Here is the work performed for the PRISENODE:
1. reading and simulating the state of the art of the ML techniques for the traffic data gathered from the network like IoT and Fog nodes (data analyzing activities)
2. Simulating state of the art through iFogSim and link them to the SDN/NFV infrastructure and import the tested toy scenarios on Mininet tool for various topologies (extracted from topology zoo)
3. Contacting with CLOUDS lab postdocs and learning from them implementing verification of the source code and integration their strategies on my architecture
4. Import the learned materials, strategies, methodologies in traffic data analyzing, ML solutions and SDN/NFV programming strategy with the help of cloud/fog techniques to UNIPD
5. Integrate security and privacy challenge interplay on the SDN/NFV various traffic data (IoT users and network characteristics) to analyze the integrity and availability of the resource in the system and understand the abnormalities of the data in Fog-supported SDN network
In the end, during these three months, we published a paper on the topic which is available in Springer. Regarding the publication, the work addressed two important problems. The first problem is related to how to manipulate the data which are gathered from the IoT/mobile applications (Android dataset). Then, how to link the gathered tuned and tested model based on the android data features on the Fog nodes instantiated on the switches and how we can assure the generated machine learning model can preserve efficient network throughput with minimal resource allocation and scheduling. To do so, first, we study the problems in gathering data from fog nodes connected to the Android mobile and how can we imply attack scenarios with the help of manipulation of the features of the traffic data gathered from Android mobiles. Afterwards, we create our attack scenarios with the help of machine learning models and inject the poison data to the existing supervised traffic data and validate it by several test scenarios (simulations).
