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Collaborative Detection of DDoS Attacks over Multiple Network Domains
Abstract
This paper presents a new distributed approach to detecting DDoS (distributed denial of services) flooding attacks at the traffic-flow level The new defense system is suitable for efficient implementation over the core networks operated byInternet service providers(ISPs). At the early stage of a DDoS attack, some traffic fluctuations are detectable at Internet routers or at the gateways of edge networks. We develop adistributed change-point detection(DCD) architecture using change aggregation trees (CAT). The idea is to detect abrupt traffic changes across multiple network domains at the earliest time. Early detection of DDoS attacks minimizes the floe cling damages to the victim systems serviced by the provider. The system is built over attack-transit routers, which work together cooperatively. Each ISP domain has a CAT server to aggregate the flooding alerts reported by the routers. CAT domain servers collaborate among themselves to make the final decision. To resolve policy conflicts at different ISP domains, anew secureinfrastructure protocol(SIP) is developed to establish mutual trust or consensus. We simulated the DCD system up to 16 network domains on the Cyber Defense Technology Experimental Research (DETER) testbed, a 220-node PC cluster for Internet emulation experiments at the University of Southern California (USC) Information Science Institute. Experimental results show that four network domains are sufficient to yield a 98 percent detection accuracy with only 1 percent false-positive alarms. Based on a 2006 Internet report onautonomous system(AS) domain distribution, we prove that this DDoS defense system can scale well to cover 84 AS domains. This security coverage is wide enough to safeguard most ISP core networks from real-life DDoS flooding attacks.
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Citations
Cited by
Low-Rate DDoS Attacks Detection and Traceback by Using New Information Metrics
Xiang, Y., Li, K., & Zhou, W. (2011) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2011.2107320 · Cited by 375
Distributed and cloud computing: from parallel processing to the Internet of things
(2012) · Choice Reviews Online · doi:10.5860/choice.49-5712 · Cited by 314
A System for Denial-of-Service Attack Detection Based on Multivariate Correlation Analysis
Tan, Z., Jamdagni, A., He, X., Nanda, P., & Liu, R. P. (2014) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2013.146 · Cited by 314
Botnet in DDoS Attacks: Trends and Challenges
Hoque, N., Bhattacharyya, D. K., & Kalita, J. K. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2457491 · Cited by 299
UDP Flooding Attack Detection Using Information Metric Measure
Boro, D., Basumatary, H., Goswami, T., & Bhattacharyya, D. K. (2016) · Advances in Intelligent Systems and Computing · doi:10.1007/978-981-10-0129-1_16 · Cited by 10
Metadata
- Authors
- Chen, Y., Hwang, K., & Ku, W.-S.
- Venue
- IEEE Transactions on Parallel and Distributed Systems
- Published
- 2007-12
- Volume
- 18
- Issue
- 12
- Pages
- 1649-1662
- ISSN
- 1045-9219 (ISSN-L)
- 1558-2183
- 2161-9883
- OpenAlex
- W2170810185
- Cited by
- 278
- References
- 0
- FWCI
- 18.66
- Type
- journal article
- Language
- en
- Open access
- closed
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- Chen, Y., Hwang, K., & Ku, W.-S. (2007). Collaborative Detection of DDoS Attacks over Multiple Network Domains. IEEE Transactions on Parallel and Distributed Systems. https://doi.org/10.1109/tpds.2007.1111
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