Cost-Sensitive Access Control for Detecting Remote to Local (R2L) and User to Root (U2R) Attacks
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2017 by IJCTT Journal|
|Year of Publication : 2017|
|Authors : Doaa Hassan|
|DOI : 10.14445/22312803/IJCTT-V43P118|
Doaa Hassan "Cost-Sensitive Access Control for Detecting Remote to Local (R2L) and User to Root (U2R) Attacks". International Journal of Computer Trends and Technology (IJCTT) V43(2):124-129, January 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Remote to local attack (r2l) has been widely known to be launched by an attacker to gain unauthorized access to a victim machine in the entire network. Similarly user to root attack (u2r) is usually launched for illegally obtaining the root’s privileges when legally accessing a local machine. One approach for detecting both attacks is to formulate both problems as a binary classification problem by deciding whether to accept or reject access requests from remote sites to local user machine or by accepting or rejecting access as root attempts. However, the cost caused by incorrect decision due to accepting illegitimate access request in a form of the damage that it might lead to is more expensive than the opposite case resulting from rejecting a valid access request. Due to this, in this paper we handle both problems in cost sensitive learning framework. We investigate how various cost-sensitive machine learning methods can be used to produce various cost sensitive detection models for detecting illegitimate remote access and access as a root requests. Those models are optimized for a user-defined cost matrix. Empirical experiment shows that the produced cost sensitive detection models are effective in reducing the overall cost of illegal remote access and access as root detection.
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cost sensitive learning methods, r2L attack, u2r attack.