Network intrusion detection systems (NIDS) to detect malicious attacks continues to meet challenges. NIDS are vulnerable to auto-generated port scan infiltration attempts and NIDS are often developed offline, resulting in a time lag to prevent the spread of infiltration to other parts of a network. To address these challenges, we use hypergraphs to capture evolving patterns of port scan attacks via the set of internet protocol addresses and destination ports, thereby deriving a set of hypergraph-based metrics to train a robust and resilient ensemble machine learning (ML) NIDS that effectively monitors and detects port scanning activities and adversarial intrusions while evolving intelligently in real-time. Through the combination of (1) intrusion examples, (2) NIDS update rules, (3) attack threshold choices to trigger NIDS retraining requests, and (4) production environment with no prior knowledge of the nature of network traffic 40 scenarios were auto-generated to evaluate the ML ensemble NIDS comprising three tree-based models. Results show that under the model settings of an Update-ALL-NIDS rule (namely, retrain and update all the three models upon the same NIDS retraining request) the proposed ML ensemble NIDS produced the best results with nearly 100% detection performance throughout the simulation, exhibiting robustness in the complex dynamics of the simulated cyber-security scenario.

翻译：为应对这些挑战,我们使用高空图通过一套互联网协议地址和目的地港口,捕捉不断变化的港口扫描攻击模式,从而产生一套基于高空的指数,以训练强有力和有复原力的混合机学习(ML)NIDS,该指数有效监测和检测港口扫描活动和对抗性入侵,同时实时进行智能演进。通过下列组合,国家航空数据系统改进了入侵实例,(2)国家航空数据系统更新规则,(3)为触发国家航空数据系统再培训请求而作出攻击门槛选择,(4)生产环境事先对网络交通性质没有了解40种情景,自动生成了这种生产环境,以评价由三种树型模型组成的多功能型国家航空数据系统。结果显示,根据更新-AL-NIDS规则的模式设置(即重新培训和根据国家航空数据系统再培训请求更新所有三种模型),通过综合:(1) 入侵实例,(2) 国家航空数据系统更新规则更新规则,规则更新规则;(2) 国家航空数据系统更新规则更新规则;(2) 国家航空数据系统更新规则更新规则规则规则;(4) 攻击规则更新规则更新规则;(4) 攻击规则更新规则更新规则更新后,以近100年期的网络安全性模拟模型模拟模型模拟模型模拟模型制作了100年期的最佳结果。