Vulnerability Discovery Modelling for Software with Multi-versions

2016 ◽  
pp. 255-265 ◽  
Author(s):  
Adarsh Anand ◽  
Subhrata Das ◽  
Deepti Aggrawal ◽  
Yury Klochkov
Keyword(s):  
Vulnerability Discovery

Deep neural-based vulnerability discovery demystified: data, model and performance

2021 ◽  
Author(s):  
Guanjun Lin ◽  
Wei Xiao ◽  
Leo Yu Zhang ◽  
Shang Gao ◽  
Yonghang Tai ◽  
...  
Keyword(s):  
Data Model ◽  
Vulnerability Discovery ◽  
And Performance

scholarly journals Bin2vec: learning representations of binary executable programs for security tasks

Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Shushan Arakelyan ◽  
Sima Arasteh ◽  
Christophe Hauser ◽  
Erik Kline ◽  
Aram Galstyan
Keyword(s):  
Program Analysis ◽  
State Of The Art ◽  
Classification Error ◽  
New Approach ◽  
Convolutional Networks ◽  
Computational Program ◽  
Functional Algorithm ◽  
Binary Program ◽  
Vulnerability Discovery ◽  
Executable Programs

AbstractTackling binary program analysis problems has traditionally implied manually defining rules and heuristics, a tedious and time consuming task for human analysts. In order to improve automation and scalability, we propose an alternative direction based on distributed representations of binary programs with applicability to a number of downstream tasks. We introduce Bin2vec, a new approach leveraging Graph Convolutional Networks (GCN) along with computational program graphs in order to learn a high dimensional representation of binary executable programs. We demonstrate the versatility of this approach by using our representations to solve two semantically different binary analysis tasks – functional algorithm classification and vulnerability discovery. We compare the proposed approach to our own strong baseline as well as published results, and demonstrate improvement over state-of-the-art methods for both tasks. We evaluated Bin2vec on 49191 binaries for the functional algorithm classification task, and on 30 different CWE-IDs including at least 100 CVE entries each for the vulnerability discovery task. We set a new state-of-the-art result by reducing the classification error by 40% compared to the source-code based inst2vec approach, while working on binary code. For almost every vulnerability class in our dataset, our prediction accuracy is over 80% (and over 90% in multiple classes).

Vulnerability discovery modelling: a general framework

2021 ◽  
Vol 16 (1/2) ◽  
pp. 192
Author(s):  
Omar H. Alhazmi ◽  
Adarsh Anand ◽  
Navneet Bhatt
Keyword(s):  
General Framework ◽  
Vulnerability Discovery

Vulnerability Discovery and Patch Modeling

2019 ◽  
pp. 401-419 ◽  
Author(s):  
Avinash K. Shrivastava ◽  
P. K. Kapur ◽  
Misbah Anjum
Keyword(s):  
Vulnerability Discovery
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