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Security research and training is attracting a lot of investment and interest from governments and the private sector. Most efforts have focused on physical security, while cyber security or digital security has been given less importance. With recent high-profile attacks it has become clear that training in cyber security is needed. Serious Games have the capability to be effective tools for public engagement and behavioural change and role play games, are already used by security professionals. Thus cyber security seems especially well-suited to Serious Games. This paper investigates whether games can be effective cyber security training tools. The study is conducted by means of a structured literature review supplemented with a general web search.While there are early positive indications there is not yet enough evidence to draw any definite conclusions. There is a clear gap in target audience with almost all products and studies targeting the general public and very little attention given to IT professionals and managers. The products and studies also mostly work over a short period, while it is known that short-term interventions are not particularly effective at affecting behavioural change.

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In this paper, a novel method is presented for lowlatency online action recognition from skeleton data. The introduction of pose based features has reduced viewpoint and anthropometric variations, so differing execution rates and personal styles are the major sources of classification error. Previous work for online action recognition fails to adequately address both execution rate and personal style. To overcome these limitations a compression and fusion of offline action recognition approaches has transpired. Specifically, clustered action manifolds are proposed for low computational latency and template fragment matching with peak key poses are introduced for low observational latency. The style invariance of spatiotemporal manifolds is combined with the execution rate invariance of Dynamic Time Warping (DTW). Experimental results on two publicly available datasets demonstrate the high accuracy of the proposed method. Keywords-gesture and behaviour analysis, human computer interaction; dimensionality reduction and manifold learning

Recognising human actions in real-time can provide users with a natural user interface (NUI) enabling a range of innovative and immersive applications. A NUI application should not restrict users' movements; it should allow users to transition between actions in quick succession, which we term as compound actions. However, the majority of action recognition researchers have focused on individual actions, so their approaches are limited to recognising single actions or multiple actions that are temporally separated. This paper proposes a novel online action recognition method for fast detection of compound actions. A key contribution is our hierarchical body model that can be automatically configured to detect actions based on the low level body parts that are the most discriminative for a particular action. Another key contribution is a transfer learning strategy to allow the tasks of action segmentation and whole body modelling to be performed on a related but simpler dataset, combined with automatic hierarchical body model adaption on a more complex target dataset.Experimental results on a challenging and realistic dataset show an improvement in action recognition performance of 16% due to the introduction of our hierarchical transfer learning. The proposed algorithm is fast with an average latency of just 2 frames (66ms) and outperforms state of the art action recognition algorithms that are capable of fast online action recognition.