| 2025 |
Qiu J, Jiang Y, Miao Y, Luo W, Pan L, Zheng X, 'A survey of coverage-guided greybox fuzzing with deep neural models', Information and Software Technology, 186 (2025) [C1]
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| 2025 |
Li Z, Zhu W, Zhang H, Miao Y, Ren J, 'The impact of unsupervised feature selection techniques on the performance and interpretation of defect prediction models', Automated Software Engineering, 32 (2025) [C1]
The performance and interpretation of a defect prediction model depend on the software metrics utilized in its construction. Feature selection techniques can enhance mo... [more]
The performance and interpretation of a defect prediction model depend on the software metrics utilized in its construction. Feature selection techniques can enhance model performance and interpretation by effectively removing redundant, correlated, and irrelevant metrics from defect datasets. Previous empirical studies have scrutinized the impact of feature selection techniques on the performance and interpretation of defect prediction models. However, most feature selection techniques examined in these studies are primarily supervised. In particular, the impact of unsupervised feature selection (UFS) techniques on defect prediction remains unknown and needs to be explored extensively. To address this gap, we systematically apply 21 UFS techniques to evaluate their impact on the performance and interpretation of unsupervised defect prediction models in binary classification and effort-aware ranking scenarios. Extensive experiments are conducted on the 28 versions from 8 projects using 4 unsupervised models. We observe that: (1) 10¿100% of the selected metrics are inconsistent between each pair of UFS techniques. (2) 29¿100% of the selected metrics are inconsistent among different software modules. (3) For unsupervised defect prediction models, some UFS techniques (e.g., AutoSpearman, LS, and FMIUFS) exhibit the ability to effectively reduce the number of metrics while maintaining or even improving model performance. (4) UFS techniques alter the ranking of the top 3 groups of metrics in defect models, affecting the interpretation of these models. Based on these findings, we recommend that software practitioners utilize UFS techniques for unsupervised defect prediction. However, caution should be exercised when deriving insights and interpretations from defect prediction models.
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| 2021 |
Miao Y, Chen C, Pan L, Han Q-L, Zhang J, Xiang Y, 'Machine Learning-based Cyber Attacks Targeting on Controlled Information: A Survey', ACM COMPUTING SURVEYS, 54 (2021) [C1]
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| 2021 |
Miao Y, Minhui X, Chen C, Pan L, Zhang J, Zhao BZH, Kaafar D, Xiang Y, 'The audio auditor: user-level membership inference in Internet of Things voice services', Proceedings on Privacy Enhancing Technologies, 2021, 209-228 (2021) [C1]
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| 2019 |
Miao Y, Zhao BZH, Xue M, Chen C, Pan L, Zhang J, et al., 'The audio auditor: Participant-level membership inference in internet of things voice services (2019)
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| 2018 |
Ruan Z, Miao Y, Pan L, Xiang Y, Zhang J, 'Big network traffic data visualization', Multimedia Tools and Applications, 77 11459-11487 (2018) [C1]
Visualization is an important tool for capturing the network activities. Effective visualization allows people to gain insights into the data information and discovery ... [more]
Visualization is an important tool for capturing the network activities. Effective visualization allows people to gain insights into the data information and discovery of communication patterns of network flows. Such information may be difficult for human to perceive its relationships due to its numeric nature such as time, packet size, inter-packet time, and many other statistical features. Many existing work fail to provide an effective visualization method for big network traffic data. This work proposes a novel and effective method for visualizing network traffic data with statistical features of high dimensions. We combine Principal Component Analysis (PCA) and Mutidimensional Scaling (MDS) to effectively reduce dimensionality and use colormap for enhance visual quality for human beings. We obtain high quality images on a real-world network traffic dataset named 'ISP'. Comparing with the popular t-SNE method, our visualization method is more flexible and scalable for plotting network traffic data which may require to preserve multi-dimensional information and relationship. Our plots also demonstrate the capability of handling a large amount of data. Using our method, the readers will be able to visualize their network traffic data as an alternative method of t-SNE.
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| 2017 |
Ruan Z, Miao Y, Pan L, Patterson N, Zhang J, 'Visualization of big data security: a case study on the KDD99 cup data set', Digital Communications and Networks, 3 250-259 (2017) [C1]
Cyber security has been thrust into the limelight in the modern technological era because of an array of attacks often bypassing untrained intrusion detection systems (... [more]
Cyber security has been thrust into the limelight in the modern technological era because of an array of attacks often bypassing untrained intrusion detection systems (IDSs). Therefore, greater attention has been directed on being able deciphering better methods for identifying attack types to train IDSs more effectively. Keycyber-attack insights exist in big data; however, an efficient approach is required to determine strong attack types to train IDSs to become more effective in key areas. Despite the rising growth in IDS research, there is a lack of studies involving big data visualization, which is key. The KDD99 data set has served as a strong benchmark since 1999; therefore, we utilized this data set in our experiment. In this study, we utilized hash algorithm, a weight table, and sampling method to deal with the inherent problems caused by analyzing big data; volume, variety, and velocity. By utilizing a visualization algorithm, we were able to gain insights into the KDD99 data set with a clear identification of "normal" clusters and described distinct clusters of effective attacks.
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