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NSNAD: negative selection-based network anomaly detection approach with relevant feature subset

机译：NSNAD：基于负选择的网络异常检测方法，具有相关特征子集

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摘要

Intrusion detection systems are one of the security tools widely deployed in network architectures in order to monitor, detect and eventually respond to any suspicious activity in the network. However, the constantly growing complexity of networks and the virulence of new attacks require more adaptive approaches for optimal responses. In this work, we propose a semi-supervised approach for network anomaly detection inspired from the biological negative selection process. Based on a reduced dataset with a filter/ranking feature selection technique, our algorithm, namely negative selection for network anomaly detection (NSNAD), generates a set of detectors and uses them to classify events as anomaly. Otherwise, they are matched against an Artificial Human Leukocyte Antigen in order to be classified as normal. The accuracy and the computational time of NSNAD are tested under three intrusion detection datasets: NSL-KDD, Kyoto2006+ and UNSW-NB15. We compare the performance of NSNAD against a fully supervised algorithm (Naive Bayes), an unsupervised clustering algorithm (K-means) and a semi-supervised algorithm (One-class SVM) with respect to multiple accuracy metrics. We also compare the time incurred by each algorithm in training and classification stages.
机译：入侵检测系统是在网络架构中广泛部署的安全工具之一，以便监视，检测和最终响应网络中的任何可疑活动。然而，网络的不断增长的复杂性和新攻击的毒力需要更适应性的最佳反应方法。在这项工作中，我们提出了一种来自生物负选择过程的网络异常检测的半监督方法。基于具有滤波器/排名特征选择技术的缩小数据集，我们的算法，即网络异常检测（NSNAD）的否定选择，生成一组检测器，并使用它们将事件分类为异常。否则，它们与人工人白细胞抗原匹配，以便被分类为正常。 NSNAD的准确性和计算时间在三个入侵检测数据集下进行测试：NSL-KDD，KYOTO2006 +和UNSW-NB15。我们将NSNAD对完全监督算法（天真贝叶斯）的性能进行比较，无监督的聚类算法（K-MEAL）和关于多精度度量的半监督算法（单级SVM）。我们还比较每种算法在训练和分类阶段产生的时间。

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