Bin Liang

Work place: School of information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China)

E-mail: liangb@ruc.edu.cn

Website:

Research Interests: Computer systems and computational processes, Computer Architecture and Organization, Information Security, Data Structures and Algorithms, Information-Theoretic Security

Biography

Bin Liang was born in P.R. China in 1973, earned B.S. degree in the field of computational
mathematic and application software, earned Ph.D. in the field of computer science in 2004
from Institute of Software, Chinese Academy of Science (ISCAS). He is now an ASSOCIATE PROFESSOR at the school of information, Renmin University of China (RUC). Before jo ning RUC in 2006, he did postdoctoral research in the department of computer science at Tsinghua
University, aim at host security and software security analysis. His current research interests include information security and static analysis. 

Author Articles
A System Call Randomization Based Method for Countering Code-Injection Attacks

By Zhaohui Liang Bin Liang Lupin Li

DOI: https://doi.org/10.5815/ijitcs.2009.01.01, Pub. Date: 8 Oct. 2009

Code-injection attacks pose serious threat to today’s Internet. The existing code-injection attack defense methods have some deficiencies on performance overhead and effectiveness. To this end, we propose a method that uses system called randomization to counter code injection attacks based on instruction set randomization idea. System calls must be used when an injected code would perform its actions. By creating randomized system calls of the target process, an attacker who does not know the key to the randomization algorithm will inject code that isn’t randomized like as the target process and is invalid for the corresponding de-randomized module. The injected code would fail to execute without calling system calls correctly. Moreover, with extended complier, our method creates source code randomization during its compiling and implements binary executable files randomization by feature matching. Our experiments on built prototype show that our method can effectively counter variety code injection attacks with low-overhead.

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