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REVIEW 3 major objections 5 minor 93 references

Malicious node aware wireless multi hop networks: a systematic review of the literature and recommendations for future research

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This systematic review maps 74 malicious-node detection methods across seven wireless multi-hop network types, groups them into 14 method families, and compares their evaluation criteria, attack models, and simulation environments to…

desk verdict A well-intentioned but methodologically broken survey whose promised network-wise comparison never materializes. read the letter →

arxiv 2506.05742 v1 pith:2QEOTZYT submitted 2025-06-06 cs.DC

Shahram Pourdehghan , Nahideh Derakhshanfard This is my paper
classification cs.DC
keywords maliciousnodedetectionwirelessmulti-hopnetworkssystematicreviewMANETWSNVANETtrustandreputationgametheory
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish a complete, organized map of the methods proposed in the literature for detecting malicious nodes in wireless multi-hop networks. It gathers 74 papers and groups them into 14 method families, including trust and reputation, authentication and encryption, node behavior, routing-protocol modifications, energy-based, game theory, neural networks, and others, then compares these families across seven network types: ad hoc, MANET, VANET, WSN, DTN, opportunistic networks, and wireless mesh/P2P variants. The value of the map, if it is accurate, is that a researcher can see which detection strategies are saturated and which are still open, and can compare reported evaluation criteria such as false-positive rate, throughput, packet delivery ratio, and detection accuracy across methods. The authors conclude that authentication/encryption, grouping/clustering, and node-behavior methods dominate the literature, while artificial neural networks and game theory are underused and worth exploring.

What carries the argument

The central object is the classification scheme of 14 method families together with the 74-paper comparison table that populates it. The table is the load-bearing artifact: each row encodes the method name, evaluation criteria, attack type, network type, and simulation environment, which is what lets the authors and readers see prevalence, gaps, and cross-network applicability.

What would settle it

Re-run the search with explicit keywords, database names, and inclusion/exclusion criteria over the same 2004–2017 window; if the resulting corpus yields a materially different distribution of method families, or if the 'above 90% detection-only' figure does not reproduce when each of the 74 rows is coded for detection versus removal, the paper's prevalence and gap conclusions fail.

Watch

Extended reading notes

Core claim

The core discovery is a taxonomy plus a comparison: malicious-node detection methods in wireless multi-hop networks can be classified into 14 categories, and the field's output is unevenly distributed across them. The paper's comparative table lists each of the 74 papers with its year, publisher, method name, criteria, proposed technique, network type, attack type, parameters, application scenario, and simulation environment. From this table the paper derives its main qualitative findings: over 90% of the surveyed works focus on detection rather than removal; trust/reputation, authentication/encryption, and grouping/clustering have the most papers; and techniques based on artificial neural networks and game theory remain relatively unexplored. The stated aim is to help future researchers choose directions by knowing what has already been tried and where the gaps are.

Load-bearing premise

The review assumes that the 74 papers it collected are a complete and unbiased sample of the malicious-node detection literature, even though its selection procedure is described only as an automatic search with loosely defined keywords and no stated dates, inclusion, or exclusion criteria.

Editorial extensions

If this is right

  • Researchers planning a detection scheme can use the 14-family taxonomy to position their work and to identify which evaluation criteria are expected in their network type.
  • The review's prevalence counts imply that trust/reputation, authentication/encryption, and node-behavior approaches are crowded, so a new contribution in these families must differentiate itself explicitly.
  • The paper's identification of artificial neural networks and game theory as underused points to concrete research openings, particularly for VANET and opportunistic networks.
  • Because the comparison table records attack types, a reader can match detection methods to specific attacks rather than to a generic malicious-node label.
  • The observation that detection dominates removal suggests that removal and revocation mechanisms are an under-developed companion problem.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper does not quantify detection accuracy by method family; a natural extension would be a meta-analysis that pools reported false-positive/false-negative rates or detection accuracy across the 74 rows to test whether some families actually outperform others.
  • Its network-type grouping could be cross-tabulated with method family to reveal which frameworks have been tried in which networks—a gap analysis the paper hints at but does not tabulate explicitly.
  • If the field follows the paper's suggestion, a likely near-term development is a wave of neural-network and game-theory detectors in VANETs and opportunistic networks; that is an extrapolation from the review's distribution, not a claim the review proves.
  • The paper's definition of a malicious node as one that drops packets or refuses to cooperate, whether by failure or by intent, implicitly bundles fault tolerance with security; readers should keep that conflation in mind when interpreting 'malicious' across the 74 papers.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The manuscript claims to be a systematic review of malicious node detection methods in wireless multi-hop networks. Its stated goal, per the abstract and research question Q2, is to categorize networks into groups (ad hoc, MANET, DTN, opportunistic, WSN, VANET, and others) and compare detection methods across those network types. The paper compiles a corpus of 74 papers, tabulates each paper's proposed technique, criterion, network type, attack type, and simulation environment, and then groups the papers in Section 5 into 14 methodological categories (e.g., routing-protocol based, trust/voting/reputation based, game-theory based). Section 6 reports method-level prevalence and offers future-work suggestions focused on artificial neural networks and game theory.

