Research Publications Authored by SLIIT Staff
Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/4195
This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
Browse
2 results
Search Results
Publication Embargo Mobile-based Malware Detection and Classification using Ensemble Artificial Intelligence(IEEE, 2019-12-05) Somasundaram, S; Kasthurirathna, D; Rupasinghe, LThe Android operating system is one of the most used operating systems in the world and has become a target to malware authors. Traditional malware detection methods such as signatures find it impossible to deal with detecting complex and intelligent malware which are capable of obfuscating and repackaging to avoid being detected. There is therefore an increase in the need to have more efficient and intelligent forms of malware detection. Artificial intelligence has now been brought to the field of malware detection and classification. Due to its accuracy and intelligence it has become an ideal solution to bridge the gap between traditional classifiers and the intelligent malware. Currently, research is mainly being conducted using either machine learning or deep learning techniques to target all or a given malware family. This paper proposes a methodology which brings an ensemble solution between the Support Vector Machine algorithm and the Convolutional Neural Network to create a solution that provides a higher accuracy than available techniques.Publication Embargo Mobile-based Malware Detection and Classification using Ensemble Artificial Intelligence(IEEE, 2019-12-05) Somasundaram, S; Kasthurirathna, D; Rupasinghe, LThe Android operating system is one of the most used operating systems in the world and has become a target to malware authors. Traditional malware detection methods such as signatures find it impossible to deal with detecting complex and intelligent malware which are capable of obfuscating and repackaging to avoid being detected. There is therefore an increase in the need to have more efficient and intelligent forms of malware detection. Artificial intelligence has now been brought to the field of malware detection and classification. Due to its accuracy and intelligence it has become an ideal solution to bridge the gap between traditional classifiers and the intelligent malware. Currently, research is mainly being conducted using either machine learning or deep learning techniques to target all or a given malware family. This paper proposes a methodology which brings an ensemble solution between the Support Vector Machine algorithm and the Convolutional Neural Network to create a solution that provides a higher accuracy than available techniques.