论文标题
用机器学习算法朝着异常检测的贝叶斯优化
Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection
论文作者
论文摘要
网络攻击非常普遍，因为它们的速度增长巨大。现在，组织和个人都关心他们的保密性，完整性和关键信息的可用性，这些信息通常会受到网络攻击的影响。为此，已经开发了几种以前基于机器的入侵检测方法，以保护网络基础架构免受此类攻击。在本文中，提出了一个有效的异常检测框架，该框架利用贝叶斯优化技术与高斯内核（SVM-RBF），随机森林（RF）和K-Neareart Neignler（K-NN）算法调整支持向量机的参数。使用ISCX 2012数据集评估了考虑的算法的性能。实验结果表明，提出的框架在准确率，精度，低触觉警报率和召回率方面的有效性。
Network attacks have been very prevalent as their rate is growing tremendously. Both organization and individuals are now concerned about their confidentiality, integrity and availability of their critical information which are often impacted by network attacks. To that end, several previous machine learning-based intrusion detection methods have been developed to secure network infrastructure from such attacks. In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique to tune the parameters of Support Vector Machine with Gaussian Kernel (SVM-RBF), Random Forest (RF), and k-Nearest Neighbor (k-NN) algorithms. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
