| Title | A Malware Detection Approach Using Malware Images and Autoencoders |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Jin, Xiang, Xing, Xiaofei, Elahi, Haroon, Wang, Guojun, Jiang, Hai |
| Conference Name | 2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS) |
| Keywords | autoencoders, Human Behavior, Labeling, Malware, malware analysis, malware detection, malware images, Predictive Metrics, privacy, pubcrawl, Resiliency, Sensor systems, supervised learning, Task Analysis, Training data, unsupervised learning |
| Abstract | Most machine learning-based malware detection systems use various supervised learning methods to classify different instances of software as benign or malicious. This approach provides no information regarding the behavioral characteristics of malware. It also requires a large amount of training data and is prone to labeling difficulties and can reduce accuracy due to redundant training data. Therefore, we propose a malware detection method based on deep learning, which uses malware images and a set of autoencoders to detect malware. The method is to design an autoencoder to learn the functional characteristics of malware, and then to observe the reconstruction error of autoencoder to realize the classification and detection of malware and benign software. The proposed approach achieves 93% accuracy and comparatively better F1-score values while detecting malware and needs little training data when compared with traditional malware detection systems. |
| DOI | 10.1109/MASS50613.2020.00009 |
| Citation Key | jin_malware_2020 |
A Malware Detection Approach Using Malware Images and Autoencoders