Resist Interest Flooding Attacks via Entropy–SVM and Jensen–Shannon Divergence in Information-Centric Networking

Zhi, Ting; Liu, Ying; Wang, Jiushuang; Zhang, Hongke

doi:10.1109/jsyst.2019.2939371

Cited by 20 publications

(14 citation statements)

References 34 publications

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“…Xin et al [109], Shigeyasu et al [78], DPE [105], TDM [104], Vassilakis et al [98], Tang et al [95], Shinohara et al [90], Wang et al [106], Benmoussa et al [24], Karami et al [49], Zhi et al [128], Ding et al [38], Zhi et al [130], Hou et al [46], ChoKIFA [22],…”

Section: Statefull Solutionsmentioning

confidence: 99%

“…However, the rate-limiting can affect legitimate consumers. Another machine learning-based detection mechanism was proposed in [128]. In this solution, a router constantly collects the entropy of Interest names, the satisfaction ratio, and the PIT usage of interfaces before using them as entries for the support vector machine (SVM) classifier.…”

Section: Proactive Solutionsmentioning

confidence: 99%

“…The cooperative detection is further classified into partial cooperation and full cooperation. (1) Partial cooperation: In a partial cooperation design, the network nodes collaborate only on the mitigation action, like [10], [128] and [23]. Each node analyzes its local traffic to detect an IFA and then takes the proper action to mitigate it.…”

Section: Distributed Detection Designmentioning

confidence: 99%

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Interest Flooding Attacks in Named Data Networking: Survey of Existing Solutions, Open Issues, Requirements, and Future Directions

et al. 2022

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Named Data Networking (NDN) is a prominent realization of the vision of Information-Centric Networking. The NDN architecture adopts name-based routing and location-independent data retrieval. Among other important features, NDN integrates security mechanisms and focuses on protecting the content rather than the communications channels. Along with a new architecture come new threats and NDN is no exception. NDN is a potential target for new network attacks such as Interest Flooding Attacks (IFAs). Attackers take advantage of IFA to launch (D)DoS attacks in NDN. Many IFA detection and mitigation solutions have been proposed in the literature. However, there is no comprehensive review study of these solutions that has been proposed so far. Therefore, in this paper, we propose a survey of the various IFAs with a detailed comparative study of all the relevant proposed solutions as counter-measures against IFAs. We also review the requirements for a complete and efficient IFA solution and pinpoint the various issues encountered by IFA detection and mitigation mechanisms through a series of attack scenarios. Finally, in this survey, we offer an analysis of the open issues and future research directions regarding IFAs.

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Section: Statefull Solutionsmentioning

confidence: 99%

Section: Proactive Solutionsmentioning

confidence: 99%

Section: Distributed Detection Designmentioning

confidence: 99%

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Interest Flooding Attacks in Named Data Networking: Survey of Existing Solutions, Open Issues, Requirements, and Future Directions

et al. 2022

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“…chip-scale optical circuit type) silicon probes for sub-millisecond deep-brain optical stimulation -e.g. for the purpose of gaining a deeper understanding of the neural code, in Perera et al [290] for the quantification of the level of rationality in supply chain networks, in Pierri et al [294] for the study of growth of malicious/misleading information in some social media diffusion networks, in Rabadan et al [299] for the identification of gene mutations that lead to the genesis and progression of tumors, in Reiter et al [306] for quantifying metastatic phylogenetic diversity, in Van de Sande et al [378] as part of a computational toolbox for single-cell gene regulatory network analysis, in Skinnider et al [337] for the prediction of the chemical structures of genomically encoded antibiotics -in order to find means against the looming global crisis of antibiotic resistance, in Tuo et al [367] for the detection of high-order single nucleotide polymorphism (SNP) interactions, in Uttam et al [370] for predicting the risk of colorectal cancer recurrence and inferring associated tumor microenvironment networks, in Zhang et al [421] for incipient fault (namely, crack) detection, in Zhi et al [425] for the strengthening of information-centric networks against interest flooding attack (IFAs), in Acera Mateos et al [3] for deep-learning classification of SARS-CoV-2 and co-infecting RNA viruses, in Avsec et al [24] for uncovering the motifs and syntax of cis-regulatory sequences in genomics data, in Barennes et al [32] for comparing the accuracy of current T cell receptor sequencing methods employed for the understanding of adaptive immune responses, in Chen et al [79] for clustering high-dimensional microbial data from RNA sequencing, in Chen et al [84] for investigating key aspects of effective vocal social communication, in Koldobskiy et al [193] for investigations of genetic and epigenetic drivers of paediatric acute lymphoblastic leukaemia, in McGinnis et al [258] for evaluating RNA sequencing of pooled blood cell samples, in Mühlroth & Grottke [268] for the detection of emerging trends and technologies through artificial intelligence techniques, in Necci et al [272] for the assessment of protein intrinsic disorder predictions, in Okada et al [277] for the identification of genetic factors that cause individual differences in whole lymphocyte profiles and their changes after vaccination, and in Zhang et al [422] for the learning of functional magnetic resonance imaging (fMRI) time-seri...…”

