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Showing 2 results for Sybil Attack
Sayed Mohamamd Tabatabaei Parsa, Hassan Shakeri,
Volume 5, Issue 1 (9-2016)
Abstract
Wireless Sensor Networks (WSNs) are an ideal solution for miscellaneous applications of surveillance and control, such as traffic control, environmental monitoring, and battlefield surveillance. The wireless sensor nodes have limited memory and processing capability. The Sybil Attack is a serious threat in which a malicious node creates multiple fake identities in order to mislead the other sensor nodes. This attack can have influence on routing protocols and the operations like voting and data aggregation. In this paper, we present a dynamic and lightweight algorithm with a confidence-aware trust approach. The Algorithm uses the trust value of each sensor node to reduce the false alarm rates and detect indirect Sybil attacks in WSNs. The simulation results demonstrate that the average detection and wrong detection rates are 92% and 0.08% respectively.
Sayed Mohammad Tabatabaei Parsa, Hassan Shakeri,
Volume 6, Issue 2 (3-2018)
Abstract
Wireless Sensor Networks (WSNs) gather the environmental information via some sensor nodes and send them after some simple processing functions if necessary. These nodes are constrained devices in terms of memory, processing capability, radio range, and energy. Due to the unattended deployment of sensor nodes and the nature of wireless communications, these networks are vulnerable to several attacks. Among these, the Sybil Attack is a serious threat in which a malicious node creates multiple fake identities in order to mislead the other sensor nodes. In this case, the malicious node can attract lots of traffic and disrupt routing protocols. In this paper, we present a confidence-aware trust model considering Time Factor to detect such attacks. In this model, we use the indirect trust, gained from the neighbors' recommendations, in order to detect the indirect Sybil attacks, in which a legal node is not directly associated with the Sybil node. The Algorithm has been implemented using MATLAB. The simulation results demonstrate significant preference of the proposed method in terms of detection accuracy and false alarm rates. The average rate of detection and false detection of the proposed model are 93% and 0.26%.
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