A Gini Impurity-Based Interest Flooding Attack Defence Mechanism in NDN

Zhi, Ting; Luo, Hongbin; Liu, Ying

doi:10.1109/lcomm.2018.2789896

Cited by 57 publications

(22 citation statements)

References 13 publications

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“…It is also noteworthy that several prior works, such as [26], [27], proposed to study statistical methods for IFA detection.…”

Section: B Interest Flooding Attackmentioning

confidence: 99%

“…In [26] the author proposed to use the well-known Gini coefficient to measure a side effect of IFA: the discrepancy in the range of requested content. The authors in [27] use a model of the IFA attack in the time-frequency domain using wavelets transform.…”

Section: B Interest Flooding Attackmentioning

confidence: 99%

“…Another common limitation of almost all prior works lies in their unknown performance. Indeed, even statistical methods, such as those proposed in [26], [27] are only evaluated empirically and, again, in simulated environments. Empirical evaluations, on their own, do not allow to guarantee any reliability of the results whose properties are unknown when practical setup is changed.…”

Section: Common Limitations Of Prior Workmentioning

confidence: 99%

“…(11)- (12), represents the variance decrease due to the IFA generated Interests; v a represents the number of samples whose loss-packet rate is changed due to the attack and is to be set according to the user's desire since, as shown in Eqs. (23)- (26) and in Section V-A there is a tradeoff between quick and accurate detection; the user can set, for instance, v a = (0, 0, . .…”

Section: Generalized Likelihood Ratio Testmentioning

confidence: 99%

“…From the power function (26), one can note that the loss of optimality of the proposed GLRT is mainly caused by the factor v a 2 2 . This is explained by the estimation of the unknown and dynamic packet-loss rate.…”

Section: Generalized Likelihood Ratio Testmentioning

confidence: 99%

See 4 more Smart Citations

Reliable Detection of Interest Flooding Attack in Real Deployment of Named Data Networking

Nguyen

Mai

Cogranne

et al. 2019

IEEE Trans.Inform.Forensic Secur.

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Named Data Networking (NDN) is a disruptive yet promising architecture for the future Internet, in which the content diffusion mechanisms are shifted from the conventional host-centric to content-centric ones so that the data delivery can be significantly improved. After a decade of research and development, NDN and the related NDN Forwarding Deamon (NFD) implementations are now mature enough to enable stakeholders, such as telcos, to consider them for a real deployment. Consequently, NDN and IP will likely cohabit, and the Future Internet may be formed of isolated administrative domains, each deploying one of these two network paradigms. The security question of the resulting architecture naturally arises. In this paper, we consider the case of Denial of Service. Even though the Interest Flooding Attack (IFA) has been largely studied and mitigated through NACK packets in pure NDN networks, we demonstrate in this paper through experimental assessments that there are still some ways to mount such an attack, and especially in the context of coupling NDN with IP, that can hardly be addressed by current solutions. Subsequently, we leverage hypothesis testing theory to develop a Generalized Likelihood Ratio Test (GLRT) adapted to evolved IFA attacks. Simulations show the relevance of the proposed model for guaranteeing the prescribed Probability of False Alarm (PFA) and highlights the trade-off between detection power and delay. Finally, we consider a real deployment scenario where NDN is coupled with IP to carry HTTP traffic. We show that the model of IFA attacks is not very accurate in practice and further develops a sequential detector to keep a high detection accuracy. By considering data from the testbed, we show the efficiency of the overall detection method.

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“…It is also noteworthy that several prior works, such as [26], [27], proposed to study statistical methods for IFA detection.…”

Section: B Interest Flooding Attackmentioning

confidence: 99%

Section: B Interest Flooding Attackmentioning

confidence: 99%