Search

Recent advances in composing Cloud applications have been driven by deployments of inter-networking heterogeneous microservices across multiple Cloud datacenters. System dependability has been of the upmost importance and criticality to both service vendors and customers. Security, a measurable attribute, is increasingly regarded as the representative example of dependability. Literally, with the increment of microservice types and dynamicity, applications are exposed to aggravated internal security threats and externally environmental uncertainties. Existing work mainly focuses on the QoS-aware composition of native VM-based Cloud application components, while ignoring uncertainties and security risks among interactive and interdependent container-based microservices. Still, orchestrating a set of microservices across datacenters under those constraints remains computationally intractable. This paper describes a new dependable microservice orchestration framework GA-Par to effectively select and deploy microservices whilst reducing the discrepancy between user security requirements and actual service provision. We adopt a hybrid (both whitebox and blackbox based) approach to measure the satisfaction of security requirement and the environmental impact of network QoS on system dependability. Due to the exponential grow of solution space, we develop a parallel Genetic Algorithm framework based on Spark to accelerate the operations for calculating the optimal or near-optimal solution. Large-scale real world datasets are utilized to validate models and orchestration approach. Experiments show that our solution outperforms the greedy-based security aware method with 42.34 percent improvement. GA-Par is roughly 4× faster than a Hadoop-based genetic algorithm solver and the effectiveness can be constantly guaranteed under different application scales.

Background. An increasing number of systematic reviews/meta-analyses (SRs/MAs) of clinical trials have begun to investigate the effects of traditional Chinese medicine (TCM) nursing in patients with stroke. To systematically appraise and synthesize these results, we conducted an overview of SRs/MAs. Methods. Eight databases from their inception to April 2020 were searched to include all SRs/MAs on TCM nursing for stroke. Methodological quality assessment was performed using Assessing the Methodological Quality of Systematic Reviews 2 (AMSTAR-2) and evidence quality assessment was performed using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE). Results. Eleven SRs/MAs regarding TCM nursing for stroke were included. The assessments with AMSTAR-2 indicated that the methodological quality of all included SRs/MAs was critically low. According to the evaluation results of GRADE, 10 (40%) outcomes were rated as critically low-quality evidence, 7 (28%) low-quality evidence, and 8 (32%) moderate-quality evidence. Descriptive analysis results showed that TCM nursing was effective for stroke. Conclusions. All included SRs/MAs suggested positive findings of TCM nursing for stroke, but the credibility of the results is limited. Studies with methodologically rigorous and adequately powered are still needed in this field.

Deep learning is widely applied to various areas for its great performance. However, it is vulnerable to adversarial attacks and poisoning attacks, which arouses a lot of concerns. A number of attack methods and defense strategies have been proposed, most of which focus on adversarial attacks that happen in the testing process. Poisoning attacks, using poisoned-training data to attack deep learning models, are more difficult to defend since the models heavily depend on the training data and strategies to guarantee their performances. Generally, poisoning attacks are conducted by leveraging benign examples with poisoned labels or poison-training examples with benign labels. Both cases are easy to detect. In this paper, we propose a novel poisoning attack named Invisible Poisoning Attack (IPA). In IPA, we use highly stealthy poison-training examples with benign labels, perceptually similar to their benign counterparts, to train the deep learning model. During the testing process, the poisoned model will handle the benign examples correctly, while output erroneous results when fed by the target benign examples (poisoning-trigger examples). We adopt the Non-dominated Sorting Genetic Algorithm (NSGA-II) as the optimizer for evolving the highly stealthy poison-training examples. The generated approximate optimal examples are promised to be both invisible and effective in attacking the target model. We verify the effectiveness of IPA against face recognition systems on different face datasets, including attack ability, stealthiness, and transferability performance.

