Abstract
With the continuous development of the network, the number of network assets continues to increase. Despite the convenience diversified network assets bring, it also poses new challenges to IP-based network asset management. Traditional asset discovery technologies mainly analyze network traffic, and detect relevant information (operating system, running software, etc.) of IP-based assets through methods such as active discovery, passive discovery, and discovery methods based on cyberspace search engines. These methods assign the same weight to all network IP-based network assets, and it is difficult to effectively analyze diversified network assets. In this paper, we propose the concept of IP-based core network assets, and collect the data of the relevant network assets based on this concept. Then, we construct a dataset and establish feature engineering for data preprocessing. As there is currently no relevant IP-based core network asset detection method, we propose an IP-based core network asset discovery technology based on pretraining of multiple autoencoders, MAE-CAD. The results show that our method can achieve 95.74% in Acc and 95.04% in F1 in the experimental environment (Acc = 98.11% and F1 = 97.16% in the actual network environment because of duplicate samples). In addition, MAE-CAD has excellent robustness. In an environment where the proportion of data is extremely unbalanced, when the IP-based core network asset data in the training set only accounts for 1/200 (0.5%), MAE-CAD can still obtain 92.91% in Acc and 91.57% in F1.
Highlights
With the development of network technology, more and more social activities rely on the network
Traditional network asset discovery refers to the discovery of the operating system version, software version, printer model, and other asset information behind the IP or domain name through network data such as traffic and company registration information to provide information for other subsequent activities [5–7]. ere are three main types of network asset discovery technologies: active discovery [8], passive discovery [7] and discovery based on network security search engines [9]
(3) We propose an IP-based core network asset discovery technology based on neural network pretraining, MAE-CAD, which can automatically further extract and perform abstract fitting from the features constructed in feature engineering
Summary
With the development of network technology, more and more social activities rely on the network. Many diversified network assets provide various services for people’s production and life, which brings great convenience for people, and at the same time poses higher requirements for cyber security [2–4]. Erefore, comprehensive network asset discovery is crucial for network managers to effectively manage assets, and it is valuable for threat analysis and the establishment of a more complete network asset protection mechanism. Ere are three main types of network asset discovery technologies: active discovery [8], passive discovery [7] and discovery based on network security search engines [9]. With the continuous development of networks, such as the further improvement of 5G and IPv6 technologies, the scale of information is increasing, and network assets are constantly increasing. With the continuous development of networks, such as the further improvement of 5G and IPv6 technologies, the scale of information is increasing, and network assets are constantly increasing. ese assets often have different
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