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However, there are scenarios, where cryptography techniques fail to capture malicious behavior of a node.
In essence, RSs try to predict the future behavior of a node by analyzing its past behavior.
This metric describes the social behavior of a node by counting the number of nodes encountered by this node.
If the base station can detect an abnormal behavior of a node, it can lunch a rekeying to refresh and revoke this node.
The behavior of a node on any given day is quantified as Proportion of Good Transactions = Number of Legitimate Transactions Total Number of Transactions.
Such a definition quantifies the social behavior of a node by counting its encounters with all the other nodes in the network over a period.
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Since the core of system dynamics is to self-adjust node activity states and connections, we focus the mathematical description on these two behaviors of a node.
If the behavior of a mobile node matches an attack profile or deviates from a normal profile, the node will be regarded as a malicious node.
The entity mobility models represent the behavior of an individual node or group of nodes independently from other nodes.
When the behavior of a mobile node matches an attack profile or deviates from a normal profile, the mobile node should be labeled as a malicious node.
The experiments performed use an energy dissipation model that we have proposed to simulate the behavior of a sensor node in terms of energy consumption.
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