title: Machine learning approaches for malware classification based on hybrid artefacts

authors: Chanajitt, Rajchada

subjects: Static

abstract: Malware could be developed and transformed into various forms to deceive users and evade antivirus and security endpoint detection. Furthermore, if one machine in the network is compromised, it could be used for lateral movement--when malware spreads stealthily without sending an alarm to monitoring systems. Malware attacks pose security threats to modern enterprises and can cause massive financial, reputation, and data loss to major enterprises. Therefore, it is important to detect these attacks effectively to reduce the loss to the minimum level. The current research uses different approaches, including static and dynamic analysis, to detect and analyze malware categories using distinct feature sets, such as imported modules, opcodes, and API calls, which can improve performance in binary and multi-class classification problems.

This thesis proposes a method for identifying and analyzing malware samples via static and dynamic approaches, including memory analysis and consecutive application operation sequences performed on the Windows 10 virtual environment. Standard classifiers and frequently used sequence models are utilized to expose the malware characteristics and benefit predictive capabilities. The features used in these algorithms are extracted from the static and dynamic analysis of malware samples, such as the rich header feature, debug information, temporary files, prefetch files, and event logs. The measurement of the classifiers and the degree of correctness are calculated using the accuracy, f1-score, Mean Absolute Error (MAE), confusion matrix, and Area under the ROC Curve (AUC). Combining two feature sets can provide the best classification performance on static file properties and dynamic analysis results, regardless of whether applying feature selection or not, achieving the accuracy and f1_score at 97% for integrating two datasets. For consecutive sequences, concatenating the Gated Recurrent Unit (GRU) and Transformers model can yield the highest accuracy at 97% for Noriben operations, while GRU can achieve the maximum accuracy for Opcode sequences at 89%.

text: Machine learning approaches for malware classification based on hybrid artefacts Malware could be developed and transformed into various forms to deceive users and evade antivirus and security endpoint detection. Furthermore, if one machine in the network is compromised, it could be used for lateral movement--when malware spreads stealthily without sending an alarm to monitoring systems. Malware attacks pose security threats to modern enterprises and can cause massive financial, reputation, and data loss to major enterprises. Therefore, it is important to detect these attacks effectively to reduce the loss to the minimum level. The current research uses different approaches, including static and dynamic analysis, to detect and analyze malware categories using distinct feature sets, such as imported modules, opcodes, and API calls, which can improve performance in binary and multi-class classification problems.

This thesis proposes a method for identifying and analyzing malware samples via static and dynamic approaches, including memory analysis and consecutive application operation sequences performed on the Windows 10 virtual environment. Standard classifiers and frequently used sequence models are utilized to expose the malware characteristics and benefit predictive capabilities. The features used in these algorithms are extracted from the static and dynamic analysis of malware samples, such as the rich header feature, debug information, temporary files, prefetch files, and event logs. The measurement of the classifiers and the degree of correctness are calculated using the accuracy, f1-score, Mean Absolute Error (MAE), confusion matrix, and Area under the ROC Curve (AUC). Combining two feature sets can provide the best classification performance on static file properties and dynamic analysis results, regardless of whether applying feature selection or not, achieving the accuracy and f1_score at 97% for integrating two datasets. For consecutive sequences, concatenating the Gated Recurrent Unit (GRU) and Transformers model can yield the highest accuracy at 97% for Noriben operations, while GRU can achieve the maximum accuracy for Opcode sequences at 89%.

year: 2023

title: Automated capture/replay test generation for android using espresso

authors: Alblooshy, Fajer

subjects: Android testing

abstract: This thesis presents the design of a new tool for automated test generation in Android applications using the Espresso framework. The primary focus is on designing a robust and efficient tool with the use of the Presentation Model (PModels) to simplify the automation of test case design. By incorporating techniques such as interaction sequence modeling and widget-based abstraction, the tool’s design aims to address the challenges of testing interactive systems, which are often timeconsuming and complex. The research includes comprehensively analyzing existing testing methodologies, identifying their limitations, and proposing enhancements to improve test coverage and reliability. Through detailed design documentation and conceptual frameworks, the study demonstrates how the tool can generate maintainable test scripts, reducing the effect required for manual testing. The tool’s design is evaluated through theoretical case studies, including various Android applications, to illustrate its potential effectiveness in real-world scenarios. This work contributes to the field of software engineering by offering an approach to automated UI testing, emphasizing the importance of design considerations in developing tools that can meet the evolving demands of software quality assurance.

text: Automated capture/replay test generation for android using espresso This thesis presents the design of a new tool for automated test generation in Android applications using the Espresso framework. The primary focus is on designing a robust and efficient tool with the use of the Presentation Model (PModels) to simplify the automation of test case design. By incorporating techniques such as interaction sequence modeling and widget-based abstraction, the tool’s design aims to address the challenges of testing interactive systems, which are often timeconsuming and complex. The research includes comprehensively analyzing existing testing methodologies, identifying their limitations, and proposing enhancements to improve test coverage and reliability. Through detailed design documentation and conceptual frameworks, the study demonstrates how the tool can generate maintainable test scripts, reducing the effect required for manual testing. The tool’s design is evaluated through theoretical case studies, including various Android applications, to illustrate its potential effectiveness in real-world scenarios. This work contributes to the field of software engineering by offering an approach to automated UI testing, emphasizing the importance of design considerations in developing tools that can meet the evolving demands of software quality assurance.

year: 2024-11-25

title: Māori & Psychology Research Unit annual report 2011

authors: Rua, Mohi

subjects: Māori and Psychology Research Unit

abstract: Annual report of the Māori and Psychology Research Unit (MPRU) 2011. The unit was established in August of 1997. The unit is designed to provide a catalyst and support network for enhancing research concerning the psychological needs, aspirations, and priorities of Maori people. The MPRU is well situated to draw together skilled and experienced interdisciplinary research groups by networking and establishing working relationships with staff and students within the Faculty of Arts and Social Sciences, the University, and the wider community.

text: Māori & Psychology Research Unit annual report 2011 Annual report of the Māori and Psychology Research Unit (MPRU) 2011. The unit was established in August of 1997. The unit is designed to provide a catalyst and support network for enhancing research concerning the psychological needs, aspirations, and priorities of Maori people. The MPRU is well situated to draw together skilled and experienced interdisciplinary research groups by networking and establishing working relationships with staff and students within the Faculty of Arts and Social Sciences, the University, and the wider community.

year: 2012