Significance. If the promised network-wise comparison were actually delivered, the review could serve as a useful starting point for researchers seeking a map of malicious-node detection techniques across different multi-hop network families. The paper has a potentially useful corpus: 74 papers from multiple publishers and years, with a table that pulls together technique names, evaluation criteria, network types, attack types, and simulators. The method taxonomy in Section 5 is also a plausible organizing scheme. However, because the central comparative claim is not implemented and the corpus contains duplicate and corrupted entries, the actual contribution as submitted is a list of method categories with descriptive summaries rather than the systematic, network-wise comparative review announced in the abstract. The significance of the manuscript in its current form is therefore limited.

major comments (3)
  1. [Abstract; Section 5; Section 6] The central claim of the paper is that it categorizes networks into groups and compares malicious node detection methods in these networks, but the body of the paper does not implement this comparison. Section 5 organizes papers by detection technique (routing-protocol based, ack based, authentication/encryption/hashing, trust/voting/reputation, etc.), not by network type. The table in Section 5 has a 'Network Type' column, yet that column is never used as an organizing or analytical dimension: there is no subsection comparing methods within WSN, MANET, VANET, DTN, or opportunistic networks, no aggregate table of performance metrics per network category, and no discussion of network-specific gaps. Section 6's conclusions discuss method-level prevalence ('techniques based on authentication, encryption and grouping, clustering and node behavior they had most papers'), not network-wise findings. Thus the headline deliverable is structurally absent, independent of the corpus-quality issues.
  2. [Section 4.2] The article selection process is not reproducible and does not meet the standards implied by the term 'systematic review.' The authors state only that they performed 'automatic search using search engines like google scholar with keywords' and list a few example keywords, but they provide no complete keyword string, no search dates, no list of databases or venues, no inclusion/exclusion criteria, and no screening or quality-assessment procedure. The distribution of articles by year and publisher is presented without explaining how the 74 papers were reduced from the initial search results. Because the review's conclusions about method prevalence and research gaps depend on the completeness and representativeness of this corpus, the absence of a reproducible protocol is a load-bearing methodological weakness.
  3. [Table in Section 5; References [6], [19], [54], [74]] The corpus contains duplicated and corrupted entries that compromise the table's integrity. Rows 10 and 11 of the table both cite reference [19] (Ram Prabha and Latha, 'Fuzzy Trust Protocol for Malicious Node Detection in Wireless Sensor Networks') but assign different method names and descriptions ('EMATP' with multi-attribute trust versus 'MATM' with fuzzy logic), and reference [6] is the same paper as [19]. Reference [74] is identical to reference [54] (Atassi et al., 'Malicious node detection in wireless sensor networks'), so two table rows likely describe the same work. In addition, references [2] and [37] are corrupted publisher or front-matter strings rather than valid citations. These problems mean that the counts and prevalence statements in Section 6 are not reliable, and they raise broader doubts about the care with which the corpus was compiled.
minor comments (5)
  1. [Section 5.2 heading] The heading 'Ack based Algoeithms' contains a typo; it should read 'Ack based Algorithms.'
  2. [Section 2.3] The text says 'the elder throughput is required' and later 'ration' for 'ratio'; these should be 'higher throughput' and 'ratio,' respectively.
  3. [Section 4.2 (Figures 2 and 3)] Figure 2 and Figure 3 have no descriptive captions, and Figure 3 lacks count labels on its bars, which makes the publisher distribution hard to read.
  4. [Section 6] The conclusion says 'we read about 70 papers,' but the table in Section 5 contains 74 rows; the count should be reconciled.
  5. [Section 5 (chart)] The 'Percentage of Methods' chart appears without a figure number or caption, and its categories are not clearly aligned with the numbered groups in the text.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: this review's claims are external summaries of 74 cited papers; the noted weaknesses are correctness and methodology issues, not circular reasoning.

full rationale

The paper is a systematic literature review. Its central claim is that it categorizes wireless multi-hop networks into groups (ad hoc, MANET, DTN, opportunistic, WSN, VANET, other) and compares malicious node detection methods across those groups. The support for this claim is the table and Section 5, which summarize methods from cited external papers. No parameter is fitted to data and then renamed as a prediction; no result is derived from an assumption that already contains the conclusion; and the authors do not invoke their own prior theorems or uniqueness results to force a choice. The review therefore contains no self-definitional step, no fitted-input-called-prediction step, and no load-bearing self-citation. The weaknesses identified by a critical reader are real but are not circularity: the article selection process is under-specified (Section 4.2 states only 'automatic search using search engines like google scholar' with broad keywords and no inclusion/exclusion criteria), the promised network-wise comparison is not actually implemented (Section 5 groups papers by method category rather than by network type), and there are indexing errors (reference [74] duplicates [54], and reference [19] is used for two different table rows). These are issues of completeness, internal consistency, and fidelity to the stated aim; they do not make the review's conclusions equivalent to its inputs by construction. The conclusion that authentication/encryption, grouping/clustering, and node-behavior techniques account for the most papers is a direct frequency count from the reviewed corpus, not a circular derivation. Future-work suggestions about neural networks and game theory are recommendations, not predictions derived from the same data in a way that assumes their truth. Accordingly, the circularity score is 0.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The review relies on two domain assumptions: the completeness/representativeness of the paper selection, and the accuracy of the transcribed table. There are no free parameters and no invented entities, because the paper is a literature survey rather than a modeling or experimental study.

