Simplified menu-driven data analysis tool with macro-like automation
- Authors: Kazembe, Luntha
- Date: 2022-10-14
- Subjects: Data analysis , Macro instructions (Electronic computers) , Quantitative research Software , Python (Computer program language) , Scripting languages (Computer science)
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/362905 , vital:65373
- Description: This study seeks to improve the data analysis process for individuals and small businesses with limited resources by developing a simplified data analysis software tool that allows users to carry out data analysis effectively and efficiently. Design considerations were identified to address limitations common in such environments, these included making the tool easy-to-use, requiring only a basic understanding of the data analysis process, designing the tool in manner that minimises computing resource requirements and user interaction and implementing it using Python which is open-source, effective and efficient in processing data. We develop a prototype simplified data analysis tool as a proof-of-concept. The tool has two components, namely, core elements which provide functionality for the data anal- ysis process including data collection, transformations, analysis and visualizations, and automation and performance enhancements to improve the data analysis process. The automation enhancements consist of the record and playback macro feature while the performance enhancements include multiprocessing and multi-threading abilities. The data analysis software was developed to analyse various alpha-numeric data formats by using a variety of statistical and mathematical techniques. The record and playback macro feature enhances the data analysis process by saving users time and computing resources when analysing large volumes of data or carrying out repetitive data analysis tasks. The feature has two components namely, the record component that is used to record data analysis steps and the playback component used to execute recorded steps. The simplified data analysis tool has parallelization designed and implemented which allows users to carry out two or more analysis tasks at a time, this improves productivity as users can do other tasks while the tool is processing data using recorded steps in the background. The tool was created and subsequently tested using common analysis scenarios applied to network data, log data and stock data. Results show that decision-making requirements such as accurate information, can be satisfied using this analysis tool. Based on the functionality implemented, similar analysis functionality to that provided by Microsoft Excel is available, but in a simplified manner. Moreover, a more sophisticated macro functionality is provided for the execution of repetitive tasks using the recording feature. Overall, the study found that the simplified data analysis tool is functional, usable, scalable, efficient and can carry out multiple analysis tasks simultaneously. , Thesis (MSc) -- Faculty of Science, Computer Science, 2022
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- Date Issued: 2022-10-14
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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- Date Issued: 2018