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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...

Requirements for getting a robot to grow up.

Conference Proceeding
Ross, P., Hart, E., Lawson, A., Webb, A., Prem, E., Poelz, P. & Morgavi, G. (2003)
Requirements for getting a robot to grow up. ISBN 9783540200574

Controlling a simulated Khepera with an XCS classifier system with memory.

Conference Proceeding
Webb, A., Hart, E., Ross, P. & Lawson, A. (2003)
Controlling a simulated Khepera with an XCS classifier system with memory. ISBN 9783540200574
Autonomous agents commonly suffer from perceptual aliasing in which differing situations are perceived as identical by the robots sensors, yet require different courses of act...