assumptions (2)
  • domain assumption The 74-paper corpus selected via the described Google Scholar search is a complete and representative sample of malicious-node detection literature.
    The paper's conclusions about the distribution of methods and research gaps (Sections 5 and 6) depend entirely on the completeness and representativeness of the included papers. No search string, date, or inclusion criteria are given in Section 4.2.
  • domain assumption Each table row accurately represents the cited paper's method, criterion, network type, and attack type.
    The comparison table in Section 4.3 is the paper's core evidence; if transcription errors are present (and there are signs, such as duplicated entries and shifted columns), the review's comparative conclusions are affected.

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Cite this review

Pith. "Pith review of Malicious node aware wireless multi hop networks: a systematic review of the literature and recommendations for future research." pith.science (2026). https://pith.science/paper/2QEOTZYT

@misc{pith2026250605742,
  author       = {Pith},
  title        = {Pith review of: Malicious node aware wireless multi hop networks: a systematic review of the literature and recommendations for future research},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2QEOTZYT}},
  note         = {Machine review of arXiv:2506.05742}
}
read the original abstract

Wireless communication provides great advantages that are not available through their wired counterparts such as flexibility, ease of deployment and use, cost reductions, and convenience. Wireless multi-hop networks (WMN) do not have any centralized management infrastructure. Wireless multi-hop networks have many benefits since proposed. In such networks when a node wants to send a packet to a destination where is not in the transmission range, depend on some intermediate nodes. In this type of networks packet sending is in the form of multiple hop until destination and this work is dynamic. Lack of centralized management cause that some nodes show malicious function. Malicious nodes are that receive packets and drop them maliciously. These malicious nodes could have many reasons such as hardware failure, software failure or lack of power. Such nodes make multiple packets drop from the network and the performance of network strongly decreases. As a result, the throughput of the network decrease, increase end-to-end delay and increase overhead. Therefore, we must aware from presence of malicious node in the network and do routing based on this awareness. Therefore, this paper aims to study and review the present malicious node detection methods that proposed in literatures. We categorized networks in groups, including ad hoc networks, MANET, DTN, Opportunistic networks, WSN, VANET and other wireless networks and compare malicious node detection met

Figures

Figures reproduced from arXiv: 2506.05742 by the authors.

Figure 1
Figure 1. wireless mesh network DTN: in delay tolerant networks, the information transferred from its source to the destination without end-to-end connectivity of the network. When transmitting data between source and destination in networking, DTNs have very high transmission delay. Unlike MANETs, DTNs characterized by intermittent connectivity between nodes (11). Body area networks (BAN): in recent years, wireless body area… view at source ↗
Figure 2
Figure 2. distribution of obtained article by year [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Number of articles based on publishers 4.3. Article Classification Classifying articles can done based on multiple criteria. In this area, proposed methods about malicious node are different. Some proposed methods of papers only detect malicious nodes. Some papers proposed methods to detect and remove malicious node from network. Above 90 percentage of articles are based on detection of malicious node and articles b… view at source ↗

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Reference graph

Works this paper leans on

93 extracted references · 80 canonical work pages

  1. [2]

    multi-hop

    Main concepts of malicious nodes and WMNs In this section, we discussed the main concepts of wireless multi-hop networks and main concepts of malicious nodes. 2.1. Wireless multi-hop networks Wireless nodes, in order to communicate with out of range nodes when wireless nodes deployed in an ad hoc setup with no infrastructure, a wireless node has to depend...

  2. [19]

    Actually, this article presents a network management technique based on cognitive

    Other Methods (3) Detects malicious nodes and malicious behavior with cognitive-based methods (observe, orient, decide and act). Actually, this article presents a network management technique based on cognitive. In this method, the nodes always monitored and in case of seeing suspicious do proper planning. This article considered heterogeneous networks. (...

    work page

  3. [74]

    Malicious node detection in wireless sensor networks

    Atassi A, Sayegh N, Elhajj I, Chehab A, Kayssi A. Malicious node detection in wireless sensor networks. Proc - 27th Int Conf Adv Inf Netw Appl Work WAINA 2013. 2013;456– 61

    work page 2013

  4. [54]

    Malicious Node Detection on Vehicular Ad-Hoc Network Using Dempster Shafer Theory for Denial of Services Attack

    Kushwah N, Sonker A. Malicious Node Detection on Vehicular Ad-Hoc Network Using Dempster Shafer Theory for Denial of Services Attack. Proc - 2016 8th Int Conf Comput Intell Commun Networks, CICN 2016. 2017;432–6

    work page 2016

  5. [6]

    These methods presents techniques with minor change in routing protocols like DSR, DSDV and so on to prevent malicious nodes from participate in routing

    Routing Protocol based Algorithms In this title, we describe papers that benefit from routing protocols. These methods presents techniques with minor change in routing protocols like DSR, DSDV and so on to prevent malicious nodes from participate in routing. (32) Designed a malicious node identification based on dynamic source routing. This paper decrease...

