Kenneth McGarry

Bamidele Ajayi, Basel Barakat, Ken McGarry

This paper assesses the performance of five machine learning classifiers: Decision Tree, Naive Bayes, LightGBM, Logistic Regression, and Random Forest using latent representations learned by a Variational Autoencoder from malware datasets. Results from the experiments conducted on different training-test splits with different random seeds reveal that all the models perform well in detecting malware with ensemble methods (LightGBM and Random Forest) performing slightly better than the rest. In addition, the use of latent features reduces the computational cost of the model and the need for extensive hyperparameter tuning for improved efficiency of the model for deployment. Statistical tests show that these improvements are significant, and thus, the practical relevance of integrating latent space representation with traditional classifiers for effective malware detection in cybersecurity is established.

Guendalina Caldarini, Sardar Jaf, Kenneth McGarry

Chatbots are intelligent conversational computer systems designed to mimic human conversation to enable automated online guidance and support. The increased benefits of chatbots led to their wide adoption by many industries in order to provide virtual assistance to customers. Chatbots utilise methods and algorithms from two Artificial Intelligence domains: Natural Language Processing and Machine Learning. However, there are many challenges and limitations in their application. In this survey we review recent advances on chatbots, where Artificial Intelligence and Natural Language processing are used. We highlight the main challenges and limitations of current work and make recommendations for future research investigation.