Name
Title | ||
|---|---|---|
Absent extreme learning machine algorithm with application to packed executable identification. |
Abstract | ||
|---|---|---|
Extreme learning machine (ELM) has been an important research topic over the last decade due to its high efficiency, easy-implementation, unification of classification and regression, and unification of binary and multi-class learning tasks. Though integrating these advantages, existing ELM algorithms cannot directly handle the case where some features of the samples are missing or unobserved, which is usually very common in practical applications. The work in this paper fills this gap by proposing an absent ELM (A-ELM) algorithm to address the above issue. By observing the fact that some structural characteristics of a part of packed malware instances hold unreasonable values, we cast the packed executable identification tasks into an absence learning problem, which can be efficiently addressed via the proposed A-ELM algorithm. Extensive experiments have been conducted on six UCI data sets and a packed data set to evaluate the performance of the proposed algorithm. As indicated, the proposed A-ELM algorithm is superior to other imputation algorithms and existing state-of-the-art ones. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s00521-014-1558-4 | Neural Computing and Applications |
Keywords | Field | DocType |
Extreme learning machine, Packed executable identification, Absent learning, Malware | Data mining,Data set,Extreme learning machine,Computer science,Artificial intelligence,Binary number,Executable,Regression,Unification,Algorithm,Imputation (statistics),Malware,Machine learning | Journal |
Volume | Issue | ISSN |
27 | 1 | 1433-3058 |
Citations | PageRank | References |
3 | 0.42 | 24 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peidai Xie | 1 | 11 | 4.97 |
| Xinwang Liu | 2 | 2355 | 140.38 |
| Jianping Yin | 3 | 978 | 89.94 |
| Yongjun Wang | 4 | 27 | 9.19 |