When investing in cyber security resources, information security managers have to follow effective decision-making strategies. We refer to this as the cyber security investment challenge.In this paper, we consider three possible decision support methodologies for security managers to tackle this challenge. We consider methods based on game theory, combinatorial optimisation , and a hybrid of the two. Our modelling starts by building a framework where we can investigate the effectiveness of a cyber security control regarding the protection of different assets seen as targets in presence of commodity threats. As game theory captures the interaction between the endogenous organisation's and attackers' decisions, we consider a 2-person control game between the security manager who has to choose among different implementation levels of a cyber security control, and a commodity attacker who chooses among different targets to attack. The pure game theoretical methodology consists of a large game including all controls and all threats. In the hybrid methodology the game solutions of individual control-games along with their direct costs (e.g. financial) are combined with a Knapsack algorithm to derive an optimal investment strategy. The combinatorial optimisation technique consists of a multi-objective multiple choice Knapsack based strategy. To compare these approaches we built a decision support tool and a case study regarding current government guidelines. The endeavour of this work is to highlight the weaknesses and strengths of different investment methodologies for cyber security, the benefit of their interaction, and the impact that indirect costs have on cyber security investment. Going a step further in validating our work, we have shown that our decision support tool provides the same advice with the one advocated by the UK government with regard to the requirements for basic technical protection from cyber attacks in SMEs .

This work, of a foundational nature, establishes a connection between secure computation and the 2nd principle of thermodynamics. In particular we show that any deterministic computation, where the final state of the system is observable, must dissipate at least W K_B T ln(2). Here W is the information theoretic notion of remaining uncertainty as defined in Quantitative Information Flow, K_B the Boltzmann constant and T the system temperature. By contrast, for probabilistic computations thermodynamic work can be extracted from secure systems: in this case, again using information theoretic results, we provide bounds on the amount of work that can be extracted. Further we show that in deterministic systems the dissipated energy is an upper bound on Smith's remaining vulnerability, by doing so we provide the first thermodynamic interpretation of guess ability. Crucially, unlike much literature on the physics of computation, our focus is not a universal model but a software field of great practical relevance, namely security. We see this work as a genuine scientific advance with the potential to enhance the understanding of both confidentiality and dissipative systems in physics.

We propose an algorithm for the visual detection and localisation of the hand of a humanoid robot. This algorithm imposes low requirements on the type of supervision required to achieve good performance. In particular the system performs feature selection and adaptation using images that are only labelled as containing the hand or not, without any explicit segmentation. Our algorithm is an online variant of Multiple Instance Learning based on boosting. Experiments in real-world conditions on the iCub humanoid robot confirm that the algorithm can learn the visual appearance of the hand, reaching an accuracy comparable with its off-line version. This remains true when supervision is generated by the robot itself in a completely autonomous fashion. Algorithms with weak supervision requirements like the one we describe are useful for autonomous robots that learn and adapt online to a changing environment. The algorithm is not hand-specific and could be easily applied to wide range of problems involving visual recognition of generic objects.

We introduce a novel type of orientation–selective rank features that are sensitive to contrast modulations (second–order stimuli). Variance Ranklets are designed in close analogy with the standard Ranklets, but use the Siegel–Tukey statistics for dispersion instead of the Wilcoxon statistics. Their response shows the same orientation selectivity pattern of Haar wavelets on second–order signals that are not detectable by linear filters. To the best of our knowledge, this is the first family of rank filters designed to detect orientation in variance modulations. We validate our descriptors with an application to texture classification over a subset of the VisTex and Brodatz databases. The combination of standard (intensity) Ranklets with Variance Ranklets greatly improves on the performance of Ranklets alone. Com-parison with other published results shows that state–of–the–art recognition rates can be achieved with a simple Nearest Neighbour classifier. 1

Ranklets are orientation selective rank features with applications to tracking, face detection, texture and medical imaging. We introduce efficient algorithms that reduce their computational complexity from O(N log N) to O(¿N + k), where N is the area of the filter. Timing tests show a speedup of one order of magnitude for typical usage, which should make Ranklets attractive for real-time applications.

