Browsing by Author "Elisa, Noe"

Now showing 1 - 4 of 4
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    Dendritic cell algorithm enhancement using fuzzy inference system for network intrusion detection
    (IEEE, 2019) Elisa, Noe; Yang, Longzhi; Fu, Xin; Naik, Nitin
    Dendritic cell algorithm (DCA) is an immune-inspired classification algorithm which is developed for the purpose of anomaly detection in computer networks. The DCA uses a weighted function in its context detection phase to process three categories of input signals including safe, danger and pathogenic associated molecular pattern to three output context values termed as co-stimulatory, mature and semi-mature, which are then used to perform classification. The weighted function used by the DCA requires either manually pre-defined weights usually provided by the immunologists, or empirically derived weights from the training dataset. Neither of these is sufficiently flexible to work with different datasets to produce optimum classification result. To address such limitation, this work proposes an approach for computing the three output context values of the DCA by employing the recently proposed TSK+ fuzzy inference system, such that the weights are always optimal for the provided data set regarding a specific application. The proposed approach was validated and evaluated by applying it to the two popular datasets KDD99 and UNSW NB15. The results from the experiments demonstrate that, the proposed approach outperforms the conventional DCA in terms of classification accuracy.
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    On the Impact of Refactorings on Software Attack Surface
    (Institute of Electrical and Electronics Engineers (IEEE), 2024) Edward, Estomii; Nyamawe, Ally S.; Elisa, Noe
    Refactoring is one of the techniques mostly employed by software developers to improve the quality attributes of their systems. However, little has been done to investigate how refactoring operations specifically aimed at improving the internal structure of software can impact its security. Refactoring usually entails different code change operations including the decomposition of classes, methods, and the reallocation of code elements. While this refinement aims to improve the internal design of a system, it might inadvertently disperse security-critical code elements throughout the codebase. Consequently, such dispersion could affect the software attack surface. To this end, this paper presents an empirical study of 30 open-source software systems developed in Python, C, and Javascript. The study scrutinized two subsequent versions of each subject application to uncover the refactoring operations applied and the trend of the software attack surface. Specifically, the study focused on the injection or removal of bugs, code smells and other vulnerabilities aiming to discern the impact of refactorings on the software attack surface. Data was collected using wellknown tools, namely SonarQube, RefDiff, and PyReff. The findings suggest that refactorings can have multiple impacts (i.e., positive, negative, or neutral) on bugs, code smells, and vulnerabilities. The findings further confirm that developers must be aware of the combination or sequence of refactoring operations that can improve software quality without compromising its security
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    The Nexus of Big Data and Big Data Analytics for Managerial Business Decision-Making: A Systematic Review Analysis
    (University of Dar es Salaam, 2024) Didas, Matendo; Chali, Frederick; Elisa, Noe
    The growing usage of big data and big data analytics in business has prompted academics and professionals to widen their examination of their implications in business decision-making procedures.  Until now, academics and business leaders have concentrated solely on the technical components of big data and analytics, ignoring the impact they have on the effectiveness of commercial decision-making systems. To begin, this paper intends to review the literature on the study of the relationship between the use of big data and big data analytics for its effectiveness in business industrial decision-making systems. Second, it gives important facts to assess whether big data and big data analytics catalyze the deployment of sophisticated business intelligence and informed decision-making representations. In this regard, the paper identifies the essential concerns that underpin the business-driven decision-making processes such as efficiency, and preciseness among others. Fundamentally, the current work contributes to the literature on big data and big data analytics for business-driven decision-making in both a theoretical fashion and provides a shot for future agenda possibilities to develop knowledge in this area.
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    Towards big data governance in cybersecurity
    (Springer, 2019) Yang, Longzhi; Li, Jie; Elisa, Noe; Prickett, Tom; Chao, Fei
    Big data refers to large complex structured or unstructured data sets. Big data technologies enable organisations to generate, collect, manage, analyse, and visualise big data sets, and provide insights to inform diagnosis, prediction, or other decision-making tasks. One of the critical concerns in handling big data is the adoption of appropriate big data governance frameworks to (1) curate big data in a required manner to support quality data access for effective machine learning and (2) ensure the framework regulates the storage and processing of the data from providers and users in a trustworthy way within the related regulatory frameworks (both legally and ethically). This paper proposes a framework of big data governance that guides organisations to make better data-informed business decisions within the related regularity framework, with close attention paid to data security, privacy, and accessibility. In order to demonstrate this process, the work also presents an example implementation of the framework based on the case study of big data governance in cybersecurity. This framework has the potential to guide the management of big data in different organisations for information sharing and cooperative decision-making.