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The modelling of deceptions in game theory and decision theory has not been well studied, despite the increasing importance of this problem in social media, public discourse, and organisational management. This paper presents an improved formulation of the extant information-theoretic models of deceptions, a framework for incorporating these models of deception into game and decision theoretic models of deception, and applies these models and this framework in an agent based evolutionary simulation that models two very common deception types employed in “fake news” attacks. The simulation results for both deception types modelled show, as observed empirically in many social systems subjected to “fake news” attacks, that even a very small population of deceivers that transiently invades a much larger population of non-deceiving agents can strongly alter the equilibrium behaviour of the population in favour of agents playing an always defect strategy. The results also show that the ability of a population of deceivers to establish itself or remain present in a population is highly sensitive to the cost of the deception, as this cost reduces the fitness of deceiving agents when competing against non-deceiving agents. Diffusion behaviours observed for agents exploiting the deception producing false beliefs are very close to empirically observed behaviours in social media, when fitted to epidemiological models. We thus demonstrate, using the improved formulation of the information-theoretic models of deception, that agent based evolutionary simulations employing the Iterated Prisoner’s Dilemma can accurately capture the behaviours of a population subject to deception attacks introducing uncertainty and false perceptions, and show that information-theoretic models of deception have practical applications beyond trivial taxonomical analysis.

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Due to the nature of the wireless media, ad-hoc wireless networks are vulnerable to various attacks. There are security protocols that prevent unauthorized nodes from accessing the network through authentication. Secrecy of information is provided through encryption. However these protocols cannot detect if any member of the network degrades the network performance due to misbehavior. Therefore an intrusion detection system (IDS) is required that monitors what is going on in the network, detects misbehavior or anomalies based on the monitored information and notifies other nodes in the network to take necessary steps such as to avoid or punish the misbehaving nodes. In this paper we propose an IDS, referred to as the SAHN-IDS, suitable for multi-hop ad-hoc wireless networks like a SAHN (Suburban Ad-hoc Network). SAHN-IDS detects misbehavior based on nodes getting an unfair share of the transmission channel. It also detects anomalies in packet forwarding, such as intermediate nodes dropping or delaying packets. Unlike mostIDSs for detecting anomalies in packet forwarding, SAHN-IDS does rely on overhearing packet transmissions of neighboring nodes, since that is ineffective in networks where nodes use different transmission power, different frequency channels and directional antennas for different neighbors. Moreover, unlike most IDSs, most of the thresholds in SAHN-IDS are set dynamically. We show the effectiveness of SAHN-IDS through simulations.

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