| Title | Influence Based Defense Against Data Poisoning Attacks in Online Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Seetharaman, Sanjay, Malaviya, Shubham, Vasu, Rosni, Shukla, Manish, Lodha, Sachin |
| Conference Name | 2022 14th International Conference on COMmunication Systems & NETworkS (COMSNETS) |
| Date Published | jan |
| Keywords | Adversarial Machine Learning, composability, compositionality, data integrity, Data models, data poisoning, Data Sanitization, Degradation, Filtering, Influence Function, Linear programming, machine learning, online learning, pubcrawl, resilience, Resiliency, Training data |
| Abstract | Data poisoning is a type of adversarial attack on training data where an attacker manipulates a fraction of data to degrade the performance of machine learning model. There are several known defensive mechanisms for handling offline attacks, however defensive measures for online learning, where data points arrive sequentially, have not garnered similar interest. In this work, we propose a defense mechanism to minimize the degradation caused by the poisoned training data on a learner's model in an online setup. Our proposed method utilizes an influence function which is a classic technique in robust statistics. Further, we supplement it with the existing data sanitization methods for filtering out some of the poisoned data points. We study the effectiveness of our defense mechanism on multiple datasets and across multiple attack strategies against an online learner. |
| Notes | ISSN: 2155-2509 |
| DOI | 10.1109/COMSNETS53615.2022.9668557 |
| Citation Key | seetharaman_influence_2022 |
Influence Based Defense Against Data Poisoning Attacks in Online Learning