Biblio

Hyperbox classifier is an efficient tool for modern pattern classification problems due to its transparency and rigorous use of Euclidian geometry. Fuzzy min-max (FMM) network efficiently implements the hyperbox classifier, and has been modified several times to yield better classification accuracy. However, the obtained accuracy is not up to the mark. Therefore, in this paper, a new improved FMM (IFMM) network is proposed to increase the accuracy rate. In the proposed IFMM network, a modified constraint is employed to check the expandability of a hyperbox. It also uses semiperimeter of the hyperbox along with k-nearest mechanism to select the expandable hyperbox. In the proposed IFMM, the contraction rules of conventional FMM and enhanced FMM (EFMM) are also modified using semiperimeter of a hyperbox in order to balance the size of both overlapped hyperboxes. Experimental results show that the proposed IFMM network outperforms the FMM, k-nearest FMM, and EFMM by yielding more accuracy rate with less number of hyperboxes. The proposed methods are also applied to histopathological images to know the best magnification factor for classification.

This paper considers the problem of the quickest detection of a change in distribution while taking privacy considerations into account. Our goal is to sanitize the signal to satisfy information privacy requirements while being able to detect a change quickly. We formulate the privacy-aware quickest change detection (QCD) problem by including a privacy constraint to Lorden's minimax formulation. We show that the Generalized Likelihood Ratio (GLR) CuSum achieves asymptotic optimality with a properly designed sanitization channel and formulate the design of this sanitization channel as an optimization problem. For computational tractability, a continuous relaxation for the discrete counting constraint is proposed and the augmented Lagrangian method is applied to obtain locally optimal solutions.

This paper presents a novel game theoretic attack-defence decision making framework for cyber-physical system (CPS) security. Game theory is a powerful tool to analyse the interaction between the attacker and the defender in such scenarios. In the formulation of games, participants are usually assumed to be rational. They will always choose the action to pursuit maximum payoff according to the knowledge of the strategic situation they are in. However, in reality the capacity of rationality is often bounded by the level of intelligence, computational resources and the amount of available information. This paper formulates the concept of bounded rationality into the decision making process, in order to optimise the defender's strategy considering that the defender and the attacker have incomplete information of each other and limited computational capacity. Under the proposed framework, the defender can often benefit from deviating from the minimax Nash Equilibrium strategy, the theoretically expected outcome of rational game playing. Numerical results are presented and discussed in order to demonstrate the proposed technique.

Inference of unknown opinions with uncertain, adversarial (e.g., incorrect or conflicting) evidence in large datasets is not a trivial task. Without proper handling, it can easily mislead decision making in data mining tasks. In this work, we propose a highly scalable opinion inference probabilistic model, namely Adversarial Collective Opinion Inference (Adv-COI), which provides a solution to infer unknown opinions with high scalability and robustness under the presence of uncertain, adversarial evidence by enhancing Collective Subjective Logic (CSL) which is developed by combining SL and Probabilistic Soft Logic (PSL). The key idea behind the Adv-COI is to learn a model of robust ways against uncertain, adversarial evidence which is formulated as a min-max problem. We validate the out-performance of the Adv-COI compared to baseline models and its competitive counterparts under possible adversarial attacks on the logic-rule based structured data and white and black box adversarial attacks under both clean and perturbed semi-synthetic and real-world datasets in three real world applications. The results show that the Adv-COI generates the lowest mean absolute error in the expected truth probability while producing the lowest running time among all.

We consider the estimation of a scalar state based on m measurements that can be potentially manipulated by an adversary. The attacker is assumed to have full knowledge about the true value of the state to be estimated and about the value of all the measurements. However, the attacker has limited resources and can only manipulate up to l of the m measurements. The problem is formulated as a minimax optimization, where one seeks to construct an optimal estimator that minimizes the “worst-case” expected cost against all possible manipulations by the attacker. We show that if the attacker can manipulate at least half the measurements (l ≥ m/2), then the optimal worst-case estimator should ignore all measurements and be based solely on the a-priori information. We provide the explicit form of the optimal estimator when the attacker can manipulate less than half the measurements (l <; m/2), which is based on (m2l) local estimators. We further prove that such an estimator can be reduced into simpler forms for two special cases, i.e., either the estimator is symmetric and monotone or m = 2l + 1. Finally we apply the proposed methodology in the case of Gaussian measurements.