Apparatus and method for malicious node detection in Internet of Things ( IoT) based on uncertain decisions

Abstract Internet of Things (IoTs) are distributed networks exposed to an open environment, collecting of self-organized nodes with limited computation and communication capabilities and energy covering deployed areas that interested by the controllers. A IoT has been making up as known as aware of environmental technologies such as sound, water contamination, temperature, pressure, motion and other pollutants. While wireless communication becomes all sectors of daily life, the security threats to IoTs become increasingly diversified, prevention based due to the open nature of the wireless medium. For example, an adversary can easily eavesdrop and replay or inject fabricated messages so it is vulnerable to malicious nodes. Different cryptographic methods can be used to defend against some of such attacks but always very limited due to IoT's natures. The node in a IoT is called compromised becomes another major problem of IoT security since it allows an adversary to enter inside the security perimeter of the network and launch attacks, which raised a serious challenge for IoTs. This patent is focusing on investigating internal attacks of IoTs with multi-hop and single sinker, such as compromised nodes in a deployed IoT, by which we first present our novel protecting algorithm to IoT to protect it with the evidences that our novel algorithm works efficiently and effectively. Our innovative algorithm and apparatus takes advantages of uncertain decisions, which involved in the posteriori probability of binary events represented by the beta family of density functions and Dempester Shafer Theory (DST). Applying this method, there is no need to have any knowledge about the structure of the network. Keywords: security of JoTs, internal attacks, beta function, Dempester Shafer Theory, sensor optimum deployment no - 0.00 , 1 1 1 +Seres1 0.00 0.20 0.40 0.60 Percentage compromised nodes Figure 5: Chart of "the "normalized average delivery rate" vs. "percentage compromised nodes." S0.80 p r0.70 -+-Series2 0.40 0 -- Seriesl z 0.10 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 Probability of the compromised nodes in the SWN Figure 6: normalized detection accuracy against the probability of the compromised nodes. Resiliency Control operation (at 30%) 'a 040 z 0 5 10 15 20 25 30 35 40 45 Normalized time units during the running simulations Figure 7: Normalized resiliency degree against the normalized time units