7,235 research outputs found
Multitask Learning for Network Traffic Classification
Traffic classification has various applications in today's Internet, from
resource allocation, billing and QoS purposes in ISPs to firewall and malware
detection in clients. Classical machine learning algorithms and deep learning
models have been widely used to solve the traffic classification task. However,
training such models requires a large amount of labeled data. Labeling data is
often the most difficult and time-consuming process in building a classifier.
To solve this challenge, we reformulate the traffic classification into a
multi-task learning framework where bandwidth requirement and duration of a
flow are predicted along with the traffic class. The motivation of this
approach is twofold: First, bandwidth requirement and duration are useful in
many applications, including routing, resource allocation, and QoS
provisioning. Second, these two values can be obtained from each flow easily
without the need for human labeling or capturing flows in a controlled and
isolated environment. We show that with a large amount of easily obtainable
data samples for bandwidth and duration prediction tasks, and only a few data
samples for the traffic classification task, one can achieve high accuracy. We
conduct two experiment with ISCX and QUIC public datasets and show the efficacy
of our approach
On Lightweight Privacy-Preserving Collaborative Learning for IoT Objects
The Internet of Things (IoT) will be a main data generation infrastructure
for achieving better system intelligence. This paper considers the design and
implementation of a practical privacy-preserving collaborative learning scheme,
in which a curious learning coordinator trains a better machine learning model
based on the data samples contributed by a number of IoT objects, while the
confidentiality of the raw forms of the training data is protected against the
coordinator. Existing distributed machine learning and data encryption
approaches incur significant computation and communication overhead, rendering
them ill-suited for resource-constrained IoT objects. We study an approach that
applies independent Gaussian random projection at each IoT object to obfuscate
data and trains a deep neural network at the coordinator based on the projected
data from the IoT objects. This approach introduces light computation overhead
to the IoT objects and moves most workload to the coordinator that can have
sufficient computing resources. Although the independent projections performed
by the IoT objects address the potential collusion between the curious
coordinator and some compromised IoT objects, they significantly increase the
complexity of the projected data. In this paper, we leverage the superior
learning capability of deep learning in capturing sophisticated patterns to
maintain good learning performance. Extensive comparative evaluation shows that
this approach outperforms other lightweight approaches that apply additive
noisification for differential privacy and/or support vector machines for
learning in the applications with light data pattern complexities.Comment: 12 pages,IOTDI 201
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