| Title | Evaluating Model Robustness to Adversarial Samples in Network Intrusion Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Schneider, Madeleine, Aspinall, David, Bastian, Nathaniel D. |
| Conference Name | 2021 IEEE International Conference on Big Data (Big Data) |
| Date Published | dec |
| Keywords | adversarial artificial intelligence, Big Data, big data security metrics, Conferences, feature extraction, machine learning, Measurement, model robustness evaluation, network intrusion detection, Network security, Perturbation methods, pubcrawl, resilience, Resiliency, Scalability, Training |
| Abstract | Adversarial machine learning, a technique which seeks to deceive machine learning (ML) models, threatens the utility and reliability of ML systems. This is particularly relevant in critical ML implementations such as those found in Network Intrusion Detection Systems (NIDS). This paper considers the impact of adversarial influence on NIDS and proposes ways to improve ML based systems. Specifically, we consider five feature robustness metrics to determine which features in a model are most vulnerable, and four defense methods. These methods are tested on six ML models with four adversarial sample generation techniques. Our results show that across different models and adversarial generation techniques, there is limited consistency in vulnerable features or in effectiveness of defense method. |
| DOI | 10.1109/BigData52589.2021.9671580 |
| Citation Key | schneider_evaluating_2021 |
Evaluating Model Robustness to Adversarial Samples in Network Intrusion Detection