Nowadays, a large number of intelligent devices involved in the industrial Internet of Things (IIoT) environment lead to unprecedented challenges in security. Due to limited resources with weak security protection, the IIoT devices can be easily compromised to launch distributed denial-of-service (DDoS) attacks, resulting in catastrophic results. Although there are many DDoS mitigations of traditional static schemes, the proactive defense method to resist attacks has not been well studied. Furthermore, existing proactive schemes ignored the delay-sensitive characteristic of applications under the IIoT environments. To address these issues, we first adopt two kinds of moving target defense (MTD) techniques that dynamically control the admission of devices and migrate service replicas to isolate attackers on limited edge clouds and mitigate DDoS attacks early near its source. Then, we formulate a multistage optimization problem of MTD mechanisms deployment and model it as constrained Markov decision processes in order to maximize the available resources of the system under the limitations of the IIoT environments. Besides, we present an MTD optimal strategy algorithm to solve decision problems in a cost-effective manner. In this article, the proposed algorithm can achieve an optimal admission allocation by means of attackers gathering within the same service where the service migration decisions are assisted by means of value iteration. The experimental results verify that the proposed algorithm, compared with existing strategies, can effectively mitigate DDoS attacks with acceptable degradation of the quality of service.

Traffic analysis plays an essential role in network management and security protection under the premise of fully protecting user privacy. Unfortunately, encryption dramatically reduces the disclosure of traffic information, making encrypted traffic analysis more challenging than plaintext traffic analysis, especially in the environment of new encryption protocols (e.g., TLS-1.3, QUIC). The existing tensor-based methods mainly focus on optimizing packet length sequence features and introducing the latest deep learning model. However, the tensor-based features cannot sufficiently express the structured non-Euclidean Markov properties inside the encrypted traffic. This paper proposes a novel traffic graphical expression model named Weaved Flow Fragment (WFF) to transform a packet sequence into a graph, which better represents the packet sequence’s inner relationship than the tensor. WFF also considers the co-evolution relationship and the cross-direction change relationship in the bidirectional flow, breaking through the limitation that the tensor-like length sequence only considers the adjacent Markov properties. Then, we use the latest graph convolutional networks, gated graph neuron networks, and capsule graph neural networks to implement classification based on WFF, respectively. Further, to give full play to the advantages of different graph neural network classifiers to improve classification effect in large-scale data scenarios, we proposed the ensemble graph neural network architecture with several ensemble mechanisms to reduce the possibility of classification error caused by overfitting and model concerns. Experiments show that our classification effect is much better than the state-of-the-art methods (achieved 99.25% F1-score) in an open-world environment, and the model size is reduced by 99.1%.

Encrypted Traffic Classification (ETC) can provide necessary information support for network management and security. The state-of-the-art ETC methods take a single flow as the unit and only use sequence features based on in-flow relationships. In an actual network, one-time access to an application will generate multiple flows. Taking a single flow as the classification unit will produce many repeated and potentially erroneous results, which dramatically reduces the classification efficiency and prevents the results from being used for effective network management and security. In this paper, we propose a multi-flow ETC method. Since multiple flows generated by an application cannot be directly bound in a complex multi-application scenario, we first cluster the encrypted traffic to acquire flow bunches through the proposed Time Sequential Hierarchical Clustering with Sliding Windows (TSHC-SW) algorithm. Then, based on the flow bunches, we propose five different multi-flow classification schemas that can realize multi-flow classification effectively with model-independent. Open-world experiments show that our method is versatile in that it can pursue classification accuracy, speed, or sample covering rate, respectively, according to the actual demand and network environment constraints. In flow clustering, we achieve 95% adjusted Rand Index and 98% purity. In the multi-flow classification, we have over 99% F1-score, 79% prediction time saving, and 5% sample covering rate increasing, which is far superior to the state-of-the-art single-flow methods.

Encrypted traffic classification can essentially support network QoS (Quality of Service) and user QoE (Quality of Experience). However, as a typical supervised learning problem, it requires sufficiently labeled samples, which should be frequently updated. The current gateway-based labeled sample acquisition methods can only be carried out under TLS traffic. It relies on the Server Name Indication, a confused optional field that can be tampered with. The current end-based methods carried out manually or automatically have low efficiency and lack sample integrity, category purity, and label authenticity. In addition, they may have colossal packet loss and violate device security and user privacy. To solve these problems, we propose a one-stop automated encrypted traffic labeled sample collection, construction, and correlation system, A3C. First, we carry out the automated process-isolated traffic collection and labeled sample construction in the mixed application scenario, which can be used on Windows, Linux, and Android systems. Then, we propose the Segmented Entropy Distribution Capsule Neural Network (SED-CapsNet) to validate the encryption of the collected samples. We also propose optional authenticity validation and context flow correlation methods. Experimental results show that the system can effectively achieve one-stop encrypted traffic labeled dataset acquisition. It is superior to the existing methods.