In network measurement, learning-based sketch is a hot topic recently, which combines traditional sketches with machine learning techniques to improve the accuracy of sketches, while reducing the deployment overhead on switches. So far, most learning-based sketches estimate the sizes of either error-prone flows or all flows using machine learning models. These models take the sketch counter values of flows as features and their real sizes as labels for training. However, the flow size distribution is highly skewed, resulting in the effect that the flow sizes estimated by models are biased toward the sizes of mouse flows, severely underestimating elephant flows. To this end, a network measurement framework via back propagation neural network (BPNN) called NeuroSketch is proposed, which can directly estimate flow sizes and flow cardinality without identifying error-prone flows. Meanwhile, in order to provide effective features for BPNNs, a novel bloom filter-based sketch named BF-Sketch is proposed in this paper. BF-Sketch not only records the count values, but also the number of hash collisions in counters as a new feature, which can efficiently reduce the underestimation of elephant flows by machine learning models. The experimental results show that NeuroSketch reduces the average absolute error (AAE) of flow size estimation by 65%, and relative errors of flow cardinality estimation by 72.23%, compared with learning-based sketches. Moreover, BF-Sketch is implemented on OVS platform and P4-programmable switch to justify its feasible deployment in commodity software and hardware switches.

Congestion control is a key technology for guaranteeing quality-of-service (QoS) in Named Data Networking (NDN). Hybrid congestion control has gradually developed into the mainstream method in NDN congestion control, which capitalizes on the advantages of receiver adjusting rate and router diverting traffic to deal with congestion. However, it has to address how to effectively coordinate consumers and routers to prevent transport performance degradation caused by repeated and excessive control. In this paper, a hybrid congestion control scheme named CoopCon is proposed, which fully gives the cooperation between consumers and routers to control the congestion adaptively. Moreover, the optimal path is used resiliently by CoopCon to enhance the robustness of multipath forwarding and multicast data delivery in NDN. The proposed CoopCon is implemented in ndnSIM. And simulation results show that CoopCon consistently achieves higher total throughput than existing work. In particular, the total throughput of consumers deployed with CoopCon is 19.4% higher than that of consumers deployed with PCON in the BRITE-generated topology. Additionally, CoopCon also achieves the best fairness in a dumbbell topology, with a fairness index of even 0.96.

Auditing security compliance with respect to security standards and policies becomes increasingly important in clouds for ensuring the transparency and accountability of a cloud provider to its tenants. However, security auditing in clouds encounters various challenges in the scalability and response time due to the large-scale cloud size and the high operational complexity. Existing approaches cannot verify the legitimacy of user requests in proper response time at runtime for a large cloud. To this end, this paper proposes a novel security auditing framework named Deep lEArning-based user-level Proactive Security auditing (DeaPS) for clouds, which leverages the Long Short-Term Memory (LSTM) neural network to automatically learn user behavior patterns from historical events and notify for possible critical events causing violations. Our solution implements the costly verification in advance for reducing the runtime response time to a realistic level. We evaluate our approach by integrating DeaPS into OpenStack and extensive experiments show that DeaPS exhibits excellent performance in large-scale clouds and outperforms other existing security auditing methods.