Name
Abstract | ||
|---|---|---|
The increase in number and variety of malware samples amplifies the need for improvement in automatic detection and classification of the malware variants. Machine learning is a natural choice to cope with this increase, because it addresses the need of discovering underlying patterns in large-scale datasets. Nowadays, neural network methodology has been grown to the state that can surpass limitations of previous machine learning methods, such as Hidden Markov Models and Support Vector Machines. As a consequence, neural networks can now offer superior classification accuracy in many domains, such as computer vision or natural language processing. This improvement comes from the possibility of constructing neural networks with a higher number of potentially diverse layers and is known as Deep Learning. |
Year | Venue | Field |
|---|---|---|
2016 | Australasian Conference on Artificial Intelligence | Computer science,Neural network architecture,Support vector machine,Speech recognition,System call,Artificial intelligence,Deep learning,Malware,Hidden Markov model,Artificial neural network,Machine learning |
DocType | Citations | PageRank |
Conference | 12 | 0.59 |
References | Authors | |
19 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bojan Kolosnjaji | 1 | 30 | 2.99 |
| Apostolis Zarras | 2 | 162 | 12.33 |
| George D. Webster | 3 | 22 | 2.28 |
| Claudia Eckert | 4 | 76 | 13.13 |