SGER: Effective Network Anomaly Detection Based on Adaptive Machine Learning

提案编号：CNS 07- 15342 PI： 院校：格鲁吉亚理工学院 Alanta，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：该项目开发用于检测网络入侵的创新自适应机器学习算法，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。而是利用现有的解决方案并使用新数据进行更新。通过在训练中检测更多数据而不损害数据隐私，这些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。分层降维和聚类。隐私保护分布式数据挖掘有效利用私有入侵检测数据集前者，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合并新数据，而无需重新计算解决方案。第二，解决的事实，通常数据集本质上是不平衡的，当问题被认为是一个二元问题，进一步推广的集群保持降维方法，以反映层次聚类结构的降维。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的解决方案中，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会对非常高维空间中的广泛应用产生巨大影响。它们的适应性允许显着降低计算复杂性，大大提高了对数据进行详细研究的可能性，而这些数据一直非常昂贵。涉及HBCU机构在该地区，这位女教师PI领导的努力，让更多的妇女和少数民族学生。

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.