Mingyue Zhang

Peilin Liu, Yanni Tang, Mingyue Zhang et al.

Graph learning has a wide range of applications in many scenarios, which require more need for data privacy. Federated learning is an emerging distributed machine learning approach that leverages data from individual devices or data centers to improve the accuracy and generalization of the model, while also protecting the privacy of user data. Graph-federated learning is mainly based on the classical federated learning framework i.e., the Client-Server framework. However, the Client-Server framework faces problems such as a single point of failure of the central server and poor scalability of network topology. First, we introduce the decentralized framework to graph-federated learning. Second, determine the confidence among nodes based on the similarity of data among nodes, subsequently, the gradient information is then aggregated by linear weighting based on confidence. Finally, the proposed method is compared with FedAvg, Fedprox, GCFL, and GCFL+ to verify the effectiveness of the proposed method. Experiments demonstrate that the proposed method outperforms other methods.

Mingyue Zhang, Nianyu Li, Sridhar Adepu et al.

The increasing prevalence of security attacks on software-intensive systems calls for new, effective methods for detecting and responding to these attacks. As one promising approach, game theory provides analytical tools for modeling the interaction between the system and the adversarial environment and designing reliable defense. In this paper, we propose an approach for securing software-intensive systems using a rigorous game-theoretical framework. First, a self-adaptation framework is deployed on a component-based software intensive system, which periodically monitors the system for anomalous behaviors. A learning-based method is proposed to detect possible on-going attacks on the system components and predict potential threats to components. Then, an algorithm is designed to automatically build a \emph{Bayesian game} based on the system architecture (of which some components might have been compromised) once an attack is detected, in which the system components are modeled as independent players in the game. Finally, an optimal defensive policy is computed by solving the Bayesian game to achieve the best system utility, which amounts to minimizing the impact of the attack. We conduct two sets of experiments on two general benchmark tasks for security domain. Moreover, we systematically present a case study on a real-world water treatment testbed, i.e. the Secure Water Treatment System. Experiment results show the applicability and the effectiveness of our approach.

Mingyue Zhang, Jialong Li, Haiyan Zhao et al.

A self-learning adaptive system (SLAS) uses machine learning to enable and enhance its adaptability. Such systems are expected to perform well in dynamic situations. For learning high-performance adaptation policy, some assumptions must be made on the environment-system dynamics when information about the real situation is incomplete. However, these assumptions cannot be expected to be always correct, and yet it is difficult to enumerate all possible assumptions. This leads to the problem of incomplete-information learning. We consider this problem as multiple model problem in terms of finding the adaptation policy that can cope with multiple models of environment-system dynamics. This paper proposes a novel approach to engineering the online adaptation of SLAS. It separates three concerns that are related to the adaptation policy and presents the modeling and synthesis process, with the goal of achieving higher model construction efficiency. In addition, it designs a meta-reinforcement learning algorithm for learning the meta policy over the multiple models, so that the meta policy can quickly adapt to the real environment-system dynamics. At last, it reports the case study on a robotic system to evaluate the adaptability of the approach.

Mingyue Zhang

Security attacks present unique challenges to self-adaptive system design due to the adversarial nature of the environment. However, modeling the system as a single player, as done in prior works in security domain, is insufficient for the system under partial compromise and for the design of fine-grained defensive strategies where the rest of the system with autonomy can cooperate to mitigate the impact of attacks. To deal with such issues, we propose a new self-adaptive framework incorporating Bayesian game and model the defender (i.e., the system) at the granularity of components in system architecture. The system architecture model is translated into a Bayesian multi-player game, where each component is modeled as an independent player while security attacks are encoded as variant types for the components. The defensive strategy for the system is dynamically computed by solving the pure equilibrium to achieve the best possible system utility, improving the resiliency of the system against security attacks.