Our paper entitled “Deep Learning Analytics for IoT Security over a Configurable Big Data Platform” has been presented at the 22nd International Symposium on Wireless Personal Multimedia Communications organized by IEEE in Lisbon, Portugal (November 2019). This work has been carried out by Athens Information Technology in the scope of the H2020 SecureIoT project, which is funded by the European Commission in the scope of its H2020 programme (contract number 779899).

1. Deep Learning Analytics
for IoT Security over a
Configurable Big Data
Platform
Speaker: Angela-Maria Despotopoulou
Research Scientist
Athens Information Technology, Greece

2. Paper Authors
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Stefanos Astaras
sast@ait.gr
Sofoklis Efremidis
sefr@ait.gr
Angela-Maria
Despotopoulou
ades@ait.gr
Nikos Kefalakis
Nikos.KEFALAKIS@intrasoft
-intl.com
John Soldatos
jsol@ait.gr

3. Presentation Take-Aways
Presentation
Contents
an architectural
framework that
provides a data driven
security monitoring
mechanism for all
layers of an IoT system
deep learning
algorithms tested over a
practical implementation
of the framework, called
Variational
Autoencoders
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4. Contemporary IoT security challenges
Vulnerabilities of smart objects (e.g. hacking of software).
Threats associated with the convergence of Information and
Operational technologies.
Increased legal requirements in complying with Regulations.
New sophisticated ways for conducting large-scale attacks.
Increased
sophistication
of IoT Systems
also means…
A
B
C
D
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6. Deep Learning
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Analytics Engine Algorithm
Trains itself using sequences of historical data.
Isolates and stores patterns of suspicious behavior.
Implemented Example:
Variational Autoencoder (VAE)
… when evaluation
suggests an attack is
taking place.
Alerts
… them, with patterns
encountered during
training
Juxtaposes
… new, previously
unknown streams of
measurements from IoT
devices
Monitors

7. Autoencoders
Conceptually separated into the
encoder and the decoder
(similarly to image
compression).
Part of the information is lost
during the encoding process
and cannot be recovered when
decoding.
Find the pair that keeps the
maximum of information when
encoding and, so, has the
minimum of reconstruction error
when decoding.
Variational Autoencoder (VAE)
an autoencoder whose training
is regularized to avoid
overfitting to the training
dataset, while performing
poorly in new unknown
datasets.
Structure Anomalies Indicator Purpose A Step Further
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8. Benefits of VAE
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The Model can be fitted to new abnormal
cases without retraining the whole dataset.
The Model can reconstruct missing
features in the input data.
The Model can detect abnormalities that
had not been encountered during training.
The Model is efficient on data of low
dimensionality and isolated attacks.
To validate the performance of
the approach and its integration
in the platform, we have trained
autoencoders of various
complexities with various
datasets. Here follow two use
cases, one for connected car
datasets and one for socially
assistive robots.
02
03
04
01

9. Connected Cars Use Case
The car behavior is represented as a set of data sequences, for example measurements from sensors
installed on the Vehicle that count its speed, fuel or steering angle.
The optimal error threshold for this model is t=0.22, where the accuracy score is 89% on this test set.
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10. Socially Assistive Robots Case
The dataset contained sensor and context reports from a Robot: motor readings, gesture reports,
event timestamps.
In this case, the ideal error threshold is t=0.02, where the accuracy score is 73%.
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11. Acknowledgements
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This work has been carried out in the scope of the H2020 SecureIoT project, which
is funded by the European Commission in the scope of its Horizon 2020 research
and innovation programme (contract number 779899). The authors acknowledge
valuable help and contributions from all partners of the project.

12. THANK YOU
Angela-Maria Despotopoulou
Athens Information Technology, Greece