19,719 research outputs found
ZipNet-GAN: Inferring Fine-grained Mobile Traffic Patterns via a Generative Adversarial Neural Network
Large-scale mobile traffic analytics is becoming essential to digital
infrastructure provisioning, public transportation, events planning, and other
domains. Monitoring city-wide mobile traffic is however a complex and costly
process that relies on dedicated probes. Some of these probes have limited
precision or coverage, others gather tens of gigabytes of logs daily, which
independently offer limited insights. Extracting fine-grained patterns involves
expensive spatial aggregation of measurements, storage, and post-processing. In
this paper, we propose a mobile traffic super-resolution technique that
overcomes these problems by inferring narrowly localised traffic consumption
from coarse measurements. We draw inspiration from image processing and design
a deep-learning architecture tailored to mobile networking, which combines
Zipper Network (ZipNet) and Generative Adversarial neural Network (GAN) models.
This enables to uniquely capture spatio-temporal relations between traffic
volume snapshots routinely monitored over broad coverage areas
(`low-resolution') and the corresponding consumption at 0.05 km level
(`high-resolution') usually obtained after intensive computation. Experiments
we conduct with a real-world data set demonstrate that the proposed
ZipNet(-GAN) infers traffic consumption with remarkable accuracy and up to
100 higher granularity as compared to standard probing, while
outperforming existing data interpolation techniques. To our knowledge, this is
the first time super-resolution concepts are applied to large-scale mobile
traffic analysis and our solution is the first to infer fine-grained urban
traffic patterns from coarse aggregates.Comment: To appear ACM CoNEXT 201
Spatial inference of traffic transition using micro-macro traffic variables
This paper proposes an online traffic inference algorithm for road segments in which local traffic information cannot be directly observed. Using macro-micro traffic variables as inputs, the algorithm consists of three main operations. First, it uses interarrival time (time headway) statistics from upstream and downstream locations to spatially infer traffic transitions at an unsupervised piece of segment. Second, it estimates lane-level flow and occupancy at the same unsupervised target site. Third, it estimates individual lane-level shockwave propagation times on the segment. Using real-world closed-circuit television data, it is shown that the proposed algorithm outperforms previously proposed methods in the literature
Using Markov Models and Statistics to Learn, Extract, Fuse, and Detect Patterns in Raw Data
Many systems are partially stochastic in nature. We have derived data driven
approaches for extracting stochastic state machines (Markov models) directly
from observed data. This chapter provides an overview of our approach with
numerous practical applications. We have used this approach for inferring
shipping patterns, exploiting computer system side-channel information, and
detecting botnet activities. For contrast, we include a related data-driven
statistical inferencing approach that detects and localizes radiation sources.Comment: Accepted by 2017 International Symposium on Sensor Networks, Systems
and Securit
Data Leak Detection As a Service: Challenges and Solutions
We describe a network-based data-leak detection (DLD)
technique, the main feature of which is that the detection
does not require the data owner to reveal the content of the
sensitive data. Instead, only a small amount of specialized
digests are needed. Our technique – referred to as the fuzzy
fingerprint – can be used to detect accidental data leaks due
to human errors or application flaws. The privacy-preserving
feature of our algorithms minimizes the exposure of sensitive
data and enables the data owner to safely delegate the
detection to others.We describe how cloud providers can offer
their customers data-leak detection as an add-on service
with strong privacy guarantees.
We perform extensive experimental evaluation on the privacy,
efficiency, accuracy and noise tolerance of our techniques.
Our evaluation results under various data-leak scenarios
and setups show that our method can support accurate
detection with very small number of false alarms, even
when the presentation of the data has been transformed. It
also indicates that the detection accuracy does not degrade
when partial digests are used. We further provide a quantifiable
method to measure the privacy guarantee offered by our
fuzzy fingerprint framework
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