Abstract Traffic classification is widely used in network security and network management. Early studies have mainly focused on mapping network traffic to different unencrypted applications, but little research has been done on network traffic classification of encrypted applications, especially the underlying traffic of encrypted applications. To address the above issues, this paper proposes a network encryption traffic classification model that combines attention mechanisms and spatiotemporal features. The model firstly uses the long short-term memory (LSTM) method to analyze continuous network flows and find the temporal correlation features between these network flows. Secondly, the convolutional neural network (CNN) method is used to extract the high-order spatial features of the network flow, and then, the squeeze and excitation (SE) module is used to weight and redistribute the high-order spatial features to obtain the key spatial features of the network flow. Finally, through the above three stages of training and learning, fast classification of network flows is achieved. The main advantages of this model are as follows: (1) the mapping relationship between network flow and label is automatically constructed by the model without manual intervention and decision by network features, (2) it has strong generalization ability and can quickly adapt to different network traffic datasets, and (3) it can handle encrypted applications and their underlying traffic with high accuracy. The experimental results show that the model can be applied to classify network traffic of encrypted and unencrypted applications at the same time, especially the classification accuracy of the underlying traffic of encrypted applications is improved. In most cases, the accuracy generally exceeds 90%.

With the continuous development of smart grids, communication networks carry more and more power services, and at the same time, they are also facing more and more security issues. For example, some malicious software usually uses encryption technology or tunnel technology to bypass firewalls, intrusion detection systems, etc., thereby posing a serious threat to the information security of smart grids. At present, the classification of network traffic mainly depends on the correct extraction of network protocol characteristics. However, the process of extracting network features by some traditional methods is time-consuming and overly dependent on experience. In order to solve the problem of accurate classification of power network traffic, this paper proposes a method of convolutional neural network based on genetic algorithm optimization (GACNN) and data statistical analysis. This method can simultaneously extract the time characteristics between different packet groups and the spatial characteristics in the same packet group. Therefore, it greatly saves manpower and gets rid of the dependence on experience value. The proposed method has been tested and verified on the UNSW-NB15 dataset and the real dataset collected by the power company. The results show that the proposed method can correctly classify abnormal network flows and is much better than traditional machine learning methods. In large-scale real network flow scenarios, the detection rate of the proposed method exceeds 97%, while the traditional method is generally less than 90%.

Domain knowledge of hierarchical task network (HTN) usually involves logical expressions with predicates. One needs to master two different languages which are used to describe domain knowledge and implement planner. This has presented enormous challenges for most programmers who are not familiar with logical expressions. To solve the problem a method of state variable representation from the programmer’s point of view is introduced. This method has powerful expressivity and can unify the representations of domain knowledge and planning algorithm. In Pyhop a HTN planner written in Python, methods and operators are all as ordinary Python functions rather than using a special-purpose language. Pyhop uses a Python object that contains variable bindings and does not include a horn-clause inference engine for evaluating preconditions of operators and methods. By taking a simple travel-planning problem, it shows that the method is easy to understand and integrate planning into ordinary programming.