Over-the-top (OTT) applications mainly communicate through HTTPS, the most famous encryption protocol family on the Internet. The classification of HTTPS encrypted traffic can effectively obtain fine-grained OTT application information for network management and cyber security. As the most expressive feature, the side-channel length sequence is widely used by current research, especially the packet length sequence. However, these attempts ignored interferences from protocol piecewise decoupling and encryption covering, leading to poor performance. Based on the application layer feature engineering theory, we proposed a new metric called Application Data Unit (ADU) length to eliminate the interferences. However, ADU length cannot be obtained directly from packets as the TLS encryption protocol covers the entire application layer, which contains an intrusive and variable HTTP header. Hence, we designed a Length-Correction Multiple Regression Neural Network (LCMRNN) algorithm to restore the real ADU length sequences accurately. Exhaustive experiments in two scenarios of the real CERNET network show that no matter the HTTP-1.1 or HTTP2.0 protocol, the LC-MRNN model can achieve significantly accurate ADU length restoration. In classification, with the assistance of the LS-LSTM classifier, our method outperforms the state-of-the-art methods with about 4.2% improvement in F1-score (93.52%).

With the extensive development of SDN technology, the scalability and reliability of SDN multi-control have gradually attracted people's attention. This paper designs the two-layer trusted multi-controller architecture based on multi-chain, and relies on the decentralization, decision-making credibility, and high-performance guarantee provided by the architecture as the core foundation to design the multi-controller scalability mechanism and reliability mechanism. As the core component of the distributed system, multi-chain provides the basic control layer with the CFT blockchain as a trusted distributed coordination component, and provides the dynamic decision layer with the BFT blockchain to ensure the multi-party participation and availability of decision-making processes such as abnormal detection and recovery. It ensures the decentralization and high performance of the cluster by dynamically adjusting the members of the decision-making layer. The scalability mechanism includes the initial load balancing mechanism(ILBM) and fine-grained dynamic load balancing mechanism, the former is responsible for the prevention of uneven load, and the latter is responsible for the detection and recovery of partial overload and global overload. The reliability solution is responsible for the detection and recovery of controller crush and link failure exceptions. Both types of mechanisms rely on the exception detection mechanism based on switch reporting(EDSR) to achieve efficient and reliable multitype exception detection. Finally, the simulation results show that the ILBM can prevent the overload of the controller, and the two-layer trusted multi-controller architecture can ensure high performance while providing credibility and decentralization for decision-making.

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.