indicators were manually created after inspection of binary samples and network traffic. The Cuckoo Sandbox, is an existing dynamic malware analysis system which meets the requirements for the proposed framework and was extended by adding a few custom modules. This research explored a way to automate the generation of detailed network-based IOCs in a popular format which can be used for sharing. This was done through careful filtering and analysis of the PCAP hie generated by the sandbox, and placing these values into the correct type of STIX objects using Python, Through several evaluations, analysis of what type of network traffic can be expected for the creation of IOCs was conducted, including a brief ease study that examined the effect of analysis time on the number of IOCs created. Using the automatically generated IOCs to create defence and detection mechanisms for the network was evaluated and proved successful, A proof of concept sharing platform developed for the STIX IOCs is showcased at the end of the research.
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