Background:
Wireless Sensor Networks (WSNs) are self-configured infrastructure-less
networks are comprising of a number of sensing devices used to monitor physical or environmental
quantities such as temperature, sound, vibration, pressure, motion etc. They collectively transmit
data through the network to a sink where it is observed and analyzed.
Materials and Methods:
The major issues in WSN are interference, delay and attacks that degrade
their performance due to their distributed nature and operation. Timely detection of attacks is imperative
for various real time applications like healthcare, military etc. To improve the Black hole
attack detection in WSN, Projected Independent Component Analysis (PICA) technique is proposed
herewith, which detects black hole attack by analyzing collected physiological data from
biomedical sensors.
Results:
The PICA technique performs attack detection through Mutual information to measure
the dependence in the joint distribution. The dependence among the nodes is identified based on
the independent probability distribution functions and mutual probability function.
Conclusion:
The black hole attack isolation is then performed through the distribution of the
attack separation message. This supports to improve Packet Delivery Ratio (PDR) with minimum
delay. The simulation is carried out based on parameters such as black hole attack detection rate
(BHADR), Black Hole Attack Detection Time (BHADT), False Positive Rate (FPR), PDR and delay.
A Review on Black Hole Attack Detection in Wireless Sensor Networks
