Identifying Misbehaving Greedy Nodes in IoT Networks

Abstract

One of the central communication infrastructures of the Internet of Things (IoT) is the IEEE 802.15.4 standard, which defines Low Rate Wireless Personal Area Networks (LR- WPAN). In order to share the medium fairly in a non-beacon-enabled mode, the standard uses Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA). The nature of connected objects with respect to various resource constraints makes them vulnerable to cyber attacks. One of the most aggressive DoS attacks is the greedy behaviour attack which aims to deprive legitimate nodes to access to the communication medium. The greedy or selfish node may violate the proper use of the CSMA/CA protocol, by tampering its parameters, in order to take as much bandwidth as possible on the network, and then monopolize access to the medium by depriving legitimate nodes of communication. Based on the analysis of the difference between parameters of greedy and legitimate nodes, we propose a method based on the threshold mechanism to identify greedy nodes. The simulation results show that the proposed mechanism provides a detection efficiency of 99.5%.

A Comprehensive Review on Smart Grids: Challenges and Opportunities

“…Neighborhood Area Networks (NAN) [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37] Software Defined Networks (SDN) [20,[38][39][40][41][42][43] Interdependent networks (IN) [44][45][46][47][48][50][51][52][53] Field Area Networks (FAN) [21,49,[54][55][56][57][58][59][60][61][62][63]69] Wireless Sensor Networks (WSN) [64][65][66][67][68][69]...…”

“…Note that 17% can find both in NANs and WSNs, respectively, pointing out a great opportunity to develop smart grids with softwaredefined topologies. Internet of Things [19,23,24,27,28,30,31,36,37,58,64,66,73,74,79,105,107] Machine learning [20,29,32,51,55,56,60,62,69,78,80,81,85,98,102,106] Data mining [47,107] Machine learning and neural training [21,54,69,81,96] Short term memory network [41,61] Power Line Communication Technology [53,63,90] Power electronics [35,50,99,101,<...>…”

“…Table 3 shows that most of the Encryption issues do not clearly define the algorithms they use, although there are some articles that use Multi-dimensional Data aggregation and even Cognitive Risk Control. Multi-dimensional Data aggregation [42,[55][56][57]60,62,64] Cognitive Risk Control [37,82] Not Defined [19,20,[22][23][24]26,28,29,31,35,36,38,39,43,[45][46][47][48]50,[52][53][54]58,61,63,73,[76][77][78][79]81,[98][99][100][101][102][103][104][105]…”

“…Although there are some schemes that contemplate the Direct Current for very specific applications in the Industry. Direct Current [28,48,55,88,99,107,108] Alternate Current [19,[21][22][23][24][25][26][27][29][30][31][35][36][37][38][40][41][42][43][45][46][47]50,[52][53][54][56][57][58][59][60][61][62][63]69,73,76,78,79,98,[108][109][110] In terms of the Type of current transmitted by the Smart Grid, Figure ...…”

“…In smart IoT networks, DoS attacks are the most aggressive, as they prevent legitimate communication between sensors. Thus, Sadek et al [36] propose a model that is based on a mechanism to estimate a threshold that effectively identifies greedy and malicious nodes with 99.5%.…”