Nowhere Metamorphic Malware Can Hide - A Biological Evolution Inspired Detection Scheme
Conference Proceeding
Babaagba, K. O., Tan, Z., & Hart, E. (2019)
Nowhere Metamorphic Malware Can Hide - A Biological Evolution Inspired Detection Scheme. In Dependability in Sensor, Cloud, and Big Data Systems and Applications. , (369-382). https://doi.org/10.1007/978-981-15-1304-6_29
The ability to detect metamorphic malware has generated significant research interest over recent years, particularly given its proliferation on mobile devices. Such malware i...
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,...
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...