research-article

Deterrence of Intelligent DDoS via Multi-Hop Traffic Divergence

Authors:

Jianping WuAuthors Info & Claims

CCS '21: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security

Pages 923 - 939

https://doi.org/10.1145/3460120.3484737

Published: 13 November 2021 Publication History

Abstract

We devise a simple, provably effective, and readily usable deterrence against intelligent, unknown DDoS threats: Demotivate adversaries to launch attacks via multi-hop traffic divergence. This new strategy is motivated by the fact that existing defenses almost always lag behind numerous emerging DDoS threats and evolving intelligent attack strategies. The root cause is if adversaries are smart and adaptive, no single-hop defenses (including optimal ones) can perfectly differentiate unknown DDoS and legitimate traffic. Instead, we formulate intelligent DDoS as a game between attackers and defenders, and prove how multi-hop traffic divergence helps bypass this dilemma by reversing the asymmetry between attackers and defenders. This insight results in EID, an Economical Intelligent DDoS Demotivation protocol. EID combines local weak (yet divergent) filters to provably null attack gains without knowing exploited vulnerabilities or attack strategies. It incentivizes multi-hop defenders to cooperate with boosted local service availability. EID is resilient to traffic dynamics and manipulations. It is readily deployable with random-drop filters in real networks today. Our experiments over a 49.8 TB dataset from a department at the Tsinghua campus network validate EID's viability against rational and irrational DDoS with negligible costs.

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Cited By

Li JSisodia DFeng YShi LZhang MEarly CReiher P(2023)Toward Adaptive DDoS-Filtering Rule Generation2023 IEEE Conference on Communications and Network Security (CNS)10.1109/CNS59707.2023.10288699(1-9)Online publication date: 2-Oct-2023
https://doi.org/10.1109/CNS59707.2023.10288699
Li QHuang HLi RLv JYuan ZMa LHan YJiang Y(2023)A comprehensive survey on DDoS defense systemsComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2023.109895233:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.comnet.2023.109895
Li HZhao YYao WXu KLi Q(2023)Towards real-time ML-based DDoS detection via cost-efficient window-based feature extractionScience China Information Sciences10.1007/s11432-021-3545-066:5Online publication date: 17-Apr-2023
https://doi.org/10.1007/s11432-021-3545-0
Show More Cited By

Index Terms

Deterrence of Intelligent DDoS via Multi-Hop Traffic Divergence
1. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Economics of security and privacy
  2. Network security
    1. Denial-of-service attacks
    2. Security protocols
2. Theory of computation
  1. Design and analysis of algorithms
    1. Online algorithms
      1. Adversary models
  2. Theory and algorithms for application domains
    1. Algorithmic game theory and mechanism design

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cover image ACM Conferences

CCS '21: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security

November 2021

3558 pages

ISBN:9781450384544

DOI:10.1145/3460120

General Chairs:
Yongdae Kim
KAIST, Republic of Korea
,
Jong Kim
POSTECH, Republic of Korea
,
Program Chairs:
Giovanni Vigna
University of California, Santa Barbara / VMware, USA
,
Elaine Shi
Carnegie Mellon University, USA

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Published: 13 November 2021

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Cited By

Li JSisodia DFeng YShi LZhang MEarly CReiher P(2023)Toward Adaptive DDoS-Filtering Rule Generation2023 IEEE Conference on Communications and Network Security (CNS)10.1109/CNS59707.2023.10288699(1-9)Online publication date: 2-Oct-2023
https://doi.org/10.1109/CNS59707.2023.10288699
Li QHuang HLi RLv JYuan ZMa LHan YJiang Y(2023)A comprehensive survey on DDoS defense systemsComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2023.109895233:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.comnet.2023.109895
Li HZhao YYao WXu KLi Q(2023)Towards real-time ML-based DDoS detection via cost-efficient window-based feature extractionScience China Information Sciences10.1007/s11432-021-3545-066:5Online publication date: 17-Apr-2023
https://doi.org/10.1007/s11432-021-3545-0
Kawazoe TFukuta N(2023)On Implementing a Simulation Environment for a Cooperative Multi-agent Learning Approach to Mitigate DRDoS AttacksRecent Advances in Agent-Based Negotiation: Applications and Competition Challenges10.1007/978-981-99-0561-4_2(15-29)Online publication date: 21-Mar-2023
https://doi.org/10.1007/978-981-99-0561-4_2
Zhang YWang YHu YLin ZZhai YWang LZhao QWen KKang L(2022)Security Performance Analysis of LEO Satellite Constellation Networks under DDoS AttackSensors10.3390/s2219728622:19(7286)Online publication date: 26-Sep-2022
https://doi.org/10.3390/s22197286
Yu BLi PLiu JZhou ZHan YLi Z(2022)Advanced Analysis of Email Sender Spoofing Attack and Related Security Problems2022 IEEE 9th International Conference on Cyber Security and Cloud Computing (CSCloud)/2022 IEEE 8th International Conference on Edge Computing and Scalable Cloud (EdgeCom)10.1109/CSCloud-EdgeCom54986.2022.00023(80-85)Online publication date: Jun-2022
https://doi.org/10.1109/CSCloud-EdgeCom54986.2022.00023

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