Software defined networking (SDN) is a promising network architecture, which decouples the control plane and data plane of a network. However, SDN opens some security challenges, such as man-in-the-middle attacks, spoofing attacks, flooding attacks and so on. In this paper, we focus on flooding attacks which consume the switch buffer and controller resource resulting in SDN framework resource overloaded. To prevent SDN framework from flooding attack, we present a defense approach called PBUF (Packet forwarding based on BUFfer sharing), which pools the idle switches to mitigate threat issues. This approach consists of buffer management and packet forwarding modules. The buffer management module gleans the statistics of incoming packets and then analyzes these statistics to estimate the buffer size by network calculus. Considering that a lot of table-miss packets will be generated and stored in buffer when the flooding attack is happening, the packet forwarding module is designed to forward these table-miss packets to idle switches to prevent the switch or controller to be overloaded. These table-miss packets will be buffered in idle switches and then sent to controller in a limited rate by generating packet-in messages. The simulation results show that PBUF is effective and only introduces a little overhead in SDN framework.Indexing terms: Flooding Attack; Security; Performance; SDN

The OpenFlow implementations (SDNs) have been deployed increasingly on varieties of networks in research institutions as well as commercial institutions. To develop an OpenFlow implementation, it is required to understand the performance of the network. A few benchmark tools (e.g., Cbench and OFlops) can be used to measure the network performance, while these tools take considerable time to simulate traffic behaviors and generate the required results, therefore extending the development time. In this paper, we present an analytical model, which is based on stochastic network calculus theory, for evaluating the performance of switch to controller. The previous studies show that stochastic network calculus can provide realistic emulation of real network traffic behaviors. Our model is evaluated by using both simulation tool and realistic testbed. The results show the stochastic network calculus based analysis model can realistically measure the network performance of the end-to-end properties between controller and switch.

Finding effective ways to collect the usage of network resources in all kinds of applications to ensure a distributed control plane has become a key requirement to improve the controller’s decision making performance. This paper explores an efficient way in combining dynamic NetView sharing of distributed controllers with the behavior of intra-service resource announcements and processing requirements that occur in distributed controllers, and proposes a rapid multipathing distribution mechanism. Firstly, we establish a resource collecting model and prove that the prisoner’s dilemma problem exists in the distributed resource collecting process in the Software-defined Network (SDN). Secondly, we present a bypass path selection algorithm and a diffluence algorithm based on Q-learning to settle the above dilemma. At last, simulation results are given to prove that the proposed approach is competent to improve the resource collecting efficiency by the mechanism of self-adaptive path transmission ratio of our approach, which can ensure high utilization of the total network we set up.

This paper is driven by a simple question of whether traffic engineering in Software Defined Networking (SDN) can react quickly to bursty and unpredictable changes in traffic demand. The key challenge is to strike a careful balance between the overhead (frequently involving the SDN controller) and performance (the degree of congestion measured as the maximum load and the balance between the minimum and the maximum loads). Exploiting OpenFlow (OF) features, quick shift of routing paths for unpredictable traffic bursty is the focal point of this work. It is achieved by using a dual routing scheme and letting the data plane to select the appropriate path in reacting to uncertainty in traffic load. The proposed work is called DUCE (Demand Uncertainty Configuration sElection). Further, we describe a traffic distribution model, an optimization solution that calculates congestion-free traffic distribution plan which guarantees that each switch can select one of the paths in a distributed way, and moreover, OF details about detaching the functionality of responding to the demand uncertainty from the control plane and delegating it to the data plane. Simulations are performed validating the efficiency of DUCE under various network scenarios.

Facilitated by messaging protocols (MP), many home devices are connected to the Internet, bringing convenience and accessibility to customers. However, most deployed MPs on IoT platforms are fragmented and are not implemented carefully to support secure communication. To the best of our knowledge, there is no systematic solution to perform automatic security checks on MP implementations yet. To bridge the gap, we present MPInspector, the first automatic and systematic solution for vetting the security of MP implementations. MPInspector combines model learning with formal analysis and operates in three stages: (a) using parameter semantics extraction and interaction logic extraction to automatically infer the state machine of an MP implementation, (b) generating security properties based on meta properties and the state machine, and (c) applying automatic property based formal verification to identify property violations. We evaluate MPInspector on three popular MPs, including MQTT, CoAP and AMQP, implemented on nine leading IoT platforms. It identifies 252 property violations, leveraging which we further identify eleven types of attacks under two realistic attack scenarios. In addition, we demonstrate that MPInspector is lightweight (the average overhead of end-to-end analysis is ~4.5 hours) and effective with a precision of 100% in identifying property violations., Comment: Accepted as a full paper at USENIX Security '21