Competition-related factors directly influence preferences for facial cues of dominance in allies

Alliance formation is a critical dimension of social intelligence in political, social and biological systems. As some allies may provide greater ‘leverage’ than others during social conflict, the cognitive architecture that supports alliance formation in humans may be shaped by recent experience, for example in light of the outcomes of violent or non-violent forms intrasexual competition. Here we used experimental priming techniques to explore this issue. Consistent with our predictions, while men’s preference for dominant allies strengthened following losses (compared to victories) in violent intrasexual contests, women’s preferences for dominant allies weakened following losses (compared to victories) in violent intrasexual contests. Our findings suggest that while men may prefer dominant (i.e. masculine) allies following losses in violent confrontation in order to facilitate successful resource competition, women may ‘tend and befriend’ following this scenario and seek support from prosocial (i.e. feminine) allies and/or avoid the potential costs of dominant allies as long-term social partners. Moreover, they demonstrate facultative responses to signals related to dominance in allies, which may shape sex differences in sociality in light of recent experience and suggest that intrasexual selection has shaped social intelligence in humans.

Threat analysis of IoT networks using artificial neural network intrusion detection system

The Internet of things (IoT) is still in its infancy and has attracted much interest in many industrial sectors including medical fields, logistics tracking, smart cities and automobiles. However, as a paradigm, it is susceptible to a range of significant intrusion threats. This paper presents a threat analysis of the IoT and uses an Artificial Neural Network (ANN) to combat these threats. A multi-level perceptron, a type of supervised ANN, is trained using internet packet traces, then is assessed on its ability to thwart Distributed Denial of Service (DDoS/DoS) attacks. This paper focuses on the classification of normal and threat patterns on an IoT Network. The ANN procedure is validated against a simulated IoT network. The experimental results demonstrate 99.4% accuracy and can successfully detect various DDoS/DoS attacks.