-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA512 Hi Eliot, Thymen asked me to pass this on to you (but I believe he is now subscribed to the list): - -- Eliot has a valid point. We didn’t focus on this aspect yet, but it’s good to keep that in mind. In general, adversaries can fool any machine learning model once they know the model parameters. One can exploit the parameters to craft so-called "adversarial examples” that will generate a specific output/classification. This is actually a research topic on it’s own called adversarial machine learning [1]. This blog [2] shows some famous examples using images of pandas. [1] https://en.wikipedia.org/wiki/Adversarial_machine_learning [2] https://openai.com/blog/adversarial-example-research/ - --- Kind regards, Mirjam On 19/03/2020 13:47, Eliot Lear wrote:
Very interesting work!
A cautionary question:
If I wanted to pretend to be one of these devices on your network, how hard would it be?
Eliot
On 19.03.20 12:56, Poonam Yadav wrote:
Thanks for sharing!
We have analysed similar pattern in many IoT devices and presented periodicity in IoT traffic as FFT (fig 4 - of IoTDI paper attached for reference) and some initial results here in this report: https://www.repository.cam.ac.uk/handle/1810/284092 and full paper is here: https://dl.acm.org/doi/10.1145/3302505.3310082
Best regards,
On Thu, Mar 19, 2020 at 10:31 AM Mirjam Kuehne <mir@ripe.net <mailto:mir@ripe.net>> wrote:
Dear colleagues,
IoT devices often perform activities on a periodic basis. Thymen Wabeke of SIDN Labs shares his analysis of periodic network traffic from IoT lightbulbs. Read it on RIPE Labs at:
https://labs.ripe.net/Members/thymen_wabeke/visualisations-of-periodi c-iot-traffic
Kind regards,
Mirjam Kühne RIPE NCC
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