Dynamic Network Security Mechanism Based on Trust Management in Wireless Sensor Networks

Abstract

Wireless sensor network is a key technology in Internet of Things. However, due to the large number of sensor nodes and limited security capability, aging nodes and malicious nodes increase. In order to detect the untrusted nodes in the network quickly and effectively and ensure the reliable operation of the network, this paper proposes a dynamic network security mechanism. Firstly, the direct trust value of the node is established based on its behavior in the regional information interaction. Then, the comprehensive trust value is calculated according to the trust recommendation value and energy evaluation value of other high-trust nodes. Finally, node reliability and management nodes are updated periodically. Malicious nodes are detected and isolated according to the credibility to ensure the dynamic, safe, and reliable operation of the network. Simulation results and analysis show that the node trust value calculated by this mechanism can reflect its credibility truly and accurately. In terms of reliable network operation, the mechanism can effectively detect malicious nodes, with higher detection rate, avoid the risk of malicious nodes as management nodes, reduce the energy consumption of nodes, and also play a defensive role in DOS attacks in wireless sensor networks.

A Trust Management Model for IoT Devices and Services Based on the Multi-Criteria Decision-Making Approach and Deep Long Short-Term Memory Technique

“…For the trust computation process, the trustee node was selected from the set of nodes based on trust attributes; (2) aggregation stage, through this stage, the trust score was calculated based on the artificial neural network (ANN) algorithm; and finally (3) the update stage, the time-driven model was used to update the trust score periodically. A study conducted by [27] recommended a dynamic trust management mechanism in wireless sensor networks (WSNs). Firstly, the node's direct trust value is determined by evaluating its performance from interaction with regional information.…”

“…Existing studies have fo cused on solving a specific type of malicious behaviors, but an advanced attacker may choose a sophisticated strategy to act maliciously. Consequently, most existing research and development efforts in the domain of trust management are centered around apply ing statistical models or machine learning techniques, such as in [3,7,14,22,23,27]. These techniques have several drawbacks, including ineffectiveness when dealing with big data and continuously changing behaviors, high memory utilization, and difficulty in quanti fying uncertainty for untrusted behaviors.…”

Detection and Location of Malicious Nodes Based on Homomorphic Fingerprinting in Wireless Sensor Networks

“…is scheme only needs to determine a trust threshold, but the trust threshold is static, and the misjudgment rate of normal nodes being judged as malicious nodes is high. In order to detect the untrusted nodes in the network quickly and effectively and ensure the reliable operation of the network, Zheng et al [19] proposed a network security mechanism based on trust management to deal with the threats faced by WSNs (DNSMTM). Based on the trusted access of nodes, this mechanism firstly calculates the local trust degree of nodes according to existing interaction behavior and further obtains the comprehensive trust degree of nodes that can reflect the trust degree of nodes, and the detection of malicious nodes is carried out according to the comprehensive trust degree of nodes.…”

“…e detection schemes [14][15][16] based on multihop acknowledgment need to transmit a large number of acknowledgment packets, which will lead to high communication overhead. e detection schemes [17][18][19][20][21][22] based on trust evaluation need more monitoring nodes, which greatly increases the overhead of the network. And the current detection schemes of malicious nodes mainly focus on how to detect and locate malicious nodes in a single path.…”

“…Advantages Disadvantages TWOACK [14] Can be easily added to source routing protocols such as the DSR protocol Greatly increases conflict and collision of network messages CHEMAS [15] Reduce the conflict and collision of network messages Cannot resist the collusive attacks PHACK [16] Not only can detect a selective forwarding attack but also can recover the routing from the location at which the data were dropped e scheme produces more ACK packets, which will consume more energy MNDREL [17] By analyzing the reputation value for the parent node evaluated by the child node, the malicious nodes in the network are effectively identified e real-time performance of the deletion model has to be improved GRFSN [18] is scheme only needs to determine a trust threshold Need complex evaluation model and the trust threshold is static, and the misjudgment rate of normal nodes being judged as malicious nodes is high DNSMTM [19] Can effectively detect malicious nodes and, with a higher detection rate, reduce the energy consumption of nodes Need a complex evaluation model and the responsibility of monitoring nodes is too heavy MSGSFS [20] Can effectively alleviate selective forwarding attack and has less energy consumption…”