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Found 7 papers, 1 papers with code
Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer Networks
In LF, we randomly flipped the labels of benign data and trained the model on the manipulated data.
UNBUS: Uncertainty-aware Deep Botnet Detection System in Presence of Perturbed Samples
Then, by examining the methods for computing the uncertainty, the defense method is proposed to increase the accuracy to about 70%.
SETTI: A Self-supervised Adversarial Malware Detection Architecture in an IoT Environment
In the SETTI architecture, we design three self-supervised attack techniques, namely Self-MDS, GSelf-MDS and ASelf-MDS.
Deep Image: A precious image based deep learning method for online malware detection in IoT Environment
In this way, a criterion is introduced that is used together with accuracy and FPR criteria for malware analysis in IoT environment.
On Defending Against Label Flipping Attacks on Malware Detection Systems
Our evaluation shows that using random forest feature selection and varying ratios of features can result in an improvement of up to 19\% accuracy when compared with the state-of-the-art method in the literature.
Similarity-based Android Malware Detection Using Hamming Distance of Static Binary Features
We test our experiments in a different type of features: API, intent, and permission features on these three datasets.
Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach
We also test our methods using various classifier algorithms and compare them with the state-of-the-art data poisoning method using the Jacobian matrix.
