IEICE TRANSACTIONS on Information
Research on Software Trust Analysis Based on Behavior
Summary :
In this paper, we propose a new trusted modeling approach based on state graphs. We introduce a novel method of deriving state-layer from a system call sequence in terms of probability and statistics theory, and we identify the state sequence with the help of Hidden Markov Model (HMM). We generate state transition graph according to software executing process and pruning rules. Then, we separate local function graphs according to software specific functions by semantic analysis. The state-layer is a bridge between the basic behaviors and the upper layer functions of software to compensate semantic faults. In addition, a pruning strategy of formulating state graphs is designed to precisely describe each piece of software functions. Finally, a detecting system based on our model is proposed, and a case study of RSS software reveals how our system works. The results demonstrate that our trusted model describes software behaviors successfully and can well detect un-trust behaviors, anomaly behaviors, and illegal input behaviors.
- Publication
- IEICE TRANSACTIONS on Information Vol.E97-D No.3 pp.488-496
- Publication Date
- 2014/03/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E97.D.488
- Type of Manuscript
- PAPER
- Category
- Software Engineering
Authors
Yingxu LAI
Beijing University of Technology
Wenwen ZHANG
Beijing University of Technology
Zhen YANG
Beijing University of Technology
Keyword
Latest Issue
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Yingxu LAI, Wenwen ZHANG, Zhen YANG, "Research on Software Trust Analysis Based on Behavior" in IEICE TRANSACTIONS on Information,
vol. E97-D, no. 3, pp. 488-496, March 2014, doi: 10.1587/transinf.E97.D.488.
Abstract: In this paper, we propose a new trusted modeling approach based on state graphs. We introduce a novel method of deriving state-layer from a system call sequence in terms of probability and statistics theory, and we identify the state sequence with the help of Hidden Markov Model (HMM). We generate state transition graph according to software executing process and pruning rules. Then, we separate local function graphs according to software specific functions by semantic analysis. The state-layer is a bridge between the basic behaviors and the upper layer functions of software to compensate semantic faults. In addition, a pruning strategy of formulating state graphs is designed to precisely describe each piece of software functions. Finally, a detecting system based on our model is proposed, and a case study of RSS software reveals how our system works. The results demonstrate that our trusted model describes software behaviors successfully and can well detect un-trust behaviors, anomaly behaviors, and illegal input behaviors.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E97.D.488/_p
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@ARTICLE{e97-d_3_488,
author={Yingxu LAI, Wenwen ZHANG, Zhen YANG, },
journal={IEICE TRANSACTIONS on Information},
title={Research on Software Trust Analysis Based on Behavior},
year={2014},
volume={E97-D},
number={3},
pages={488-496},
abstract={In this paper, we propose a new trusted modeling approach based on state graphs. We introduce a novel method of deriving state-layer from a system call sequence in terms of probability and statistics theory, and we identify the state sequence with the help of Hidden Markov Model (HMM). We generate state transition graph according to software executing process and pruning rules. Then, we separate local function graphs according to software specific functions by semantic analysis. The state-layer is a bridge between the basic behaviors and the upper layer functions of software to compensate semantic faults. In addition, a pruning strategy of formulating state graphs is designed to precisely describe each piece of software functions. Finally, a detecting system based on our model is proposed, and a case study of RSS software reveals how our system works. The results demonstrate that our trusted model describes software behaviors successfully and can well detect un-trust behaviors, anomaly behaviors, and illegal input behaviors.},
keywords={},
doi={10.1587/transinf.E97.D.488},
ISSN={1745-1361},
month={March},}
Copy
TY - JOUR
TI - Research on Software Trust Analysis Based on Behavior
T2 - IEICE TRANSACTIONS on Information
SP - 488
EP - 496
AU - Yingxu LAI
AU - Wenwen ZHANG
AU - Zhen YANG
PY - 2014
DO - 10.1587/transinf.E97.D.488
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2014
AB - In this paper, we propose a new trusted modeling approach based on state graphs. We introduce a novel method of deriving state-layer from a system call sequence in terms of probability and statistics theory, and we identify the state sequence with the help of Hidden Markov Model (HMM). We generate state transition graph according to software executing process and pruning rules. Then, we separate local function graphs according to software specific functions by semantic analysis. The state-layer is a bridge between the basic behaviors and the upper layer functions of software to compensate semantic faults. In addition, a pruning strategy of formulating state graphs is designed to precisely describe each piece of software functions. Finally, a detecting system based on our model is proposed, and a case study of RSS software reveals how our system works. The results demonstrate that our trusted model describes software behaviors successfully and can well detect un-trust behaviors, anomaly behaviors, and illegal input behaviors.
ER -
English
