| Title | A new approach to detect next generation of malware based on machine learning |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Ben Abdel Ouahab, Ikram, Elaachak, Lotfi, Alluhaidan, Yasser A., Bouhorma, Mohammed |
| Conference Name | 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) |
| Date Published | sep |
| Keywords | cybersecurity, Human Behavior, machine learning, machine learning algorithms, Malware, malware classification, Pediatrics, Predictive Metrics, privacy, pubcrawl, Radio frequency, Resiliency, Technological innovation, Training, visualization, visualization technique, Zero-Day |
| Abstract | In these days, malware attacks target different kinds of devices as IoT, mobiles, servers even the cloud. It causes several hardware damages and financial losses especially for big companies. Malware attacks represent a serious issue to cybersecurity specialists. In this paper, we propose a new approach to detect unknown malware families based on machine learning classification and visualization technique. A malware binary is converted to grayscale image, then for each image a GIST descriptor is used as input to the machine learning model. For the malware classification part we use 3 machine learning algorithms. These classifiers are so efficient where the highest precision reach 98%. Once we train, test and evaluate models we move to simulate 2 new malware families. We do not expect a good prediction since the model did not know the family; however our goal is to analyze the behavior of our classifiers in the case of new family. Finally, we propose an approach using a filter to know either the classification is normal or it's a zero-day malware. |
| DOI | 10.1109/3ICT53449.2021.9581625 |
| Citation Key | ben_abdel_ouahab_new_2021 |
A new approach to detect next generation of malware based on machine learning