Abstract:
The passive eavesdropping attack s data
privacy. The security of IoT is still an unresolved question. Soon the people all over the world
adopt IoT as smart home applications, wearables, and Industrial automation too. The risks in the
confidentiality of private data are raising the number of devices and data exchanged. Even though
the data having various light-weight encryption practices, the attackers still have a way to invade
the confidentiality of the data. Metadata information in the packet header such as the size of the
packet, protocol type, MAC address of source and destination, and state of the device can be
exploited in a way that the attacker can be able to understand the user activities in a particular
device. Application of deep packet inspection method and machine learning to huge data packets
of a targeted user can reveal a lot of personal information. In this study, we propose a model to
protect the header information. Speck, a lightweight encryption algorithm is used to encrypting the
metadata such as MAC addresses of the source and destination. Because the MAC address reveals
the device type and services to the attacker. The Software-Defined Network is chosen to be the
central control for packets transmission to overcome the anonymity of packet flow. The decoupling
mechanism of the control plane and the forwarding plane, the controller directs the encrypted
packets to the routers and switches as a secured third party. The model was simulated using the
RYU controller and tested in the Mininet SDN emulation platform using 4 nodes and respective
connecting Ovsk switches with the central controller. The model has been validated using the
packet capturing process with Wireshark.
