Measuring and Enhancing Prediction Capabilities of Vulnerability Discovery Models for Apache and IIS HTTP Servers

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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