Biblio

A new Domain Name System (DNS) cache poisoning attack aiming at clients has emerged recently. It induced cloud users to visit fake web sites and thus reveal information such as account passwords. However, the design of current DNS defense architecture does not formally consider the protection of clients. Although the DNS traffic encryption technology can alleviate this new attack, its deployment is as slow as the new DNS architecture. Thus we propose a lightweight adaptive intelligent defense strategy, which only needs to be deployed on the client without any configuration support of DNS. Firstly, we model the attack and defense process as a static stochastic game with incomplete information under bounded rationality conditions. Secondly, to solve the problem caused by uncertain attack strategies and large quantities of game states, we adopt a deep reinforcement learning (DRL) with guaranteed monotonic improvement. Finally, through the prototype system experiment in Alibaba Cloud, the effectiveness of our method is proved against multiple attack modes with a success rate of 97.5% approximately.

As massive research efforts are poured into server-side DNS security enhancement in online cloud service platforms, sophisticated APTs tend to develop client-side DNS attacks, where defenders only have limited resources and abilities. The collaborative DNS attack is a representative newest client-side paradigm to stealthily undermine user cache by falsifying DNS responses. Different from existing static methods, in this paper, we propose a moving target defense solution named multi-vNIC intelligent mutation to free defenders from arduous work and thwart elusive client-side DNS attack in the meantime. Multiple virtual network interface cards are created and switched in a mutating manner. Thus attackers have to blindly guess the actual NIC with a high risk of exposure. Firstly, we construct a dynamic game-theoretic model to capture the main characteristics of both attacker and defender. Secondly, a reinforcement learning mechanism is developed to generate adaptive optimal defense strategy. Experiment results also highlight the security performance of our defense method compared to several state-of-the-art technologies.

Generating adversarial samples is gathering much attention as an intuitive approach to evaluate the robustness of learning models. Extensive recent works have demonstrated that numerous advanced image classifiers are defenseless to adversarial perturbations in the white-box setting. However, the white-box setting assumes attackers to have prior knowledge of model parameters, which are generally inaccessible in real world cases. In this paper, we concentrate on the hard-label black-box setting where attackers can only pose queries to probe the model parameters responsible for classifying different images. Therefore, the issue is converted into minimizing non-continuous function. A black-box approach is proposed to address both massive queries and the non-continuous step function problem by applying a combination of a linear fine-grained search, Fibonacci search, and a zeroth order optimization algorithm. However, the input dimension of a image is so high that the estimation of gradient is noisy. Hence, we adopt a zeroth-order optimization method in high dimensions. The approach converts calculation of gradient into a linear regression model and extracts dimensions that are more significant. Experimental results illustrate that our approach can relatively reduce the amount of queries and effectively accelerate convergence of the optimization method.