Collaborative Research: SaTC: CORE: Medium: ONSET: Optics-enabled Network Defenses for Extreme Terabit DDoS Attacks
Collaborative Research: SaTC: CORE: Medium: ONSET: Optics-enabled Network Defenses for Extreme Terabit DDoS Attacks
批准号:
2132643
负责人:
Zaoxing Liu
金额:
$40.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2022
资助国家:
美国
项目状态:
已结题
起止时间:
2022-01-01 至 2024-04-30
中文摘要
分布式拒绝服务(DDoS)攻击继续对关键网络基础设施构成明显且迫在眉睫的危险。DDoS攻击的复杂性随着高级策略的不断适应而增加(例如,动态地改变威胁姿态)并引起附带损害(即,合法流量的更高延迟和损失)。此外,高级攻击还可以采用侦察(例如,映射网络以找到瓶颈链路)以瞄准网络基础设施本身。鉴于这些趋势,最先进的防御(例如,高级擦除、新兴的软件定义防御和可编程交换硬件)具有根本的缺点。该项目将开发一个新的框架,称为“针对极端太比特DDoS攻击的光学网络防御”(ONSET)。该框架为防御敏捷性的新维度提供了一个案例,可以通过编程控制网络的拓扑结构(除了处理行为之外),以应对高级和未来的攻击。该项目将促进使用光学技术作为一种令人兴奋的视觉媒体,通过适当的传播渠道吸引K-12学生。该项目还将在光网络,软件定义网络和网络安全的交叉点产生新的课程材料,使学生成为这个新兴问题领域的专家。该项目将采取跨学科的方法,涵盖安全、光学、系统和网络,以解决沿着的三个方面的基本挑战:(1)新颖的“数据平面”解决方案,以快速重新配置波长和交换机,以及可编程交换机的新功能,以在线速率快速识别恶意流量和良性流量;(2)新颖的“控制平面”编排机制,用于跨光网络和可编程交换机的可扩展资源管理算法和协调控制;以及(3)新的“北向应用程序编程接口(API)”以表达对抗当前和未来DDoS攻击的新颖防御(例如,侦察)。该项目将开发一个新的框架,称为“针对极端太比特DDoS攻击的光学网络防御”(ONSET)。研究工作将产生使用开源和标准化接口的端到端原型,以展示ONSET的新防御能力。ONSET的有效性将通过使用真实的试验台和大规模模拟,对运营网络进行试点研究,以创建实际部署的路线图。项目成果将以开源软件工具、模型和模拟框架的形式发布,为行业和学术工作提供信息。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
Distributed Denial of Service (DDoS) attacks continue to present a clear and imminent danger to critical network infrastructures. DDoS attacks have increased in sophistication with advanced strategies to continuously adapt (e.g., changing threat postures dynamically) and induce collateral damage (i.e., higher latency and loss for legitimate traffic). Furthermore, advanced attacks may also employ reconnaissance (e.g., mapping the network to find bottleneck links) to target the network infrastructure itself. In light of these trends, state-of-art defenses (e.g., advanced scrubbing, emerging software-defined defenses, and programmable switching hardware) have fundamental shortcomings. This project will develop a new framework, referred to as "Optics-enabled In-Network defenSe for Extreme Terabit DDoS attacks" (ONSET). The framework makes a case for new dimensions of defense agility that can programmatically control the topology of the network (in addition to the processing behavior) to tackle advanced and future attacks. The project will facilitate the use of optical technologies as an exciting visual medium for engaging K-12 students via suitable channels for dissemination. The project will also result in new course materials at the intersection of optical networking, software-defined networking, and network security to enable students to become domain experts in this emerging problem space. The project will take an interdisciplinary approach spanning security, optics, systems, and networks, to address fundamental challenges along three thrusts: (1) novel "data plane" solutions to rapidly reconfigure the wavelengths and switches and new capabilities in programmable switches to rapidly identify malicious vs. benign traffic at line rate; (2) novel "control plane" orchestration mechanisms for scalable resource management algorithms and coordinated control across optical networking and programmable switches; and (3) new "northbound application programming interfaces (APIs)" to express novel defenses to combat current and future DDoS attacks (e.g., with reconnaissance). This project will develop a new framework, referred to as "Optics-enabled In-Network defenSe for Extreme Terabit DDoS attacks" (ONSET). The research efforts will result in end-to-end prototypes using open-source and standardized interfaces to demonstrate the novel defense capabilities of ONSET. The efficacy of ONSET will be evaluated using pilot studies on operational networks to create a roadmap to practical deployment, using real testbeds and large-scale simulations. The project outcomes will be released as open-source software tools, models, and simulation frameworks that will inform industry and academic work.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.
期刊论文(3)
专著(0)
科研奖励(0)
会议论文
DOI:
--
发表时间:
2023
期刊:
影响因子:
--
作者:
[Hun Namkung;Zaoxing Liu;Daehyeok Kim;Vyas Sekar;P. Steenkiste]
通讯作者:
Hun Namkung;Zaoxing Liu;Daehyeok Kim;Vyas Sekar;P. Steenkiste
In-Memory Key-Value Store Live Migration with NetMigrate
使用 NetMigrate 进行内存中键值存储实时迁移
DOI:
--
发表时间:
2024
期刊:
22nd USENIX Conference on File and Storage Technologies (FAST '24
影响因子:
--
作者:
[Zhu, Zeying, Zhu, Yibo, Liu, Zaoxing]
通讯作者:
Liu, Zaoxing
Enabling Efficient and General Subpopulation Analytics in Multidimensional Data Streams
在多维数据流中实现高效、通用的子群体分析
DOI:
10.14778/3551793.3551867
发表时间:
2022
期刊:
Proceedings of the VLDB Endowment
影响因子:
2.5
作者:
[Manousis, Antonis, Cheng, Zhuo, Basat, Ran Ben, Liu, Zaoxing, Sekar, Vyas]
通讯作者:
Sekar, Vyas
Collaborative Research: SaTC: CORE: Medium: ONSET: Optics-enabled Network Defenses for Extreme Terabit DDoS Attacks
-
批准号:2415754
-
项目类别:Standard Grant
-
资助金额:$40.0万
-
财政年份:2023
-
负责人:Zaoxing Liu
-
依托单位:
Collaborative Research: CNS: Medium: Scalable Learning from Distributed Data for Wireless Network Management
-
批准号:2106946
-
项目类别:Continuing Grant
-
资助金额:$20.0万
-
财政年份:2021
-
负责人:Zaoxing Liu
-
依托单位:
Collaborative Research: CNS Core: Medium: FROOT: Future-Proof, Trustworthy Telemetry on Heterogeneous Networks
-
批准号:2107086
-
项目类别:Standard Grant
-
资助金额:$50.0万
-
财政年份:2021
-
负责人:Zaoxing Liu
-
依托单位:
国内基金
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