We propose an accurate tracking algorithm based on a multi-feature statistical model. The model combines in a single particle filter colour and gradient-based orientation information. A reliability measure derived from the particle distribution is used to adaptively weigh the contribution of the two features. Furthermore, information from the tracker is used to set the dimension of the filters for the computation of the gradient, effectively solving the scale selection problem. Experiments over a set of real-world sequences show that the adaptive use of colour and orientation information improves over either feature taken separately, both in terms of tracking accuracy and of reduction of lost tracks. Also, the automatic scale selection for the derivative filters results in increased robustness. 2

In this paper we discuss a general framework for feature selection based on nonparametric statistics. The three stage approach we propose is based on the assumption that the available data set is representative of a certain concept and aims at learning from the data the selection of a subset of descriptive features out of a large pool of measurements. The first stage requires the computation of a large number of image features. Simple significance tests and the maximum likelihood principle are at the basis of the second stage in which a saliency measure is used to reject the features which do not appear to be descriptive of the given data set. The third and final stage, by using the Spearman independence rank test, selects a maximal number of pairwise independent features. We report experiments on a face dataset (the MIT-CBCL database) which confirm the quality and the potential of the approach.

We present a robust algorithm for point tracking on deformable objects. The key elements are the use of orientation selective rank features (ranklets), local filter adaptation and dynamic model update. A multi-scale vector of ranklets is used to encode a neighbourhood of each tracked point. The shape of the filters is optimised for each neighbourhood independently. Substantial appearance variations are catered for by maintaining a stack of models for each tracked point. This enables the system to recalibrate whenever the object reverts to its original appearance.

We study spatial learning and navigation for autonomous agents. A state space representation is constructed by unsupervised Hebbian learning during exploration. As a result of learning, a representation of the continuous two-dimensional (2-D) manifold in the high-dimensional input space is found. The representation consists of a population of localized overlapping place fields covering the 2-D space densely and uniformly. This space coding is comparable to the representation provided by hippocampal place cells in rats. Place fields are learned by extracting spatio-temporal properties of the environment from sensory inputs. The visual scene is modeled using the responses of modified Gabor filters placed at the nodes of a sparse Log-polar graph. Visual sensory aliasing is eliminated by taking into account self-motion signals via path integration. This solves the hidden state problem and provides a suitable representation for applying reinforcement learning in continuous space for action selection. A temporal-difference prediction scheme is used to learn sensorimotor mappings to perform goal-oriented navigation. Population vector coding is employed to interpret ensemble neural activity. The model is validated on a mobile Khepera miniature robot.

Ranklets are a family of multiscale, orientation-selective rank features suitable for characterising complex patterns. On square pixel lattices, ranklets bear a strong similarity to Haar wavelets. This extends to the sensitivity to horizontal and vertical edges. We propose a generalisation of ranklets to hexagonal pixels. The sixfold rotational symmetry of the lattice translates into features that are tuned to three preferential directions in the image. We present experimental results on a pattern recognition task (face detection) over a large image database, using both square and hexagonal pixels. Comparison of the results in the two cases confirms the consistent performance of the generalised ranklets. 1

Presents results of the face verification contest that was organized in conjunction with International Conference on Pattern Recognition 2000. Participants had to use identical data sets from a large, publicly available multimodal database XM2VTSDB. Training and evaluation was carried out according to an a priori known protocol. Verification results of all tested algorithms have been collected and made public on the XM2VTSDB website, facilitating large scale experiments on classifier combination and fusion. Tested methods included, among others, representatives of the most common approaches to face verification -elastic graph matching, Fisher's linear discriminant and support vector machines.

We model hippocampal place cells and head-direction cells by combin(cid:173) ing allothetic (visual) and idiothetic (proprioceptive) stimuli. Visual in(cid:173) put, provided by a video camera on a miniature robot, is preprocessed by a set of Gabor filters on 31 nodes of a log-polar retinotopic graph. Unsu(cid:173) pervised Hebbian learning is employed to incrementally build a popula(cid:173) tion of localized overlapping place fields. Place cells serve as basis func(cid:173) tions for reinforcement learning. Experimental results for goal-oriented navigation of a mobile robot are presented.