Section: We Obtainmentioning

confidence: 99%

A precise bare simulation approach to the minimization of some distances. Foundations

Broniatowski¹,

Stummer²

2021

Preprint

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In information theory -as well as in the adjacent fields of statistics, machine learning, artificial intelligence, signal processing and pattern recognition -many flexibilizations of the omnipresent Kullback-Leibler information distance (relative entropy) and of the closely related Shannon entropy have become frequently used tools. To tackle corresponding constrained minimization (respectively maximization) problems by a newly developed dimension-free bare (pure) simulation method, is the main goal of this paper. Almost no assumptions (like convexity) on the set of constraints are needed, within our discrete setup of arbitrary dimension, and our method is precise (i.e., converges in the limit). As a side effect, we also derive an innovative way of constructing new useful distances/divergences. To illustrate the core of our approach, we present numerous examples. The potential for widespread applicability is indicated, too; in particular, we deliver many recent references for uses of the involved distances/divergences and entropies in various different research fields (which may also serve as an interdisciplinary interface).

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“…The motivation behind this research work is to mitigate the IFA that leads packet broadcast storm of uncontrolled and unsolicited traffic across VNDN. Various IFA mitigation schemes have been proposed for NDN scenario, that is, per-face Interest rate monitoring techniques [13][14][15][16], statistical modelling-based approaches [17][18][19], adaptive in-network caching [20][21][22][23][24][25] and packet routing algorithms [26][27][28][29][30][31][32]. However, the drawback of these schemes is that along with malicious Interest packets, a high volume of legitimate Interest packets is dropped as well.…”

Section: Introductionmentioning

confidence: 99%

Interest flooding attack mitigation in a vehicular named data network

Abdullah

Raza

Zia

et al. 2021

IET Intelligent Trans Sys

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Vehicular named data network (VNDN) is the next‐generation network architecture for intelligent transportation system. Contrary to the conventional transmission control protocol/internet protocol (TCP/IP) communication model, VNDN follows a data‐centric approach where the user is interested in ‘WHAT’ instead of ‘WHERE’. Interest flooding attack (IFA) is one of the prominent security concerns in VNDNs. In IFA, attackers request for non‐existent content to exhaust network resources and cause Interest packet flooding across the network. A novel attack mitigation scheme to counter IFA in VNDN has been proposed in this study. The proposed priority‐based per‐flow Interest rate monitoring (PP‐FIRM) scheme determines the suspicious flow of malicious incoming Interest packets in attacked vehicles. A priority flag is assigned to the incoming flow of Interest packets that detects the occurrence of an attack. The priority of incoming Interest packet flow is calculated using a collaborative or neighbour‐assisted approach. A comparison with another attack mitigation scheme validates that the proposed scheme performs better in terms of an improved cache hit ratio and Interest satisfaction ratio during the attack window. Besides this, pending Interest table utilisation, packet collisions rate, Interest packets retransmission count, end‐to‐end delay, and the ratio of timed out Interest packets have also been reduced. Furthermore, the scalability of the proposed research strategy is also evaluated by changing the density of attackers in real time. Moreover, in the proposed attack mitigation model, the rate of incoming legitimate Interest packets increases by reducing the drop rate of valid Interest packets.

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Resist Interest Flooding Attacks via Entropy–SVM and Jensen–Shannon Divergence in Information-Centric Networking

Cited by 20 publications

References 34 publications

Interest Flooding Attacks in Named Data Networking: Survey of Existing Solutions, Open Issues, Requirements, and Future Directions

Interest Flooding Attacks in Named Data Networking: Survey of Existing Solutions, Open Issues, Requirements, and Future Directions

A precise bare simulation approach to the minimization of some distances. Foundations

Interest flooding attack mitigation in a vehicular named data network

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