  6. [37]

    A game theoretic approach to detect and co-exist with malicious nodes in wireless networks

    Wang W, Chatterjee M, Kwiat K, Li Q. A game theoretic approach to detect and co-exist with malicious nodes in wireless networks. Comput Networks [Internet]. 2014;71:63–83. Available from: http://dx.doi.org/10.1016/j.comnet.2014.06.008

    work page doi:10.1016/j.comnet.2014.06.008 2014

  7. [1]

    In such networks every node where wants to send a packet to out of itself range, use relay nodes

    Introduction In WMN, the nodes of the network used as relay node. In such networks every node where wants to send a packet to out of itself range, use relay nodes. The independency of nodes depending on intermediate nodes. This network in known as WMNs and have various types like ad hoc networks, MANET, VANET, DTN, WSN, Opportunistic network where we expl...

  8. [3]

    We found some articles that are not reliable completely and are not systematic

    Related Work There are not many survey articles about malicious node in the literatures. We found some articles that are not reliable completely and are not systematic. These articles only reviewed some proposed methods in malicious node detection or removal. For example, (20) describes malicious nodes only from one aspect and only in MANET and do not att...

Show all 93 references
  1. [4]

    First, we explain question formalization and then describe how articles selected

    Research methodology In this section the study methodology and the question that caused to do this research is present. First, we explain question formalization and then describe how articles selected. At the end of this section, we classify articles based on publishers and ye...

    2007

  2. [5]

    The grouping of papers is as below and some paper that does not fall any category, described separate

    Article Grouping In this section, we categorized studied papers in some groups and describe each paper in general description of their performance. The grouping of papers is as below and some paper that does not fall any category, described separate

  3. [7]

    In this study author proposed an improvement in end-to-end acknowledgment scheme called TWOACK

    Ack based Algoeithms (46) Uses Elliptic curve cryptography for improved security with reduced key size and with less computation. In this study author proposed an improvement in end-to-end acknowledgment scheme called TWOACK. The TWOACK scheme detects malicious links not nodes...

  4. [8]

    This study presents an adaptive security module for improvement secure communication to authentication new nodes, create a secure link, and broadcast it between neighbors

    Algorithms based on Authentication, Encryption, Hashing (7) Detects malicious nodes. This study presents an adaptive security module for improvement secure communication to authentication new nodes, create a secure link, and broadcast it between neighbors. This work prevents e...

  5. [9]

    Message Communicating based Schemes (60) Basic idea is to store the basic message about communications between nodes, and send them to the base node where these messages are combined to node feature vectors. Once the base node gets all nodes feature vector, it uses the type kn...

  6. [10]

    This paper actually mixed mechanism of trust based and voting based to delete malicious nodes from the network

    Trust, Voting and Reputation Methods (18) Used local and global information of nodes and a voting system to exclude malicious nodes from network. This paper actually mixed mechanism of trust based and voting based to delete malicious nodes from the network. This mechanism divi...

  7. [11]

    The packets that created in source must equal the packets received in destination, so not being equal meaning malicious nodes are in the route

    Throughput and Packet Delivery based (33) Using Packet Delivery Ratio (PDR) criterion detects malicious nodes. The packets that created in source must equal the packets received in destination, so not being equal meaning malicious nodes are in the route. (26) Only considered t...

  8. [12]

    A camouflage event is a reputable event generated in response to a basestation request

    Based on Spatial and Temporal Information (66) Exploit the spatial and temporal information of camouflage event while analyzing the packets to identify malicious activity. A camouflage event is a reputable event generated in response to a basestation request. A camouflage even...

  9. [13]

    This work is done with behavior patterns of nodes

    Based on Node Behavior (1) Detects malicious nodes with outlier mining-based technique in P2P networks. This work is done with behavior patterns of nodes. Simulation results demonstrated that this technique detects malicious nodes with lower false positive rate and false negat...

  10. [14]

    This work is done by selecting appropriate cluster head to increase performance

    Based on Grouping and Clustering (36) Wants to detect malicious nodes in MANET. This work is done by selecting appropriate cluster head to increase performance. Cluster head could delete RREQ in reason low energy and send a RELEASE message to all nodes, when a node received a ...

  11. [15]

    This work identify malicious nodes in VANET

    Based on Artificial Neural Network (34) Identify malicious nodes using artificial neural networks. This work identify malicious nodes in VANET. The attack that considered for this network is DOS attack. Targets of this paper are development a secure communication network and d...

