Research Area:  Wireless Sensor Networks

Abstract:

Smart healthcare is one of the core applications of the Internet of Things (IoT) technology in which the body sensors capture the patients periodic data and transmit seamlessly over the wireless communications to remote monitoring stations. Due to the open nature of wireless communications, privacy-preserving and security are the biggest concern for IoT enabled healthcare systems. Several trust-based and cryptography-based solutions proposed to achieve the privacy-preserving and data security, however, such layered solutions failed to address the cross-layer attacks such as Man-in-Middle Attack (MIMA). Thus we believe that cross-layer attack detection methods achieve effective protection against such attacks. This paper proposed the novel lightweight cross-layer trust computation algorithm for MIMA attacker detection called IC-MADS. The IC-MADS consists of two main contributions such as energy-efficient clustering and cross-layer attack detection. The clustering algorithm proposed for optimal Cluster Head (CH) selection using the probability computation and evaluation approach. Each sensor node probability is computed using the parameters like residual energy, distance to Base Station, and node degree parameters. The node with a higher probability value becomes the CH. This solves the energy imbalance and load balancing problems. The cross-layer trust evaluation approach introduced node evaluation to detect the MIMA attackers in the network. The trust computation of each node is performed by using the sensor nodes parameters across the different layers such as network, physical, and MAC layers. The aggregate trust value of each sensor node is then compared with the threshold value to check whether the node is an attacker. The simulation results prove that IC-MADS achieves better protection against MIMA attacks with minimum overhead and energy consumption.

Keywords:  

Author(s) Name:  Ashwini Kore & Shailaja Patil

Publisher name:  Springer

DOI:  10.1007/s11277-020-07250-0

Volume Information:  volume 113, pages 727–746 (2020)

Paper Link:   https://link.springer.com/article/10.1007/s11277-020-07250-0