On defending against label flipping attacks on malware detection systems

@article{Taheri2020OnDA,
  title={On defending against label flipping attacks on malware detection systems},
  author={R. Taheri and R. Javidan and M. Shojafar and Zahra Pooranian and A. Miri and M. Conti},
  journal={Neural Computing and Applications},
  year={2020},
  pages={1-20}
}
Label manipulation attacks are a subclass of data poisoning attacks in adversarial machine learning used against different applications, such as malware detection. These types of attacks represent a serious threat to detection systems in environments having high noise rate or uncertainty, such as complex networks and Internet of Thing (IoT). Recent work in the literature has suggested using the K -nearest neighboring algorithm to defend against such attacks. However, such an approach can suffer… Expand
Label flipping attacks against Naive Bayes on spam filtering systems
Adversarial Label-Flipping Attack and Defense for Graph Neural Networks
Clustering-Aided Multi-View Classification: A Case Study on Android Malware Detection
...
1
2
...

References

SHOWING 1-10 OF 43 REFERENCES
Can machine learning model with static features be fooled: an adversarial machine learning approach
Label Sanitization against Label Flipping Poisoning Attacks
Adversarial Feature Selection Against Evasion Attacks
Support vector machines under adversarial label contamination
Is Feature Selection Secure against Training Data Poisoning?
Data Poisoning Attacks against Online Learning
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks
Detecting Poisoning Attacks on Machine Learning in IoT Environments
...
1
2
3
4
5
...