Advanced API Security 使用通过 Google 的机器学习算法构建的模型来检测 API 的安全威胁。这些模型使用包含已知安全威胁的真实 API 流量数据集（包括您的当前流量数据，如果已启用）进行预训练。因此，这些模型会学习识别异常的 API 流量模式（例如 API 抓取和异常），并根据类似的模式将事件归为一组。
[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["很难理解","hardToUnderstand","thumb-down"],["信息或示例代码不正确","incorrectInformationOrSampleCode","thumb-down"],["没有我需要的信息/示例","missingTheInformationSamplesINeed","thumb-down"],["翻译问题","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC)：2025-09-03。"],[[["\u003cp\u003eThis page provides information about Advanced API Security features in Apigee and Apigee hybrid.\u003c/p\u003e\n"],["\u003cp\u003eAdvanced API Security uses detection rules, including machine learning models and descriptive rules, to identify unusual patterns in API traffic that might indicate malicious activity.\u003c/p\u003e\n"],["\u003cp\u003eThe detection rules include machine learning models like "Advanced API Scraper" and "Advanced Anomaly Detection," which are trained on real API traffic data to identify patterns indicative of security threats.\u003c/p\u003e\n"],["\u003cp\u003eOther detection rules include "Brute Guessor," "Flooder," "OAuth Abuser," "Robot Abuser," "Static Content Scraper," and "TorListRule", each targeting specific types of potential API abuse.\u003c/p\u003e\n"],["\u003cp\u003eSecurity incidents, which are groups of similar events representing security threats, can be triggered by one or multiple detection rules.\u003c/p\u003e\n"]]],[],null,["# Detection rules\n\n*This page\napplies to **Apigee** and **Apigee hybrid**.*\n\n\n*View [Apigee Edge](https://docs.apigee.com/api-platform/get-started/what-apigee-edge) documentation.*\n\nAdvanced API Security uses *detection rules* to detect unusual patterns in\nAPI traffic that could represent malicious activity. These rules include both\nmachine learning models, trained on real API data, and descriptive rules,\nbased on known types of API threats.\n| **Note:** The Advanced API Security [Abuse detection](/apigee/docs/api-security/abuse-detection) page uses detection rules to detect security incidents. A security incident is a group of events with similar patterns that could represent a security threat. Note that one incident might be triggered by multiple detection rules, in which case all of the rules that triggered the incident are listed in the Abuse detection [Environment details](/apigee/docs/api-security/abuse-detection#environment-details) view.\n\nThe following table lists the detection rules and their descriptions.\n\nAbout Advanced Anomaly Detection\n--------------------------------\n\nThe Advanced Anomaly Detection algorithm learns from your API traffic, taking into account\nfactors like error rates, traffic volume, request size, latency, geolocation, and other traffic\nmetadata at the environment level. If there are significant shifts in traffic patterns (for\nexample, a surge in traffic, error rates, or latency), the model flags the IP address that\ncontributed to the anomaly in Detected Traffic.\n| **Note:** Use of Advanced Anomaly Detection requires opting in to training the model on your API traffic data. For more information, see [Opt in for machine learning models for Abuse Detection](/apigee/docs/api-security/abuse-detection#opt-in-for-machine-learning-models-for-machine-learning).\n\nYou can also combine anomaly detection with\n[security actions](/apigee/docs/api-security/security-actions) to automatically flag\nor deny traffic that is detected as anomalous by the model. See the\n[\"Using Apigee Advanced API Security's Security Actions to Flag and Block Suspicious Traffic\"\ncommunity post](https://www.googlecloudcommunity.com/gc/Cloud-Product-Articles/Using-Apigee-Advanced-API-Security-s-Security-Actions-to-Flag/ta-p/842645) for additional information.\n\n### Model behavior\n\nTo reduce the risk that bad actors can exploit the model, we do not expose specific details\nabout how the model works or how incidents are detected. However, this additional\ninformation can help you make the best use of anomaly detection:\n\n- **Accounting for seasonal variance:**Because the model is trained on your traffic data, it can recognize and account for seasonal traffic variances (such as holiday traffic), if your traffic data includes previous data for that pattern, such as the same holiday in a previous year.\n- **Surfacing anomalies:**\n - **For existing Apigee and hybrid customers:** Apigee recommends that you have at least 2 weeks of historical API traffic data and, for more accurate results 12 weeks of historical data is preferable. Advanced Anomaly Detection starts surfacing anomalies within six hours of opting in to model training.\n - **New Apigee users:** The model starts surfacing anomalies 6 hours after opt-in, if you have a minimum of 2 weeks of historical data. However, we recommend using caution when acting on detected anomalies until the model has at least 12 weeks of data for training. The model is continuously trained on your historical traffic data so that it becomes more accurate over time.\n\n### Limitations\n\nFor Abuse Detection Advanced Anomaly Detection:\n\n- Anomalies are detected at the environment level. Anomaly detection at an individual proxy level is not supported at this time.\n- Anomaly detection is not supported for VPC-SC customers at this time.\n\nMachine learning and detection rules\n------------------------------------\n\nAdvanced API Security uses models built with Google's machine learning algorithms to\ndetect security threats to your APIs. These models are pre-trained on real\nAPI traffic data sets (including your current traffic data, if enabled) that contain known\nsecurity threats. As a result,\nthe models learn to recognize unusual API traffic patterns, such as API scraping and anomalies,\nand cluster events together based on similar patterns.\n\nTwo of the detection rules are based on machine learning models:\n\n- Advanced API Scraper\n- Advanced Anomaly Detection\n\n| **Note:** The data used to train the machine learning models for the rules Advanced API Scraper and Advanced Anomaly Detection contain metadata, including source IP address, source geography, and the values of some HTTP request headers. However, the detection data received by the models do not include the actual values of this metadata. The model makes detections based on the statistical properties of the data, not on the values of the metadata."]]
