An Evolutionary Game-Based Secure Clustering Protocol With Fuzzy Trust Evaluation and Outlier Detection for Wireless Sensor Networks

Network Master Node Assessed Trust Factor with Arbitrary Neighbor Assessment for Secure Route Detection in 6G Enabled Wireless Sensor Networks

“…Trustworthy and reliable data delivery is challenging to achieve with WSNs because of their unique properties and limitations. To provide secure data transmission and alleviate the conflict between energy consumption and security, Yang et al [8] detailed an evolutionary game-based safe clustering approach for WSNs that employs fuzzy trust evaluation and outlier detection. The initial phase involves implementing a fuzzy trust assessment method that effectively decreases trust uncertainty and derives trust values from transmission evidences.…”

“…Because of their open and hostile environment, diversified set of critical applications, and open media, WSNs are susceptible to a wide variety of attacks. When confronted with assaults on individual compromised nodes [8], traditional security mechanisms such as authentication and cryptography can only provide partial protection. The entire WSN might be brought down or taken over if a compromised node was commanded to launch attacks against other nodes in the network [9].…”

A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

“…Several energy consumption concerned network system designs take K-means clustering into account, as observed in [109]- [112]. Energy aware secured data transmission scheme is presented in [113] that uses fuzzy trust evaluation and K-means algorithm-based outlier detection. DoS immune IoT network system configuration is studied in [114] where the clustering of data related to immune network traffic and attacked network traffic is performed by means of K-means algorithm.…”

“…Research topic Research analysis/findings K-means clustering [103] Joint resource allocation and clustering mechanisms Energy efficiency analysis [104] Routing protocol with K-means clustering, maximum stable set problem and continuous hopfield network Improved throughput, transmission delay reduction and cluster stability [105] Routing algorithm Data transmission rate analysis and energy utilization [106] Edge computing node deployment mechanism Analysis of computing resources deployment cost and network delay tradeoff with proposed mechanism and comparison of that against traditional K-means clustering and random deployment method [107] Non-linear equalization operation Performance evaluation in terms of computational complexity, cost and hardware constraints [108] Device clustering scheme Improved packet delivery ratio and latency compared to ACO and GWO [109] Unequal clustering mechanism Transmission delay and energy consumption reduction with proposed algorithm and comparison of that against withEKMT, UCR and CU-CRA" [110] Device clustering scheme Comparison of energy utilization against low-energy adaptive clustering hierarchy (LEACH) based approaches [111] Device clustering scheme Analysis of energy consumption balancing with the proposed scheme [112] Device clustering scheme Analysis of network coverage and energy utilization tradeoff [113] Device clustering scheme Analysis of transmission delay reduction and protection from malicious device induced attacks [114] Data clustering Immunity from DoS attacks Expectation Maximization [115] Aerial base station deployment with proposed EM based approach Improved downlink capacity, low energy consumption and service delay against traditional EM and K-means approach [116] Indoor localization Performance evaluation of the localization method [117] Indoor localization Performance evaluation against KNN in terms of localization errors [118] Time difference of arrival (TDOA) source direct position determination Reduction of computational load of the direct position determination [119] Passive localization scheme Analysis of the localization performance, computational complexity and communication overhead [120] Mulltitarget localization scheme Localization error analysis with the proposed method, basis pursuit, GMP and least square compressive sensing [121] RIS channel modeling Outrage probability analysis for the proposed approach [122] Channel characterization method Performance analysis in terms of RMS delay, AOA and ZOA spread [123...…”

Automation and Orchestration of Zero Trust Architecture:Potential Solutions and Challenges

“…However, supervised learning must have labeled data to train the model, in most ZTA practical application scenarios, users and devices features lack clear labels, and unsupervised learning such as K-means [90,162,96,154] can solve this problem by clustering trust objects without label into different level of trust groups. To further improve unsupervised learning performance, semi-supervised learning, which combines supervised and unsupervised learning, can be used to optimize the clustering boundaries.…”