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- Adversarial Attacks and Defenses (1)
- Biometric quality (1)
- Code-based static data (1)
- Cybersecurity. Bug Reports (1)
- Data Mining (1)
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- Decision level fusion (1)
- FIQA (1)
- Face biometrics (1)
- Face image quality (1)
- Face recognition (1)
- Feature Selection (1)
- Feature level fusion (1)
- File-to-file Relations (1)
- HIN (1)
- LSTM (1)
- Machine Learning (1)
- Malware Detection (1)
- Malware detection (1)
- Multimodal learning (1)
- Network traffic data (1)
- Power consumption (1)
- Social Code platform (1)
- System logs (1)
- Text Classification (1)
Articles 1 - 5 of 5
Full-Text Articles in Physical Sciences and Mathematics
Evaluation And Understandability Of Face Image Quality Assessment, Mohammad I. Nouyed
Evaluation And Understandability Of Face Image Quality Assessment, Mohammad I. Nouyed
Graduate Theses, Dissertations, and Problem Reports
Face image quality assessment (FIQA) has been an area of interest to researchers as a way to improve the face recognition accuracy. By filtering out the low quality images we can reduce various difficulties faced in unconstrained face recognition, such as, failure in face or facial landmark detection or low presence of useful facial information. In last decade or so, researchers have proposed different methods to assess the face image quality, spanning from fusion of quality measures to using learning based methods. Different approaches have their own strength and weaknesses. But, it is hard to perform a comparative assessment of …
Automatic Detection Of Insecure Codes In Stack Overflow, Shifu Hou
Automatic Detection Of Insecure Codes In Stack Overflow, Shifu Hou
Graduate Theses, Dissertations, and Problem Reports
As the popularity of modern social coding paradigm such as Stack Overflow grows, its potential security risks increase as well (e.g., insecure codes could be easily embedded and distributed). To address this largely overlooked issue, we bring a new insight to exploit social coding properties in addition to code content for automatic detection of insecure code snippets in Stack Overflow. To determine if the given code snippets are insecure, we not only analyze the code content, but also utilize various kinds of relations among users, badges, questions, answers, code snippets and keywords in Stack Overflow. To model the rich semantic …
Multimodal Approach For Malware Detection, Jarilyn M. Hernandez Jimenez
Multimodal Approach For Malware Detection, Jarilyn M. Hernandez Jimenez
Graduate Theses, Dissertations, and Problem Reports
Although malware detection is a very active area of research, few works were focused on using physical properties (e.g., power consumption) and multimodal features for malware detection. We designed an experimental testbed that allowed us to run samples of malware and non-malicious software applications and to collect power consumption, network traffic, and system logs data, and subsequently to extract dynamic behavioral-based features. We also extracted code-based static features of both malware and non-malicious software applications. These features were used for malware detection based on: feature level fusion using power consumption and network traffic data, feature level fusion using network traffic …
Intelligent Malware Detection Using File-To-File Relations And Enhancing Its Security Against Adversarial Attacks, Lingwei Chen
Intelligent Malware Detection Using File-To-File Relations And Enhancing Its Security Against Adversarial Attacks, Lingwei Chen
Graduate Theses, Dissertations, and Problem Reports
With computing devices and the Internet being indispensable in people's everyday life, malware has posed serious threats to their security, making its detection of utmost concern. To protect legitimate users from the evolving malware attacks, machine learning-based systems have been successfully deployed and offer unparalleled flexibility in automatic malware detection. In most of these systems, resting on the analysis of different content-based features either statically or dynamically extracted from the file samples, various kinds of classifiers are constructed to detect malware. However, besides content-based features, file-to-file relations, such as file co-existence, can provide valuable information in malware detection and make …
Security Bug Report Classification Using Feature Selection, Clustering, And Deep Learning, Tanner D. Gantzer
Security Bug Report Classification Using Feature Selection, Clustering, And Deep Learning, Tanner D. Gantzer
Graduate Theses, Dissertations, and Problem Reports
As the numbers of software vulnerabilities and cybersecurity threats increase, it is becoming more difficult and time consuming to classify bug reports manually. This thesis is focused on exploring techniques that have potential to improve the performance of automated classification of software bug reports as security or non-security related. Using supervised learning, feature selection was used to engineer new feature vectors to be used in machine learning. Feature selection changes the vocabulary used by selecting words with the greatest impact on classification. Feature selection was able to increase the F-Score across the datasets by increasing the precision. We also explored …