1,686 research outputs found
An investigation of a deep learning based malware detection system
We investigate a Deep Learning based system for malware detection. In the
investigation, we experiment with different combination of Deep Learning
architectures including Auto-Encoders, and Deep Neural Networks with varying
layers over Malicia malware dataset on which earlier studies have obtained an
accuracy of (98%) with an acceptable False Positive Rates (1.07%). But these
results were done using extensive man-made custom domain features and investing
corresponding feature engineering and design efforts. In our proposed approach,
besides improving the previous best results (99.21% accuracy and a False
Positive Rate of 0.19%) indicates that Deep Learning based systems could
deliver an effective defense against malware. Since it is good in automatically
extracting higher conceptual features from the data, Deep Learning based
systems could provide an effective, general and scalable mechanism for
detection of existing and unknown malware.Comment: 13 Pages, 4 figure
The Neural Testbed: Evaluating Joint Predictions
Predictive distributions quantify uncertainties ignored by point estimates.
This paper introduces The Neural Testbed: an open-source benchmark for
controlled and principled evaluation of agents that generate such predictions.
Crucially, the testbed assesses agents not only on the quality of their
marginal predictions per input, but also on their joint predictions across many
inputs. We evaluate a range of agents using a simple neural network data
generating process. Our results indicate that some popular Bayesian deep
learning agents do not fare well with joint predictions, even when they can
produce accurate marginal predictions. We also show that the quality of joint
predictions drives performance in downstream decision tasks. We find these
results are robust across choice a wide range of generative models, and
highlight the practical importance of joint predictions to the community
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