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We studied people’s success on the detection of phishing emails after they were trained under one of three phishing frequency conditions, where the proportion of the phishing emails during training varied as: low frequency (25% phishing emails), medium frequency (50% phishing emails) and high frequency (75% phishing emails). Individual base susceptibility to phishing emails was measured in a pre-training phase in which 20% of the emails were phishing; this performance was then compared to a post-training phase in which participants aimed at detecting new rare phishing emails (20% were phishing emails). The Hit rates, False Alarm rates, sensitivities and response criterion were analyzed. Results revealed that participants receiving higher frequency of phishing emails had a higher hit rate but also higher false alarm rate at detecting phishing emails at post-training compared to participants encountering lower frequency levels during training. These results have implications for designing new training protocols for improving detection of phishing emails.

MANETs are getting exposure due to their versatile applications in the last few years. New networking paradigms like VANETs and FANETs have evolved by using the concept of MANETs. FANETs provide a distinguished approach to tackle with the situations like emergency, natural disaster, military battle fields, UAVs etc. Due to the high mobility in FANETs nodes and rapid topology change, it is a big challenge for researcher to apply routing in FANETs. Mobility models also play a very important role in optimizing the performance of routing protocol in FANET. The research presented aims to apply OLSR routing protocol in FANETs and study of OLSR under different mobility models to optimize the performance of OLSR in FANETs.

SummaryThe use of unmanned aerial vehicles has significantly increased for forming an ad hoc network owing to their ability to perform in exciting environment such as armed attacks, border surveillance, disaster management, rescue operation, and transportation. Such types of ad hoc networks are popularly known as flying ad hoc networks (FANETs). The FANET nodes have 2 prominent characteristics—collaboration and cooperation. Trust plays an important role in predicting the behavior of such nodes. Researchers have proposed various methods (direct and indirect) for calculation of the trust value of a given node in ad hoc networks, especially in mobile ad hoc networks and vehicular ad hoc networks. The major characteristic that differentiates a FANET from other ad hoc networks is the velocity of the node; as a result, there are frequent losses in connection and topology change. Therefore, the existing methods of trust calculation are not efficient and effective. In this paper, a fuzzy‐based novel trust model has been proposed to handle the behavioral uncertainty of FANET nodes. Nodes are classified using a multicriteria fuzzy classification method based on node's behavior and performance in the fuzzy and complex environment. Quality of service and social parameter (recommendation) are considered for evaluating the trust value of each node to segregate the selfish and malicious nodes. With the node classification, FANET nodes are rewarded or punished to transform node behavior into a trust value. Compared with the existing trust techniques, the simulation results show that the proposed model has better adaptability, accuracy, and performance in FANETs.

Phishing is the practice of deceiving humans into disclosing sensitive information or inappropriately granting access to a secure system. Unfortunately, there is a severe lack of theoretical models to adequately explain and predict the cognitive dynamics underlying end-user susceptibility to phishing emails. This paper reports findings from an Instance-Based Learning (IBL) model developed to predict human response to emails obtained from a laboratory experiment. Particularly, this work investigates the effectiveness of using established natural language processing methods, such as LSA, GloVe, and BERT, to represent email text within IBL models. We found that using representations that consider contextual meanings assigned by humans could enable IBL agents to predict human response with high accuracy (80%). In addition, we found that traditional NLP methods that capture semantic meanings in natural language may not be effective at representing how people may encode and recall email messages. We discuss the implications of these findings.