  12. [16]

    First, constructing wireless sensor network based on energy field through the energy potential field of the routing protocol

    Energy Based (57) Propose a detection method of malicious nodes of wireless sensor network based on energy field. First, constructing wireless sensor network based on energy field through the energy potential field of the routing protocol. This article uses an energy predictio...

  13. [17]

    This article after detecting malicious nodes, create a post-detection game between normal and malicious nodes to happen coexistence

    Based on Game Theory (17) Used game theory for interaction between normal and malicious nodes to detect and coexistence. This article after detecting malicious nodes, create a post-detection game between normal and malicious nodes to happen coexistence. This paper uses a speci...

  14. [18]

    The source and destination and the member nodes are assigned with its unique id so that the nodes can be uniquely identified

    Node ID Based (52) Proposed an algorithm to find link failures by acquiring information from source and destination using agents and repairing the link. The source and destination and the member nodes are assigned with its unique id so that the nodes can be uniquely identified...

  15. [20]

    We categorized used methods in some groups and compared those methods in table

    Conclusion and future works In this study we read about 70 papers on malicious node detection and extract their methods. We categorized used methods in some groups and compared those methods in table. The techniques based on authentication, encryption and grouping, clustering ...

  16. [21]

    An outlier mining-based malicious node detection model for hybrid P2P networks

    Meng X, Ren S. An outlier mining-based malicious node detection model for hybrid P2P networks. Comput Networks. 2016;108:29–39

    2016

  17. [22]

    Available online at www.sciencedirect.com

    Science PC, Authors T, This EB V, By-nc-nd CC, Conference I, Authors T, et al. Available online at www.sciencedirect.com. 2015;49:264–73

    2015

  18. [23]

    A perspective to adopt continuous dynamic cognition for malicious node detection in heterogeneous networks

    Sunilkumar G, Thriveni J, Venugopal KR, Patnaik LM. A perspective to adopt continuous dynamic cognition for malicious node detection in heterogeneous networks. Procedia Comput Sci [Internet]. 2015;46(Icict 2014):997–1004. Available from: http://dx.doi.org/10.1016/j.procs.2015.01.010

    2015 doi

  19. [24]

    Detection of malicious node using improved CONFIDANT technique in Ad-hoc Networks

    Devi N, Sunitha R. Detection of malicious node using improved CONFIDANT technique in Ad-hoc Networks. Proc 2nd IEEE Int Conf Eng Technol ICETECH 2016. 2016;(March):604–10

    2016

  20. [25]

    RETENTION: A reactive trust-based mechanism to detect and punish malicious nodes in ad hoc grid environments

    Braga RB, Chaves IA, De Oliveira CT, Andrade RMC, Neuman De Souza J, Martin H, et al. RETENTION: A reactive trust-based mechanism to detect and punish malicious nodes in ad hoc grid environments. J Netw Comput Appl. 2013;36(1):274–83

    2013

  21. [27]

    Adaptive security design with malicious node detection in cluster-based sensor networks

    Hsieh MY, Huang YM, Chao HC. Adaptive security design with malicious node detection in cluster-based sensor networks. Comput Commun. 2007;30(11–12):2385–400

    2007

  22. [28]

    An Efficient Energy based Detection of Malicious Node in Mobile Wireless Sensor Networks

    Sharmila S, Umamaheswari G. An Efficient Energy based Detection of Malicious Node in Mobile Wireless Sensor Networks. J Inst Eng Ser B. 2012;93(1):25–30

    2012

  23. [29]

    An overview of diverse protocols and probing of malicious node using AODV in VANET environment

    Bedi R, Kaur I, Gangwar RC. An overview of diverse protocols and probing of malicious node using AODV in VANET environment. 2016 3rd Int Conf Comput Sustain Glob Dev. 2016;3296–300

    2016

  24. [30]

    A PKI Based Mesh Router Authentication Scheme to Protect from Malicious Node in Wireless Mesh Network * 1

    Lee KH, Hong CS. A PKI Based Mesh Router Authentication Scheme to Protect from Malicious Node in Wireless Mesh Network * 1. 2009;405–13

    2009

  25. [31]

    An Approach to Detect Malicious Node for Delay Tolerant Networks Department of Information Technology ; R ; XTRij TR / L EA L ; EAR ; d ( TRij , TRj ) = Ri

    Chandavale AA. An Approach to Detect Malicious Node for Delay Tolerant Networks Department of Information Technology ; R ; XTRij TR / L EA L ; EAR ; d ( TRij , TRj ) = Ri. 2015;1–6

    2015

  26. [32]

    BAN-trust: An attack-resilient malicious node detection scheme for body area networks

    Li W, Zhu X. BAN-trust: An attack-resilient malicious node detection scheme for body area networks. 2016 Int Conf Comput Netw Commun ICNC 2016. 2016

    2016

  27. [33]

    Reputation based malicious node detection in OppNets

    Ahmad A, Alajeely M, Doss R. Reputation based malicious node detection in OppNets. 2016 13th Int Jt Conf Comput Sci Softw Eng JCSSE 2016. 2016

    2016

  28. [34]

    Identification of malicious nodes in an AODV pure ad hoc network through guard nodes

    Raza I, Hussain SA. Identification of malicious nodes in an AODV pure ad hoc network through guard nodes. Comput Commun. 2008;31(9):1796–802

    2008

  29. [35]

    Snášel V, Abraham A, Krömer P, Pant M, Muda AK. Innovations in bio-inspired computing and applications: Proceedings of the 6th international conference on innovations in bio-inspired computing and applications (IBICA 2015) held in Kochi, India during december 16-18, 2015. Adv ...

