Collaborative Research: Real-Time Data-Driven Anomaly Detection for Complex Networks

协作研究：复杂网络的实时数据驱动异常检测

• 批准号: 2040572
• 负责人: Yasin Yilmaz
• 金额: $ 22.5万
• 依托单位: University of South Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2040572&HistoricalAwards=false
• 关键词: Collaborative; Research; Real; Time; Data

Anomaly detection is an important problem dealing with the detection of abnormal data patterns. Importance of anomaly detection lies in the fact that an anomaly in the observed data may be a sign of an unwanted and often actionable event such as failure, malicious activity, etc. in the underlying system. In many real-time systems, timely and accurate detection of abnormal data patterns is crucial, and will allow proper countermeasures to be taken in a timely manner, to counteract any possible harm. Although anomaly detection has long been studied, today's complex networks exhibit new challenges, such as: low latency requirements, data size, system dynamics, unknown distributions, distributed nature, and privacy. The objective of this proposal is to investigate effective and scalable approaches for real-time data-driven anomaly detection in complex systems with these challenges. The main themes of this proposal address multiple important problems in the early detection of anomalies and attacks in a general complex network setting. Considering the importance of cybersecurity in today's world, methodologies to understand and forewarn changes in the organizational dynamics of such complicated networks is of immense significance. This proposal directly addresses these issues by bringing a fresh and novel set of engineering tools and ideas.Following a systematic approach, this project first considers (1) how to timely detect anomalies in centralized high-dimensional systems with dynamicity and hidden anomaly challenges; (ii) how to deal with resource constraints in monitoring distributed systems; and (iii) how to enable privacy-preserving solutions for real-time anomaly detection in distributed systems. These challenges and the solution methods presented in this project are generally applicable to a variety of complex systems. To be specific, this project focuses on two challenging IoT networks: surveillance camera network and smart home network. The proposed approaches exploit an array of advanced techniques including sequential change detection, deep reinforcement learning, event-triggered processing, and differential privacy, and will bring significant innovations to the theory and applications of anomaly detection. In particular, the practical use of proposed algorithms will be demonstrated and their performance will be evaluated with respect to the state of the art using hardware implementations of two IoT networks - a surveillance camera network and a smart home network.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

异常检测是用于检测异常数据模式的重要问题。异常检测的重要性在于一个事实，即观察到的数据中的异常可能是基础系统中不必要且经常可行的事件的迹象，例如失败，恶意活动等。在许多实时系统中，及时，准确地检测异常数据模式至关重要，并且将允许及时采取适当的对策，以抵消任何可能的危害。尽管长期研究了异常检测，但当今的复杂网络表现出新的挑战，例如：低延迟需求，数据大小，系统动态，未知分布，分布式性质和隐私。该提案的目的是研究在具有这些挑战的复杂系统中实时数据驱动的异常检测的有效和可扩展方法。该提案的主要主题在早期检测到通用复杂网络环境中的异常和攻击时解决了多个重要问题。考虑到网络安全在当今世界的重要性，理解和预知此类复杂网络的组织动态变化的方法具有巨大的意义。该提案通过带来新的和新颖的工程工具和想法来直接解决这些问题。遵循系统的方法，该项目首先考虑（1）如何在具有动态性和隐藏异常挑战的集中式高维系统中及时检测异常； （ii）如何在监视分布式系统中处理资源限制； （iii）如何在分布式系统中启用用于实时异常检测的隐私解决方案。这些挑战和该项目中提出的解决方案方法通常适用于各种复杂系统。具体来说，该项目着重于两个具有挑战性的物联网网络：监视摄像头网络和智能家庭网络。所提出的方法利用了一系列先进技术，包括顺序变化检测，深度强化学习，事件触发的处理和差异隐私，并将为异常检测的理论和应用带来重大创新。特别是，将证明对拟议算法的实际使用，并将使用两个IoT网络的硬件实现来评估其性能 - 监视摄像机网络和智能家庭网络。该奖项反映了NSF的法定任务，并认为通过基金会的知识优点和广泛的crietia crietia crietia criperia criperia criperia criperia criperia criperia criperia criperia cripitia criperia criperia cripitia cristia recteria rection the奖项都值得一提。

项目成果

Self-Supervised Learning for Online Anomaly Detection in High-Dimensional Data Streams

• DOI: 10.3390/electronics12091971
• 发表时间: 2023-04
• 期刊: Electronics
• 影响因子: 2.9
• 作者: Mahsa Mozaffari;Keval Doshi;Yasin Yılmaz

Home Energy Recommendation System (HERS): A Deep Reinforcement Learning Method Based on Residents’ Feedback and Activity

• DOI: 10.1109/tsg.2022.3158814
• 发表时间: 2022-07
• 期刊: IEEE Transactions on Smart Grid
• 影响因子: 9.6
• 作者: S. S. Shuvo-S.;Yasin Yılmaz

Detecting Dangerous Maritime Refugee Migration Paths through Cell Phone Activities

通过手机活动检测危险的海上难民迁移路径

• DOI: 10.1109/bigdata55660.2022.10021131
• 发表时间: 2022
• 期刊: 2022 IEEE International Conference on Big Data (Big Data
• 作者: Coban, Mustafa;Yumusak, Semih;Yilmaz, Yasin;Altun, Huseyin Oktay
• 通讯作者: Altun, Huseyin Oktay

Federated Learning-based Driver Activity Recognition for Edge Devices

• DOI: 10.1109/cvprw56347.2022.00377
• 发表时间: 2022-06
• 期刊: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
• 作者: Keval Doshi;Yasin Yılmaz

Demand-Side and Utility-Side Management Techniques for Increasing EV Charging Load

• DOI: 10.1109/tsg.2023.3235903
• 发表时间: 2023-09
• 期刊: IEEE Transactions on Smart Grid
• 影响因子: 9.6
• 作者: S. S. Shuvo-S.;Yasin Yılmaz