One Technique is Not Enough: A Comparison of Vulnerability Discovery Techniques

Change Point Problem in Security Vulnerability Discovery Model

2019 International Conference on Software Security and Assurance (ICSSA) ◽

10.1109/icssa48308.2019.00010 ◽

2019 ◽

Author(s):

Ankur Shukla ◽

Basel Katt

Keyword(s):

Change Point ◽

Point Problem ◽

Security Vulnerability ◽

Change Point Problem ◽

Vulnerability Discovery

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

Neural Computing and Applications ◽

10.1007/s00521-021-05954-3 ◽

2021 ◽

Author(s):

Guanjun Lin ◽

Wei Xiao ◽

Leo Yu Zhang ◽

Shang Gao ◽

Yonghang Tai ◽

...

Keyword(s):

Data Model ◽

Vulnerability Discovery ◽

And Performance

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Bin2vec: learning representations of binary executable programs for security tasks

Cybersecurity ◽

10.1186/s42400-021-00088-4 ◽

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).

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