To search, Click below search items. |
| All Published Papers Search Service |
|
|
|
Title |
Machine Learning Enhanced Access Control for Big Data |
|
Author |
Hamza ES-SAMAALI , Anas Abou El Kalam, Aissam Outchakoucht, Siham Benhadou |
| Citation |
Vol. 20 No. 3 pp. 83-91 |
|
Abstract |
Access Controls (AC) are one of the main means of defense in IT systems, unfortunately, Big Data Systems are still lacking in this field, the current well-known ACs are vulnerable and can be compromised because of policy misconfiguration and lack of contextuality. In this article we propose a Machine Learning approach to optimize ABAC (Attribute Based Access Control) with the aim to reduce the attacks that are overlooked by the hardcoded policies (i.e: users abusing their privileges). We use unsupervised learning outlier detection algorithms to detect anomalous user behaviors. The Framework was implemented in Python and its performance tested using the UNSW-NB15 Data Set. |
|
Keywords |
Access Control Big Data Machine Learning Outlier Detection ABAC Security |
|
URL |
