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Deep Learning Analytics for IoT Security over a Configurable Big Data Platform
- 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 30/11/2019 2 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 30/11/2019 3
- 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 30/11/2019 4
- 5. 30/11/2019 5
- 6. Deep Learning 30/11/2019 6 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 30/11/2019 7
- 8. Benefits of VAE 30/11/2019 8 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. 30/11/2019 9
- 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%. 30/11/2019 10
- 11. Acknowledgements 30/11/2019 11 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