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PhD in Cyber Attack Detection In IoT Devices
Phd in Security Attacks & Energy In WSN Network
May 27, 2021PhD in Optical Communication Using Machine Learning
May 27, 2021
PhD in Cyber Attack Detection In IoT Devices
INTRODUCTION
IoT or Internet of Things devices offers ease to the users as they are packed with sensors such as heat, humidity, speed, sound, etc. which are controlled by internet enabled devices such as mobile phone or laptop.
This often leads to cyber attacks as multiple devices are networked through insecure operating systems. Hence, the proposed research focuses on the mitigation of cybersecurity and also overcome resource limitations.
PhD in Cyber Attack Detection In IoT Devices
IDEAS
There is a need to develop a technology for cyber-attack detection and the proposed model offers an algorithm that is employed with Zolertia Z1 motes so as to check the effectiveness in cyber security and reduction in power consumption. Once, the results are positive, the same technology can be implied to other IoT devices.
ISSUES IN THE CURRENT TECHNOLOGY
The distributed attacks on the IoT devices render the entire model and records the confidential information leading to data breaching.
Secondly, there is a challenge to reduce the energy consumption quantity in the current model.
APPROACH
To overcome this issue, the IDM algorithm has been applied with Zolertia Z1 motes for testing efficiency.
PhD in Cyber Attack Detection In IoT Devices
PROPOSAL
The IDS algorithm has been used in the model as it swiftly detects known anomalies with a low risk of raising false alarms. The tested IoT is based on the second generation MSP430, a low powered 16-bit MCU functioning at a frequency of 16 MHz. This is structured with the CC2420 transceiver that operates at 2.4 GHz.
A testbed of 9 motes had been used in this and out of that 8 were used to make a 15 x 5 ft grid; the last mote was deployed to power trace the energy consumption. The resulted power trace is used for determining the efficiency of the deployed algorithm based on the accurate data on the energy intake of the entire model. To calculate it, the following equation has been used:
Where,
CPU= duration of mote activation
LPM= Duration for which the mote was changed in low power consumption mode
Tx = Time took for the transmission
Rx = Total listening time
As Zolertia Z1 is a low powered device, Contiki OS was used as it is well compatible with MSP430.
The battery life of the device was deduced using:
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