    2015

  30. [36]

    Joint network lifetime and delay optimization for topology control in heterogeneous wireless multi-hop networks

    Gui J, Zeng Z. Joint network lifetime and delay optimization for topology control in heterogeneous wireless multi-hop networks. Comput Commun [Internet]. 2015;59:24–36. Available from: http://dx.doi.org/10.1016/j.comcom.2015.01.005

    2015 doi

  31. [38]

    An accurate and precise malicious node exclusion mechanism for ad hoc networks

    Ferraz LHG, Velloso PB, Duarte OCMB. An accurate and precise malicious node exclusion mechanism for ad hoc networks. Ad Hoc Networks [Internet]. 2014;19:142–55. Available from: http://dx.doi.org/10.1016/j.adhoc.2014.03.001

    2014 doi

  32. [39]

    Fuzzy Trust Protocol for Malicious Node Detection in Wireless Sensor Networks

    Ram Prabha V, Latha P. Fuzzy Trust Protocol for Malicious Node Detection in Wireless Sensor Networks. Wirel Pers Commun. 2017;94(4):2549–59

    2017

  33. [40]

    A Survey on Selfish Node Detection in Mobile Ad Hoc Network

    Misra S. A Survey on Selfish Node Detection in Mobile Ad Hoc Network. Gyancity J Electron Comput Sci. 2019;3(2):25–8

    2019

  34. [41]

    A Survey on Malicious Node Detection in Wireless Sensor Networks

    Nakul P. A Survey on Malicious Node Detection in Wireless Sensor Networks. 2013;2(1)

    2013

  35. [42]

    A Survey on Malicious Nodes in Mobile Ad hoc Network Erode Arts and Science College Erode Arts and Science College

    Subbulakshmi MS, Mohana SJ. A Survey on Malicious Nodes in Mobile Ad hoc Network Erode Arts and Science College Erode Arts and Science College. 4(4):137–42

  36. [43]

    Distributed detection of mobile malicious node attacks in wireless sensor networks

    Ho JW, Wright M, Das SK. Distributed detection of mobile malicious node attacks in wireless sensor networks. Ad Hoc Networks [Internet]. 2012;10(3):512–23. Available from: http://dx.doi.org/10.1016/j.adhoc.2011.09.006

    2012 doi

  37. [44]

    Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications

    Sonekar S V, Kshirsagar MM. Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications. 2017;515. Available from: http://link.springer.com/10.1007/978-981-10-3153-3

    2017 doi

  38. [45]

    Dynamic camouflage event based malicious node detection architecture

    Pongaliur K, Xiao L, Liu AX. Dynamic camouflage event based malicious node detection architecture. J Supercomput. 2013;64(3):717–43

    2013

  39. [46]

    Impact of malicious node on broadcast schemes

    Rahim A, Muyaha F Bin. Impact of malicious node on broadcast schemes. Commun Comput Inf Sci. 2009;58:86–92

    2009

  40. [47]

    Detection of malicious node in wireless sensor network

    Padmaja P, Marutheswar G V. Detection of malicious node in wireless sensor network. Proc - 7th IEEE Int Adv Comput Conf IACC 2017. 2017;193–8

    2017

  41. [48]

    Impact of malicious node on secure incentive based advertisement distribution (SIBAD) in VANET

    Padmapriya V, Smitha S, Sujatha DN, Venugopal KR. Impact of malicious node on secure incentive based advertisement distribution (SIBAD) in VANET. Proc - 7th IEEE Int Adv Comput Conf IACC 2017. 2017;226–32

    2017

  42. [49]

    Challenge evaluation algorithm to identify malicious node in MANET

    Kulkarni SB, Yuvaraju BN. Challenge evaluation algorithm to identify malicious node in MANET. Proc - 7th IEEE Int Adv Comput Conf IACC 2017. 2017;297–302

    2017

  43. [50]

    A review on node activity detection, selfish & malicious behavioral patterns using watchdog algorithm

    Nimje C, Junghare P. A review on node activity detection, selfish & malicious behavioral patterns using watchdog algorithm. Proc Int Conf Inven Syst Control ICISC 2017. 2017;1–5

    2017

  44. [51]

    CRCMD&R: Cluster and Reputation based cooperative malicious node Detection & Removal scheme in MANETs

