Name
Title | ||
|---|---|---|
Evaluation Of Hybrid Deep Learning Techniques For Ensuring Security In Networked Control Systems |
Abstract | ||
|---|---|---|
With the rapid application of the network based communication in industries, the security related problems appear to be inevitable for automation networks. The integration of internet into the automation plant benefited companies and engineers a lot and on the other side paved ways to number of threats. An attack on such control critical infrastructure may endangers people's health and safety, damage industrial facilities and produce financial loss. One of the approach to secure the network in automation is the development of an efficient Network based Intrusion Detection System (NIDS). Despite several techniques available for intrusion detection, they still lag in identifying the possible attacks or novel attacks on network efficiently. In this paper, we evaluate the performance of detection mechanism by combining the deep learning techniques with the machine learning techniques for the development of Intrusion Detection System (IDS). The performance metrics such as precession, recall and F-Measure were measured. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 22ND IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA) | Intrusion Detection System (IDS), Machine Learning, Deep Learning, NSL-KDD, Network Security, Automation |
Field | DocType | ISSN |
Computer security,Support vector machine,Critical infrastructure,Real-time computing,Feature extraction,Automation,Artificial intelligence,Engineering,Deep learning,Control system,Intrusion detection system,The Internet | Conference | 1946-0740 |
Citations | PageRank | References |
1 | 0.38 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sasanka Potluri | 1 | 6 | 1.59 |
| Navin Francis Henry | 2 | 1 | 0.38 |
| Christian Diedrich | 3 | 76 | 17.15 |