Privacy protection and poisoning resilience are important concerns for federated learning (FL). During a relatively long period, the corresponding solutions are regarded as orthogonal and investigated separately. Unfortunately, due to the increasingly complex structure and ever-growing parameter dimensions of the models to be trained, the forthright coupling of existing differential privacy and Byzantine resilience techniques has been proved incompatible with FL. This emerging problem prompts us to give serious thought to jointly guaranteeing data privacy and model integrity. Besides, worse still, the multi-task characteristic and data imbalance of heterogeneous FL inevitably introduce huge variances, which make privacy-preserving under acceptable accuracy loss even more complicated, not to mention efficient and agile poisoning resistance. Against this bothersome situation, we propose a community-oriented secure heterogeneous FL (CoS-HFL) framework to provide guaranteed privacy protection and significant model robustness simultaneously. CoS-HFL includes two parts: community-oriented duplex privacy amplification and credit-based poisoning resistance. The former copes with potential leakage threats with both uplink and downlink obfuscations. The latter further actively thwarts poisoning attacks based on credibility evaluation. Furthermore, we conduct experiments on benchmark datasets to highlight the performance of CoS-HFL in terms of privacy amplification, poisoning resistance, and learning accuracy under adversarial and heterogeneous environments.

Kubernetes (K8s) has become a core technology for cloud-native applications. However, a design flaw of the external IP in K8s leads to the service-oriented man-in-the-middle attack. Existing solutions (e.g., script monitor) attempt to address it passively, which allows attackers enough analysis time to bypass these static rule reviews. Differently, we propose a mutation-enabled proactive defense mechanism, aiming to change the asymmetry between attackers and defenders. It involves the address mutation (i.e., network identification) module and the connection ID (i.e., communication identification) mutation module. In the former module, we analyze mutation constraints and prove the corresponding mutation grouping problem to be NP-hard. Then, a maximally coloring-driven mutation grouping algorithm is developed. Since the address allocation time grows linearly with the service size, we design a prefetched address allocation algorithm. After designing the interaction flow between modules, we present a randomized algorithm in the latter module. Thus our mechanism does not affect methods oriented to other attacks. Eventually, it can continuously interrupt the attack and keep the service connection by incrementally updating K8s and the transport layer protocol. Experiments in the Alibaba cloud demonstrate that it can effectively defend against the attack with an acceptable performance loss.

Intel software guard extensions (SGX) technology allows cloud vendors to provide customers with an independent and trusted execution environment (TEE). It protects critical data confidentiality and integrity from malicious software. However, more and more SGX side-channel attacks have appeared, which seriously undermine the confidence of tenants in cloud security. The related research focuses on system hardware and SGX compiler solutions for specific attacks, which also has difficulties in deployment. Differently, we propose an intelligent-driven proactive defense strategy, which is based on live migration. To the best of our knowledge, this is the first proactive defense against SGX side-channel attacks. We adopt the Markov decision process to solve the migration programming problem. The innovative deep reinforcement learning (DRL) solves problems of the unknown state transition probability and large machine load states, which is called self-checking proximal policy optimization (SPPO). It changes the reward pattern, improving the convergence speed and stability of DRL. In prototype experiments, we deploy the strategy in the OpenStack platform agilely to prove the defense performance and low virtual machine costs.

A new class of poisoning attacks has recently emerged targeting the client-side Domain Name System (DNS) cache. It allows users to visit fake websites unconsciously, thereby revealing their information, such as passwords. However, the current DNS defense architecture does not include DNS clients. Although relative encryption solutions can mitigate this attack, they require the cooperation of multiple parties, and the deployment speed is slow. Therefore, we propose an intelligent-driven proactive defense strategy. First, we model the offensive and defensive process as a stochastic game based on moving target defense. Second, we adopt and optimize Proximal Policy Optimization (PPO), a deep reinforcement learning method, to solve problems caused by uncertain attack strategies and unknown state transition probability. Third, we design a self-checking component in PPO to solve the uncertainty of action space caused by game state constraints based on our previous work. Thus the convergence speed and stability of PPO are improved. Finally, to the best of our knowledge, we are the first to game with intelligent attackers besides three conventional ones. Our strategy does not require any modifications to the DNS architecture. Through an extensive experimental campaign, the prototype system is proved to be effective against multiple attack modes. Its success rate is 98.5% approximately, and network round-trip time is about 55 ms. Even for random attackers, our method can achieve the theoretical maximum defensive success rate.

In today's “Internet of Everything (IoE)” era, the collaboration from massive participants significantly boosts the performance and efficiency of model training. This trend also un-avoidably stirs up considerable concerns about multi-dimensional security problems. Under the circumstances, federated learning (FL) is enthusiastically adopted, as it protects privacy to a certain extent by only processing personal data locally. Nevertheless, FL's characteristics of concealment also pave the way for sev-eral emerging attacks during the training process, i.e., model inversion, poisoning, and backdoor. Currently, although partially mitigating attack effects, existing countermeasures against those threats are studied separately and orthogonal. This separation makes those defense methods mutually exclusive and restrictive in real-world application scenarios, far from satisfying. In this paper, we extensively model different attack paradigms into three types based on CIA-triad, the well-known information security primitive, and propose a novel dual-shuffle method to thwart aforementioned threats jointly. Concretely speaking, our primary model shuffling mechanism provides the confidentiality guarantee based on the information-theoretic notion of identifiability; then, an augmented client shuffling mechanism purges the user group of adversaries proactively without any compromise of anonymous constraints. By conducting a series of experiments on bench-mark datasets, we demonstrate that our method could achieve significant security and convergence performance against three state-of-the-art attacks.

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.

In the multimedia cloud platform, the resource scheduling performance directly affects the energy consumption, resource utilization of the active physical machine (PM) and virtual machine (VM) security. Besides, service level agreement (SLA) violation rate also fluctuates with the strategy. Many optimization methods have been launched to cope with this scheduling task, while none of them accommodate all the above aspects in a uniform manner to our best knowledge. In this paper, aiming at optimizing the four sides performance, we propose a new resource scheduling strategy called Security Enhanced Particle Swarm Optimization (SE-PSO) based on VM migration which uses Particle Swarm Optimization (PSO) as a kernel part. Firstly, the inertia factor and the learning factor are dynamically adapted to improve the search performance of SE-PSO. Then, by periodically predicting physical hotspots with the exponential smoothing model, we reduce unnecessary migrations and thus minimize the VM migration security risk. Finally, roulette wheel idea is applied to achieve long-term optimization of the platform resources. The experiments conducted in CloudSim with real-world dataset also show that SE-PSO has a good overall performance in energy consumption, resource utilization, SLA violation rate and migration security compared with the mainstream PSO algorithm.