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Found 2 papers, 1 papers with code
Edge-Detect: Edge-centric Network Intrusion Detection using Deep Neural Network
Our results demonstrate that in comparison to conventional DLM techniques, our model maintains a high testing accuracy of 99% even with lower resource utilization in terms of cpu and memory.
Ranked #1 on
Network Intrusion Detection
on UNSW-NB15
Black-box Adversarial Attacks in Autonomous Vehicle Technology
Also, the issue of late convergence in a Simple black-box attack (SimBA) is addressed by minimizing the loss of the most confused class which is the incorrect class predicted by the model with the highest probability, instead of trying to maximize the loss of the correct class.
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