Dr. Xiaojuan Zhang Profile

Dr. Xiaojuan Zhang

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 4 March 2024 Paper

A novel example of fuzzing testing based on deep neural network

Yayun Zhu, Baiji Hu, Baiyi Wang, Ziqing Lin, Jingyi Cao, Xiaojuan Zhang, Lin Jiang, Haixiang Wang

Proceedings Volume 12981, 1298168 (2024) https://doi.org/10.1117/12.3014979

Read Abstract +

In recent years, the secure operation of power systems has grasped the focus of researchers' attention, and fuzzing test-based vulnerability mining has become a commonly used tool for evaluating its stability and robustness in the face of various anomalies. In fuzzing testing, test case generation is concerned with the ultimate performance of vulnerability mining, however, existing techniques generally focus only on coverage and not on the vulnerable parts. Therefore, based LSTM arrays that combined with residual connections, we propose a novel approach to learn the vulnerable parts of the power system to guide the process of fuzzing by utilizing the powerful feature representation capability of neural networks. Experiments show that the improved method can achieve faster convergence and higher vulnerability detection rate of neural networks, while the FNR of which is 20.4% and TPR is 79.6%.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years