Dr. Sridhar Venkatesan Profile

Dr. Sridhar Venkatesan

at Peraton Labs

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 | 30 May 2022 Presentation + Paper

Poisoning attacks on machine learning models in cyber systems and mitigation strategies

Rauf Izmailov, Sridhar Venkatesan, Achyut Reddy, Ritu Chadha, Michael De Lucia, Alina Oprea

Proceedings Volume 12117, 1211702 (2022) https://doi.org/10.1117/12.2622112

KEYWORDS: Data modeling, Statistical modeling, Sensors, Systems modeling, Machine learning, Performance modeling, Computer networks, Computer intrusion detection, Data analysis, Data corrections

Read Abstract +

Poisoning attacks on training data are becoming one of the top concerns among users of machine learning systems. The goal of such attacks is to inject a small set of maliciously mislabeled training data into the training pipeline so as to detrimentally impact a machine learning model trained on such data. Constructing such attacks for cyber applications is especially challenging due to their realizability constraints. Furthermore, poisoning mitigation techniques for such applications are also not well understood. This paper investigates techniques for realizable data poisoning availability attacks (using several cyber applications), in which an attacker can insert a set of poisoned samples at the training time with the goal of degrading the accuracy of the deployed model. We design a white-box, realizable poisoning attack that degraded the original model’s accuracy by generating mislabeled samples in close vicinity of a selected subset of training points. We investigate this strategy and its modifications for key classifier architectures and provide specific implications for each of them. The paper also proposes a novel data cleaning method as a defense against such poisoning attacks. Our defense includes a diversified ensemble of classifiers, each trained on a different subset of the training set. We use the disagreement of the classifiers’ predictions as a decision whether to keep a given sample in the training dataset or remove it. The results demonstrate the efficiency of this strategy with very limited performance penalty.

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