SGER: Effective Network Anomaly Detection Based on Adaptive Machine Learning

SGER：基于自适应机器学习的有效网络异常检测

• 批准号: 0715342
• 负责人: Haesun Park
• 金额: $ 9万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0715342&HistoricalAwards=false
• 关键词: SGER; Effective; Network; Anomaly; Detection

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.

提案＃：CNS 07-15342PI（S）：帕克，Haesun机构：佐治亚州技术研究所Alanta，GA 30332-0002TITLE：SGER：SGER：SGER：基于自适应机器学习项目的有效网络异常检测提议：该项目，该项目，为开发创新的机器学习量的检测，特别是针对检测机器的估计量，尤其是检测到的目标，特别是检测的范围。检测动态更改数据集的情况，而无需从头开始重新计算解决方案。相反，现有的解决方案被使用并使用新数据更新。通过在不损害数据隐私的情况下检测培训中的更多数据，该算法旨在提高检测能力。该工作涉及基于机器学习的网络异常检测方法的以下具有挑战性的方面：。自适应机器学习算法的开发。层次降低和聚类，以及。保留分布式数据挖掘以有效利用私人入侵检测数据集。前者响应随着时间的推移而响应数据的变化，设计/创建有效的算法，以删除旧数据的影响并结合新数据，而无需重新计算解决方案。第二个问题是，当问题被认为是二进制问题时，通常数据集通常在本质上是不平衡的，因此进一步概括了群集保留尺寸缩小方法，以反映降低维度的层次结构结构。后者响应数据隐私，通过将本地生成的结果集成到一个集成的解决方案中，而无需揭示每个本地数据集中的关键信息，从而确保了隐私的影响：研究产生的方法可能会对在很高的高度范围的应用中产生很大的影响。它们的适应性可显着降低计算复杂性，从而大大提高了对数据非常昂贵的数据的详细研究的可能性。这位女教师PI涉及该地区的HBCU机构，努力吸引更多的妇女和少数族裔学生。