    Sharma S, Gambhir S. CRCMD&R: Cluster and Reputation based cooperative malicious node Detection & Removal scheme in MANETs. Proc 2017 11th Int Conf Intell Syst Control ISCO 2017. 2017;336–40

    2017

  45. [52]

    Energy Efficient Malicious Node Detection System in Wireless Sensor Networks

    Santhosh J, Rajivkannan A, Vijayarajeswari R. Energy Efficient Malicious Node Detection System in Wireless Sensor Networks. Asian J Res Soc Sci Humanit. 2017;7(2):624

    2017

  46. [53]

    Detecting malicious node in network using packet delivery ratio

    Tyagi S, Gopal G, Garg V. Detecting malicious node in network using packet delivery ratio. Comput Sustain Glob Dev (INDIACom), 2016 3rd Int Conf. 2016;3313–8

    2016

  47. [55]

    A residual error analysis based secure CS approach for malicious node attack

    Cao Y, Yan J, Zhu WP. A residual error analysis based secure CS approach for malicious node attack. IEEE CITS 2016 - 2016 Int Conf Comput Inf Telecommun Syst. 2016;(2)

    2016

  48. [56]

    A cooperative approach for malicious node detection in impromptu wireless networks

    Gupta D, Dhawale C, Misra S. A cooperative approach for malicious node detection in impromptu wireless networks. 2016 Int Conf Comput Tech Inf Commun Technol ICCTICT 2016 - Proc. 2016;624–9

    2016

  49. [57]

    Downloaded from http://iranpaper.ir

    Conference I, Technologies A. Downloaded from http://iranpaper.ir. 2015;206–11

    2015

  50. [58]

    Malicious node detection using ID- based authentication technique

    Gupta A, Mohit P, Karati A, Amin R, Biswas GP. Malicious node detection using ID- based authentication technique. 2016 3rd Int Conf Recent Adv Inf Technol RAIT 2016. 2016;398–403

    2016

  51. [59]

    Detection of malicious node and development of routing strategy in VANET

    Jahan R, Suman P. Detection of malicious node and development of routing strategy in VANET. 3rd Int Conf Signal Process Integr Networks, SPIN 2016. 2016;472–6

    2016

  52. [60]

    Top-k Query Processing and Malicious Node Identification Based on Node Grouping in MANETs

    Tsuda T, Komai Y, Hara T, Nishio S. Top-k Query Processing and Malicious Node Identification Based on Node Grouping in MANETs. IEEE Access. 2016;4:993–1007

    2016

  53. [61]

    A loom for revealing selfish and malicious node in cluster based adhoc wireless networks

    Sonekar S V., Kshirsagar M. A loom for revealing selfish and malicious node in cluster based adhoc wireless networks. 2015 Int Conf Comput Sustain Glob Dev INDIACom

    2015

  54. [62]

    Malicious Node Traceback in Opportunistic Networks Using Merkle Trees

    Alajeely M, Ahmad A, Doss R. Malicious Node Traceback in Opportunistic Networks Using Merkle Trees. Proc - 2015 IEEE Int Conf Data Sci Data Intensive Syst 8th IEEE Int Conf Cyber, Phys Soc Comput 11th IEEE Int Conf Green Comput Commun 8th IEEE Inte. 2015;147–52

    2015

  55. [63]

    ZIDS: Zonal-based Intrusion Detection System for studying the malicious node behaviour in MANET

    Sangeetha V, Kumar SS. ZIDS: Zonal-based Intrusion Detection System for studying the malicious node behaviour in MANET. 2015 Int Conf Emerg Res Electron Comput Sci Technol ICERECT 2015. 2016;276–81

    2015

  56. [64]

    Detection and avoidance of malicious node in MANET

    Patel KS, Shah JS. Detection and avoidance of malicious node in MANET. IEEE Int Conf Comput Commun Control IC4 2015. 2016

    2015

  57. [65]

    Energy efficient detection of malicious nodes using secure clustering with load balance and reliable node disjoint multipath routing in Wireless Sensor Networks

    Bhat P, Reddy KS. Energy efficient detection of malicious nodes using secure clustering with load balance and reliable node disjoint multipath routing in Wireless Sensor Networks. 2015 Int Conf Adv Comput Commun Informatics, ICACCI 2015. 2015;954–8

    2015

  58. [66]

    Improved EAACK scheme for detection and isolation of a malicious node in MANET

    Patil A, Marathe N, Padiya P. Improved EAACK scheme for detection and isolation of a malicious node in MANET. Proc 2015 Int Conf Appl Theor Comput Commun Technol iCATccT 2015. 2016;529–33

    2015

  59. [67]

    Malicious node detection in OppNets using hash chain technique

    Alajeely M, Ahmad A, Doss R. Malicious node detection in OppNets using hash chain technique. Proc 2015 4th Int Conf Comput Sci Netw Technol ICCSNT 2015. 2016;(Iccsnt):925–30

    2015

  60. [68]

    Mitigating the wrongful bandwidth occupation by malicious node in public wireless access network

