Managing Trust and Detecting Malicious Groups in Peer-to-Peer IoT Networks

Peer-to-peer (P2P) networking is becoming prevalent in Internet of Thing (IoT) platforms due to its low-cost low-latency advantages over cloud-based solutions. However, P2P networking suffers from several critical security flaws that expose devices to remote attacks, eavesdropping and credential theft due to malicious peers who actively work to compromise networks. Therefore, trust and reputation management systems are emerging to address this problem. However, most systems struggle to identify new smart models of malicious peers, especially those who cooperate together to harm other peers. This paper proposes an intelligent trust management system, namely, Trutect, to tackle this issue. Trutect exploits the power of neural networks to provide recommendations on the trustworthiness of each peer. The system identifies the specific model of an individual peer, whether good or malicious. The system also detects malicious collectives and their suspicious group members. The experimental results show that compared to rival trust management systems, Trutect raises the success rates of good peers at a significantly lower running time. It is also capable of accurately identifying the peer model

Managing Trust and Detecting Malicious Groups in Peer-to-Peer IoT Networks

Peer-to-peer (P2P) networking is becoming prevalent in Internet of Thing (IoT) platforms due to its low-cost low-latency advantages over cloud-based solutions. However, P2P networking suffers from several critical security flaws that expose devices to remote attacks, eavesdropping and credential theft due to malicious peers who actively work to compromise networks. Therefore, trust and reputation management systems are emerging to address this problem. However, most systems struggle to identify new smart models of malicious peers, especially those who cooperate together to harm other peers. This paper proposes an intelligent trust management system, namely, Trutect, to tackle this issue. Trutect exploits the power of neural networks to provide recommendations on the trustworthiness of each peer. The system identifies the specific model of an individual peer, whether good or malicious. The system also detects malicious collectives and their suspicious group members. The experimental results show that compared to rival trust management systems, Trutect raises the success rates of good peers at a significantly lower running time. It is also capable of accurately identifying the peer model

Artificial intelligence-based fuzzy logic systems for predicting radiation protection awareness levels among university population

The present study aims to assess the knowledge level of radiation protection among individuals of Princess Nourah bint Abdulrahman University (PNU) using artificial intelligence baesd fuzzy logic system. This crosssectional study included 428 PNU participants. They were asked to fill in the online questionnaire, consisting of demographic data, education level, and radiation protection awareness. After informed consent was completed, a statistical package for the social sciences as well as fuzzy logic system was used for data analysis. The participant group consisted of 98.4% females, 96.3% individuals aged 18–28 years (the most common age group), 63.1% bachelor’s degree holders, and 65.7% medical participants. Specialty and radiation protection awareness exhibited significant association (P 0.05). PNU individuals in the medical field differed significantly (P > 0.05) with the non-medical individual in their knowledge of radiation protection. This study suggests that PNU individuals in the medical field have a reasonable awareness of radiation protection. However, the general knowledge of nonmedical individuals must be improved to raise awareness. Based on the obtained results by using fuzzy model, this study suggests that the tool can be used in the process of radiation protection awareness in other institutions and areas