经济代写|博弈论代写Game Theory代考|Data Processing

Doug I. Jones

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经济代写|博弈论代写Game Theory代考|Data Processing

Records from the Censys database are stored using JSON documents with deeply nested fields, containing location and ownership (AS) properties, as well as attributes extracted from parsed responses, including headers, banners, certificate chains, and so on. However, while these documents contain a wide range of characteristics about Internet hosts, the information cannot be fed into a classification model out of the box, and we need to convert these documents to numerical feature vectors for analysis by a machine learning model.

JSON documents follow a tree-like structure, allowing different fields to be nested inside one another, e.g., properties regarding the location of a host, including country, city, latitude, and longitude. Therefore, simply extracting tokens from the string corresponding to a JSON document fails to recognize its structure, and does not provide any information about the field from which the token was extracted.

To address the above problem, we use the approach developed by Sarabi and Liu (2018) to extract high-dimensional binary features vectors from these documents. This feature extraction algorithm first learns the schema (JSON Schema 2021) of JSON documents in the Censys database by inspecting a number of sample documents and then extracts binary features from each field according to the learned schema. This then produces features that can be attributed to fields of the original JSON documents and are extracted according to the data type of those fields (i.e., string, categorical, Boolean). Furthermore, for optional fields we can also generate features that reflect their existence in a document, e.g., open ports, or if a host is returning headers/banners for different protocols.
Figure 21.1 shows an example of how a JSON document can be transformed into a binary vector representation using this approach. Note that each generated feature is assigned to a certain field of the original JSON document, allowing us to separate features extracted from location and ownership (AS) information, as well as features extracted from different port responses. This allows us to gradually add the information of scanned ports to our models for performing predictions of remaining ports.

We train the feature extraction model from Sarabi and Liu (2018) on one million randomly drawn records from the 1/1/2019 Censys snapshot (chosen independently from the dataset detailed in Section 21.2.2), producing 14443 binary features extracted from 37 different ports. To control the number of generated features, we impose a limit of $0.05 \%$ on the sparsity of extracted features. These features are in the form of tags assigned to a host, e.g., if a host responds to probes on a certain port, if it belongs to a particular country, or if we observe certain tokens in fields inside the document, e.g., AS names, headers/banners, etc.

We exclude features that are extracted from Censys documents’ metadata, which are added by Censys by processing the information gathered from all scanned ports and cannot be assigned to a certain port. We further remove 11 ports that have been observed on less than $0.3 \%$ of active IP addresses, since we cannot collect enough samples on these ports for training robust models. We also remove port 3389 (RDP protocol), observed on $1.9 \%$ of active IPs, due to poor prediction performance, indicating that our feature set is not effective in predicting responses for this port. After pruning the feature set we obtain 13679 features from 20 ports, as well as location and AS properties, for training/evaluating our framework. Table 21.1 contains the fields/ports used for our analysis, as well as the number of features extracted from each field, and frequencies of active/open ports among active IP addresses.

经济代写|博弈论代写Game Theory代考|Features for Model Training

For training a model, we first produce labels for each port of an IP address by observing whether Censys has reported a response under said port for its record of that IP address. Note that for an inactive IP, all the produced labels are zero, meaning that no port is responding to requests. We then use different subsets of the binary features discussed in Section 21.2 .3 for training binary classifiers, as detailed below.

Prescan features These include features extracted from location and AS properties, which are available before performing any scans. These features provide a priori information about each host, which can be used as initial attributes for predicting port responses. Location information can help detect patterns in the behavior of IPs in different regions, while AS properties can help predict labels based on the type/owner of the IP address. For instance, observing the word “university” in an AS name can indicate an educational network, while “cable” can help recognize residential/ISP networks.

Postscan features Assuming that probes are performed sequentially, classifiers can also leverage features extracted from previous probes of an IP address for predicting the responses of the remaining ports. These then provide a posteriori features for classification. Note that using a stateless scanner such as ZMap (Durumeric et al. 2013), we only record whether a host has responded on a certain port, resulting in a single binary feature. However, with a stateful scan such as ZGrab (The ZMap Project 2021), a full handshake is completed with the server, and subsequent classifiers can also make use of parsed responses, resulting in a richer feature set. We evaluate both of these cases to determine the improvement provided by machine learning for stateless and stateful scans.

博弈论代考

经济代写|博弈论代写Game Theory代考|Data Processing

JSON 文档遵循树状结构，允许不同的字段相互嵌套，例如，有关主机位置的属性，包括国家、城市、纬度和经度。因此，简单地从对应于 JSON 文档的字符串中提取标记无法识别其结构，并且不会提供有关提取标记的字段的任何信息。

有限元方法代写

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MATLAB代写

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