Reviving legacy enterprise systems with microservice-based architecture within cloud environments
Conference Proceeding
Habibullah, S., Liu, X., Tan, Z., Zhang, Y., & Liu, Q. (2019)
Reviving legacy enterprise systems with microservice-based architecture within cloud environments. In Computer Science Conference Proceedingshttps://doi.org/10.5121/csit.2019.90713
Evolution has always been a challenge for enterprise computing systems. The microservice based architecture is a new design model which is rapidly becoming one of the most eff...
An Approach to Evolving Legacy Enterprise System to Microservice-Based Architecture through Feature-Driven Evolution Rules
Journal Article
Habibullah, S., Liu, X., & Tan, Z. (2018)
An Approach to Evolving Legacy Enterprise System to Microservice-Based Architecture through Feature-Driven Evolution Rules. International Journal of Computer Theory and Engineering, 10(5), 164-169. https://doi.org/10.7763/ijcte.2018.v10.1219
Evolving legacy enterprise systems into a lean system architecture has been on the agendas of many enterprises. Recent advance in legacy system evaluation is in favour of micr...
Automatic Generation of Adversarial Metamorphic Malware Using MAP-Elites
Conference Proceeding
Babaagba, K. O., Tan, Z., & Hart, E. (2020)
Automatic Generation of Adversarial Metamorphic Malware Using MAP-Elites. In Applications of Evolutionary Computation. EvoApplications 2020. , (117-132). https://doi.org/10.1007/978-3-030-43722-0_8
In the field of metamorphic malware detection, training a detection model with malware samples that reflect potential mutants of the malware is crucial in developing a model r...
Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples
Conference Proceeding
Babaagba, K., Tan, Z., & Hart, E. (2020)
Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples. https://doi.org/10.1109/CEC48606.2020.9185668
Detecting metamorphic malware provides a challenge to machine-learning models as trained models might not generalise to future mutant variants of the malware. To address this,...