Modeling and Reducing the Attack Surface in Software Systems

被引:2
作者
Yee, George O. M. [1 ]
机构
[1] Carleton Univ, Dept Syst & Comp Engn, Comp Res Lab, Aptusinnova Inc, Ottawa, ON, Canada
来源
2019 IEEE/ACM 11TH INTERNATIONAL WORKSHOP ON MODELLING IN SOFTWARE ENGINEERING (MISE 2019) | 2019年
关键词
software; system; sensitive; data; location; attack surface; reduction;
D O I
10.1109/MiSE.2019.00016
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
In today's world, software is ubiquitous and relied upon to perform many important and critical functions. Unfortunately, software is riddled with security vulnerabilities that invite exploitation. Attackers are particularly attracted to software systems that hold sensitive data with the goal of compromising the data. For such systems, this paper proposes a modeling method applied at design time to identify and reduce the attack surface, which arises due to the locations containing sensitive data within the software system and the accessibility of those locations to attackers. The method reduces the attack surface by changing the design so that the number of such locations is reduced. The method performs these changes on a graphical model of the software system. The changes are then considered for application to the design of the actual system to improve its security.
引用
收藏
页码:55 / 62
页数:8
相关论文
共 25 条
[11]   VulPecker: An Automated Vulnerability Detection System Based on Code Similarity Analysis [J].
Li, Zhen ;
Zou, Deqing ;
Xu, Shouhuai ;
Jin, Hai ;
Qi, Hanchao ;
Hu, Jie .
32ND ANNUAL COMPUTER SECURITY APPLICATIONS CONFERENCE (ACSAC 2016), 2016, :201-213
[12]   An Attack Surface Metric [J].
Manadhata, Pratyusa K. ;
Wing, Jeannette M. .
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 2011, 37 (03) :371-386
[13]  
Matulevicius R, 2017, FUNDAMENTALS SECURE, P63
[14]  
Nanthaamornphong A., 2013, P 1 INT WORKSH SOFTW, P9
[15]   Reducing the Attack Surface [J].
Neville-Neil, George V. .
COMMUNICATIONS OF THE ACM, 2018, 61 (02) :27-28
[16]  
Pang Y., 2017, Proceedings of the 2017 International Conference on Deep Learning Technologies - ICDLT '17, P6
[17]   VCCFinder: Finding Potential Vulnerabilities in Open-Source Projects to Assist Code Audits [J].
Perl, Henning ;
Dechand, Sergej ;
Smith, Matthew ;
Arp, Daniel ;
Yamaguchi, Fabian ;
Rieck, Konrad ;
Fahl, Sascha ;
Acar, Yasemin .
CCS'15: PROCEEDINGS OF THE 22ND ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, 2015, :426-437
[18]  
Salter C, 1999, NEW SECURITY PARADIGMS WOEKSHOP, PROCEEDINGS, P2
[19]  
Sherman M, 2014, P 2 INT WORKSHOP SOF, P5
[20]  
Sindre G, 2007, LECT NOTES COMPUT SC, V4542, P355