scholarly journals Employing Keyed Hash Algorithm, Sequential Probability Ratio Test, and Temperature Comparison Test as Security Against Node Capture Attacks of IoT-Based WSNs

2021 ◽  
pp. 500-507
Author(s):  
Jhon Aron F. Varca ◽  
◽  
Earl Nestor T. Velasquez ◽  
Joseph Bryan G. Ibarra

The emergence of IoT opened new opportunities for development in various fields. With all the information that it gathers, it became an interesting target for multiple attackers. Thus, this study will enforce security solutions to IoT-based devices specifically in the perception layer by incorporating a Temperature Comparison Test, Keyed Hash Algorithm and evaluating it using SPRT especially in the defense against malicious activities detected in the nodes of a network namely for Mobile and Immobile attacks. For immobile attacks, using the keyed hash algorithm and the SPRT, the hash key of the passcodes was compared to determine the safety of the nodes. Hence, from the functionality test that was conducted, and evaluating the data gathered using SPRT and Bernoulli’s equation, the reliability of the protocol to detect Immobile attacks is concluded to have a 100% detection rate. For mobile node attacks, the study assumes the environment to be under normal, warm, and cold room temperatures. where both mobile and without mobile attack is simulated, the result shows that there is only an overall 3% difference from the temperature measure by the sensor to the ambient temperature. Hence, combining these protocols that are applied in the study eliminates the single points of failure in the nodes that are either applicable only to a distributed scheme or mobility support, the study also compared the tested protocol to the other existing protocols.

1993 ◽  
Vol 9 (3) ◽  
pp. 431-450 ◽  
Author(s):  
Noel Cressie ◽  
Peter B. Morgan

Under more general assumptions than those usually made in the sequential analysis literature, a variable-sample-size-sequential probability ratio test (VPRT) of two simple hypotheses is found that maximizes the expected net gain over all sequential decision procedures. In contrast, Wald and Wolfowitz [25] developed the sequential probability ratio test (SPRT) to minimize expected sample size, but their assumptions on the parameters of the decision problem were restrictive. In this article we show that the expected net-gain-maximizing VPRT also minimizes the expected (with respect to both data and prior) total sampling cost and that, under slightly more general conditions than those imposed by Wald and Wolfowitz, it reduces to the one-observation-at-a-time sequential probability ratio test (SPRT). The ways in which the size and power of the VPRT depend upon the parameters of the decision problem are also examined.


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