Noisy Label Learning for Security Defects

03/09/2022
by   Roland Croft, et al.
0

Data-driven software engineering processes, such as vulnerability prediction heavily rely on the quality of the data used. In this paper, we observe that it is infeasible to obtain a noise-free security defect dataset in practice. Despite the vulnerable class, the non-vulnerable modules are difficult to be verified and determined as truly exploit free given the limited manual efforts available. It results in uncertainty, introduces labeling noise in the datasets and affects conclusion validity. To address this issue, we propose novel learning methods that are robust to label impurities and can leverage the most from limited label data; noisy label learning. We investigate various noisy label learning methods applied to software vulnerability prediction. Specifically, we propose a two-stage learning method based on noise cleaning to identify and remediate the noisy samples, which improves AUC and recall of baselines by up to 8.9 hurdles in terms of achieving a performance upper bound with semi-omniscient knowledge of the label noise. Overall, the experimental results show that learning from noisy labels can be effective for data-driven software and security analytics.

READ FULL TEXT

page 4

page 8

research
03/06/2021

LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment

Deep neural network models are robust to a limited amount of label noise...
research
06/27/2022

Towards Harnessing Feature Embedding for Robust Learning with Noisy Labels

The memorization effect of deep neural networks (DNNs) plays a pivotal r...
research
12/28/2022

Learning to Detect Noisy Labels Using Model-Based Features

Label noise is ubiquitous in various machine learning scenarios such as ...
research
02/13/2019

Vulnerability Prediction Based on Weighted Software Network for Secure Software Building

To build a secure communications software, Vulnerability Prediction Mode...
research
11/02/2017

Deep Learning from Noisy Image Labels with Quality Embedding

There is an emerging trend to leverage noisy image datasets in many visu...
research
04/27/2022

Robust Face Anti-Spoofing with Dual Probabilistic Modeling

The field of face anti-spoofing (FAS) has witnessed great progress with ...
research
02/15/2021

Expected Exploitability: Predicting the Development of Functional Vulnerability Exploits

Assessing the exploitability of software vulnerabilities at the time of ...

Please sign up or login with your details

Forgot password? Click here to reset