56 research outputs found
Data Driven Prediction Architecture for Autonomous Driving and its Application on Apollo Platform
Autonomous Driving vehicles (ADV) are on road with large scales. For safe and
efficient operations, ADVs must be able to predict the future states and
iterative with road entities in complex, real-world driving scenarios. How to
migrate a well-trained prediction model from one geo-fenced area to another is
essential in scaling the ADV operation and is difficult most of the time since
the terrains, traffic rules, entities distributions, driving/walking patterns
would be largely different in different geo-fenced operation areas. In this
paper, we introduce a highly automated learning-based prediction model
pipeline, which has been deployed on Baidu Apollo self-driving platform, to
support different prediction learning sub-modules' data annotation, feature
extraction, model training/tuning and deployment. This pipeline is completely
automatic without any human intervention and shows an up to 400\% efficiency
increase in parameter tuning, when deployed at scale in different scenarios
across nations.Comment: Accepted by the 31st IEEE Intelligent Vehicles Symposium (2020
Deep Learning Based Malware Classification Using Deep Residual Network
The traditional malware detection approaches rely heavily on feature extraction procedure, in this paper we proposed a deep learning-based malware classification model by using a 18-layers deep residual network. Our model uses the raw bytecodes data of malware samples, converting the bytecodes to 3-channel RGB images and then applying the deep learning techniques to classify the malwares. Our experiment results show that the deep residual network model achieved an average accuracy of 86.54% by 5-fold cross validation. Comparing to the traditional methods for malware classification, our deep residual network model greatly simplify the malware detection and classification procedures, it achieved a very good classification accuracy as well. The dataset we used in this paper for training and testing is Malimg dataset, one of the biggest malware datasets released by vision research lab of UCSB
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