Multi-rate Threshold FlipThem

A standard method to protect data and secrets is to apply threshold cryptography in the form of secret sharing. This is motivated by the acceptance that adversaries will compromise systems at some point; and hence using threshold cryptography provides a defence in depth. The existence of such powerful adversaries has also motivated the introduction of game theoretic techniques into the analysis of systems, e.g. via the FlipIt game of van Dijk et al. This work further analyses the case of FlipIt when used with multiple resources, dubbed FlipThem in prior papers. We examine two key extensions of the FlipThem game to more realistic scenarios; namely separate costs and strategies on each resource, and a learning approach obtained using so-called fictitious play in which players do not know about opponent costs, or assume rationality

Threshold FlipThem:when the winner does not need to take all

We examine a FlipIt game in which there are multiple resources which a monolithic attacker is trying to compromise. This extension to FlipIt was considered in a paper in GameSec 2014, and was there called FlipThem. Our analysis of such a situation is focused on the situation where the attacker’s goal is to compromise a threshold of the resources. We use our game theoretic model to enable a defender to choose the correct configuration of resources (number of resources and the threshold) so as to ensure that it makes no sense for a rational adversary to try to attack the system. This selection is made on the basis of the relative costs of the attacker and the defender

FlipDyn with Control: Resource Takeover Games with Dynamics

We present the FlipDyn, a dynamic game in which two opponents (a defender and an adversary) choose strategies to optimally takeover a resource that involves a dynamical system. At any time instant, each player can take over the resource and thereby control the dynamical system after incurring a state-dependent and a control-dependent costs. The resulting model becomes a hybrid dynamical system where the discrete state (FlipDyn state) determines which player is in control of the resource. Our objective is to compute the Nash equilibria of this dynamic zero-sum game. Our contributions are four-fold. First, for any non-negative costs, we present analytical expressions for the saddle-point value of the FlipDyn game, along with the corresponding Nash equilibrium (NE) takeover strategies. Second, for continuous state, linear dynamical systems with quadratic costs, we establish sufficient conditions under which the game admits a NE in the space of linear state-feedback policies. Third, for scalar dynamical systems with quadratic costs, we derive the NE takeover strategies and saddle-point values independent of the continuous state of the dynamical system. Fourth and finally, for higher dimensional linear dynamical systems with quadratic costs, we derive approximate NE takeover strategies and control policies which enable the computation of bounds on the value functions of the game in each takeover state. We illustrate our findings through a numerical study involving the control of a linear dynamical system in the presence of an adversary.Comment: 17 Pages, 2 figures. Under review at IEEE TA

Anomaly detection in competitive multiplayer games

As online video games rise in popularity, there has been a significant increase in fraudulent behavior and malicious activity. Numerous methods have been proposed to automate the identification and detection of such behaviors but most studies focused on situations with perfect prior knowledge of the gaming environment, particularly, in regards to the malicious behaviour being identified. This assumption is often too strong and generally false when it comes to real-world scenarios. For these reasons, it is useful to consider the case of incomplete information and combine techniques from machine learning and solution concepts from game theory that are better suited to tackle such settings, and automate the detection of anomalous behaviors. In this thesis, we focus on two major threats in competitive multiplayer games: intrusion and device compromises, and cheating and exploitation. The former is a knowledge-based anomaly detection, focused on understanding the technology and strategy being used by the attacker in order to prevent it from occurring. One of the major security concerns in cyber-security are Advanced Persistent Threats (APT). APTs are stealthy and constant computer hacking processes which can compromise systems bypassing traditional security measures in order to gain access to confidential information held in those systems. In online video games, most APT attacks leverage phishing and target individuals with fake game updates or email scams to gain initial access and steal user data, including but not limited to account credentials and credit card numbers. In our work, we examine the two player game called FlipIt to model covert compromises and stealthy hacking processes in partial observable settings, and show the efficiency of game theory concept solutions and deep reinforcement learning techniques to improve learning and detection in the context of fraud prevention. The latter defines a behavioral-based anomaly detection. Cheating in online games comes with many consequences for both players and companies; hence, cheating detection and prevention is an important part of developing a commercial online game. However, the task of manually identifying cheaters from the player population is unfeasible to game designers due to the sheer size of the player population and lack of test datasets. In our work, we present a novel approach to detecting cheating in competitive multiplayer games using tools from hybrid intelligence and unsupervised learning, and give proof-of-concept experimental results on real-world datasets

Image Enhancement via Deep Spatial and Temporal Networks

Image enhancement is a classic problem in computer vision and has been studied for decades. It includes various subtasks such as super-resolution, image deblurring, rain removal and denoise. Among these tasks, image deblurring and rain removal have become increasingly active, as they play an important role in many areas such as autonomous driving, video surveillance and mobile applications. In addition, there exists connection between them. For example, blur and rain often degrade images simultaneously, and the performance of their removal rely on the spatial and temporal learning. To help generate sharp images and videos, in this thesis, we propose efficient algorithms based on deep neural networks for solving the problems of image deblurring and rain removal. In the first part of this thesis, we study the problem of image deblurring. Four deep learning based image deblurring methods are proposed. First, for single image deblurring, a new framework is presented which firstly learns how to transfer sharp images to realistic blurry images via a learning-to-blur Generative Adversarial Network (GAN) module, and then trains a learning-to-deblur GAN module to learn how to generate sharp images from blurry versions. In contrast to prior work which solely focuses on learning to deblur, the proposed method learns to realistically synthesize blurring effects using unpaired sharp and blurry images. Second, for video deblurring, spatio-temporal learning and adversarial training methods are used to recover sharp and realistic video frames from input blurry versions. 3D convolutional kernels on the basis of deep residual neural networks are employed to capture better spatio-temporal features, and train the proposed network with both the content loss and adversarial loss to drive the model to generate realistic frames. Third, the problem of extracting sharp image sequences from a single motion-blurred image is tackled. A detail-aware network is presented, which is a cascaded generator to handle the problems of ambiguity, subtle motion and loss of details. Finally, this thesis proposes a level-attention deblurring network, and constructs a new large-scale dataset including images with blur caused by various factors. We use this dataset to evaluate current deep deblurring methods and our proposed method. In the second part of this thesis, we study the problem of image deraining. Three deep learning based image deraining methods are proposed. First, for single image deraining, the problem of joint removal of raindrops and rain streaks is tackled. In contrast to most of prior works which solely focus on the raindrops or rain streaks removal, a dual attention-in-attention model is presented, which removes raindrops and rain streaks simultaneously. Second, for video deraining, a novel end-to-end framework is proposed to obtain the spatial representation, and temporal correlations based on ResNet-based and LSTM-based architectures, respectively. The proposed method can generate multiple deraining frames at a time, which outperforms the state-of-the-art methods in terms of quality and speed. Finally, for stereo image deraining, a deep stereo semantic-aware deraining network is proposed for the first time in computer vision. Different from the previous methods which only learn from pixel-level loss function or monocular information, the proposed network advances image deraining by leveraging semantic information and visual deviation between two views