Name
Title | ||
|---|---|---|
Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues |
Abstract | ||
|---|---|---|
The massive growth of data that are transmitted through a variety of devices and communication protocols have raised serious security concerns, which have increased the importance of developing advanced intrusion detection systems (IDSs). Deep learning is an advanced branch of machine learning, composed of multiple layers of neurons that represent the learning process. Deep learning can cope with large-scale data and has shown success in different fields. Therefore, researchers have paid more attention to investigating deep learning for intrusion detection. This survey comprehensively reviews and compares the key previous deep learning-focused cybersecurity surveys. Through an extensive review, this survey provides a novel fine-grained taxonomy that categorizes the current state-of-the-art deep learning-based IDSs with respect to different facets, including input data, detection, deployment, and evaluation strategies. Each facet is further classified according to different criteria. This survey also compares and discusses the related experimental solutions proposed as deep learning-based IDSs. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.knosys.2019.105124 | Knowledge-Based Systems |
Keywords | Field | DocType |
Intrusion detection,Anomaly detection,Deep learning | Open research,Software deployment,Computer science,Artificial intelligence,Deep learning,Intrusion detection system,Machine learning,Communications protocol | Journal |
Volume | ISSN | Citations |
189 | 0950-7051 | 15 |
PageRank | References | Authors |
0.64 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arwa Aldweesh | 1 | 15 | 0.64 |
| Abdelouahid Derhab | 2 | 277 | 32.68 |
| Ahmed Z. Emam | 3 | 29 | 4.10 |