SGER: Effective Network Anomaly Detection Based on Adaptive Machine Learning

项目编号：CNS 07-15342PI(s): Park， Haesun机构：Georgia Institute of Technology atlanta， GA 30332-0002标题：SGER：基于自适应机器学习的有效网络异常检测项目建议：该项目旨在开发创新的自适应机器学习算法，用于检测网络入侵，特别是异常检测，旨在提高动态变化数据集的检测率和检测速度，而无需从头开始重新计算解决方案。相反，利用现有的解决方案并使用新数据进行更新。通过在不损害数据隐私的情况下检测更多的训练数据，该算法旨在提高检测能力。这项工作解决了基于机器学习的网络异常检测方法的以下挑战性方面：自适应机器学习算法的发展。层次降维与聚类；有效利用私有入侵检测数据集的保护隐私分布式数据挖掘。前者响应数据随时间的变化，设计/创建有效的算法来删除旧数据的影响并纳入新数据，而无需重新计算解决方案。其次，针对二值问题时数据集本质上不平衡的问题，进一步推广了保簇降维方法，以反映降维中的分层簇结构。后者响应数据隐私，设计基于机器学习的异常检测算法，通过将本地生成的结果集成到一个集成解决方案中，而不泄露每个本地数据集中的关键信息，从而保护隐私。更广泛的影响：该研究产生的方法可能对非常高维空间的广泛应用产生重大影响。它们的适应性大大降低了计算复杂性，大大提高了对数据进行详细研究的可能性，而这些研究一直以来都非常昂贵。在该地区的一个HBCU机构的参与下，这位女教师PI领导了一项努力，以吸引更多的女性和少数民族学生。

Proposal #: CNS 07-15342PI(s): Park, Haesun Institution: Georgia Institute of Technology Alanta, GA 30332-0002Title: SGER: Effective Network Anomaly Detection based on Adaptive Machine LearningProject Proposed:This project, developing innovative adaptive machine learning algorithms for detecting network intrusions, especially anomaly detection, aims to increase the detection rate and speed of detection for dynamically changing data sets without recomputing the solutions from scratch. Instead, the existing solutions are utilized and updated with new data. By detecting more data in training without compromising data privacy, the algorithms are designed to increase the detection capability. The work addresses the following challenging aspects of machine learning based methods for network anomaly detection:. Development of Adaptive Machine Learning Algorithms,. Hierarchical Dimension Reduction and Clustering, and. Privacy Preserving Distributed Data Mining for Effective Utilization of Private Intrusion Detection Data Sets.The former, responding to the change of data over time, designs/creates efficient algorithms to delete the influence of old data and incorporate the new data, without recomputing the solution. The second, addressing the fact that typically data sets are intrinsically unbalanced when the problem is considered as a binary problem, generalizes further the cluster preserving dimension reduction methods to reflect the hierarchical cluster structure in dimension reduction. The latter, responding to data privacy, designs machine learning based anomaly detection algorithms by integrating locally generated results into one integrated solution without revealing the critical information in each local data set, thereby preserving privacy.Broader Impacts: The research produces methods that are likely to have great impact on a broad range of applications in very high-dimensional spaces. Their adaptability allows significant reduction in the computational complexity substantially improving the possibility of detailed study of data which has been prohibitively expensive. Involving an HBCU institution in the area, this female faculty PI leads an effort to engage more women and minority students.