Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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Author: search.filters.author.Jang-Jaccard JSubject: search.filters.subject.1702 Cognitive Sciences

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    Masquerade Attacks Against Security Software Exclusion Lists
    (AJIIPS, 2019) McIntosh T; Jang-Jaccard J; Watters P; Susnjak T
    Security software, commonly known as Antivirus, has evolved from simple virus scanners to become multi-functional security suites. To combat ever-growing malware threats, modern security software utilizes both static and dynamic analysis to assess malware threats, inevitably leading to occasional false positive and false negative reports. To mitigate this, existing state-of-the-art security software offers the feature of Exclusion Lists to allow users to exclude specified files and folders from being scanned or monitored. Through rigorous evaluation, however, we found that some of such products stored their Exclusion Lists as unencrypted cleartexts either in known or predictable locations. In this paper we empirically demonstrate how easy it is to exploit the Exclusion Lists by launching masquerade attacks. We argue that the Exclusion Lists should be better implemented such as using application whitelisting, the contents of the lists to be better safeguarded, and only be readable by authorized entities within a strong access control scheme.
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    Joint Spectral Clustering based on Optimal Graph and Feature Selection
    (Springer Nature Switzerland AG, 2021-02) Zhu J; Jang-Jaccard J; Liu T; Zhou J
    Redundant features and outliers (noise) included in the data points for a machine learning clustering model heavily influences the discovery of more distinguished features for clustering. To solve this issue, we propose a spectral new clustering method to consider the feature selection with the L2 , 1-norm regularization as well as simultaneously learns orthogonal representations for each sample to preserve the local structures of data points. Our model also solves the issue of out-of-sample, where the training process does not output an explicit model to predict unseen data points, along with providing an efficient optimization method for the proposed objective function. Experimental results showed that our method on twelve data sets achieves the best performance compared with other similar models.