    Nitta S, Shigeyasu T. Mitigating the wrongful bandwidth occupation by malicious node in public wireless access network. Proc - 2015 18th Int Conf Network-Based Inf Syst NBiS

    2015

  61. [69]

    Top-k query processing and malicious node identification against data replacement attack in MANETs

    Tsuda T, Komai Y, Sasaki Y, Hara T, Nishio S. Top-k query processing and malicious node identification against data replacement attack in MANETs. Proc - IEEE Int Conf Mob Data Manag. 2014;1:279–88

    2014

  62. [70]

    Identifying malicious node using trust value in cluster based MANET (IMTVCM)

    Annadurai P, Vijayalaksmi S. Identifying malicious node using trust value in cluster based MANET (IMTVCM). 2014 Int Conf Control Instrumentation, Commun Comput Technol ICCICCT 2014. 2014;1150–8

    2014

  63. [71]

    Malicious node detection using minimal event cycle computation method in wireless sensor networks

    Priyanka JSA, Tephillah S, Balamurugan AM. Malicious node detection using minimal event cycle computation method in wireless sensor networks. Int Conf Commun Signal Process ICCSP 2014 - Proc. 2014;905–9

    2014

  64. [72]

    Performance analysis of proposed mobile autonomous agent for detection of malicious node and protecting against attacks in MANET

    Stephen John T, Aranganathan A. Performance analysis of proposed mobile autonomous agent for detection of malicious node and protecting against attacks in MANET. Int Conf Commun Signal Process ICCSP 2014 - Proc. 2014;1937–41

    2014

  65. [73]

    Malicious node detection for the future network security from epistemic uncertainties

    Huang X, Ahmed MR, Cui H, Shutao L. Malicious node detection for the future network security from epistemic uncertainties. 2013 16th Int Symp Wirel Pers Multimed Commun. 2013;1–6

    2013

  66. [75]

    Uniform node sampling service robust against collusions of malicious nodes

    Anceaume E, Busnel Y, Sericola B. Uniform node sampling service robust against collusions of malicious nodes. Proc Int Conf Dependable Syst Networks. 2013

    2013

  67. [76]

    Cross layer approach to detect malicious node in MANET

    Patil VN, Thorat SA. Cross layer approach to detect malicious node in MANET. 2013 4th Int Conf Comput Commun Netw Technol ICCCNT 2013. 2013

    2013

  68. [77]

    Malicious node detection scheme based on energy potential field in wireless sensor networks

    Xu X, Zhao T, Zheng X, Wang H, Fang W. Malicious node detection scheme based on energy potential field in wireless sensor networks. 7th Int Conf Comput Converg Technol (ICCCT), 2012 [Internet]. 2012;637–42. Available from: http://www.scopus.com/inward/record.url?eid=2-s2.0- 84...

    2012

  69. [78]

    Adoption of cognition for malicious node detection in homogeneous and heterogeneous wireless networks

    Sunilkumar G, Shivaprakash K, Thriveni J, Venugopal KR, Patnaik LM. Adoption of cognition for malicious node detection in homogeneous and heterogeneous wireless networks. Proc - 2012 Int Symp Cloud Serv Comput ISCOS 2012. 2013;36–41

    2012

  70. [79]

    Detection of malicious node in wireless sensor network based on data mining

    Singh M, Mehta G, Vaid C, Oberoi P. Detection of malicious node in wireless sensor network based on data mining. Proc Turing 100 - Int Conf Comput Sci ICCS 2012. 2012;291–4

    2012

  71. [80]

    A multivariate classification algorithm for malicious node detection in large-scale WSNs

    Dai H, Liu H, Jia Z, Chen T. A multivariate classification algorithm for malicious node detection in large-scale WSNs. Proc 11th IEEE Int Conf Trust Secur Priv Comput Commun Trust - 11th IEEE Int Conf Ubiquitous Comput Commun IUCC-2012. 2012;239–45

    2012

  72. [81]

    PPN: Prime product number based malicious node detection scheme for MANETs

    Gambhir S, Sharma S. PPN: Prime product number based malicious node detection scheme for MANETs. Proc 2013 3rd IEEE Int Adv Comput Conf IACC 2013. 2013;335– 40

    2013

  73. [82]

    Robust node selection for cooperative spectrum sensing with malicious users

    Zeng K, Wang J, Li S, Čabrić D. Robust node selection for cooperative spectrum sensing with malicious users. Proc - IEEE Mil Commun Conf MILCOM. 2011;79–84

    2011

  74. [83]

    Secure aggregation with malicious node revocation in sensor networks

    Chen B, Yu H. Secure aggregation with malicious node revocation in sensor networks. Proc - Int Conf Distrib Comput Syst. 2011;581–92

    2011

  75. [84]

    CBDS: A cooperative bait detection scheme to prevent malicious node for MANET based on hybrid defense architecture

    Chang JM, Tsou PC, Chao HC, Chen JL. CBDS: A cooperative bait detection scheme to prevent malicious node for MANET based on hybrid defense architecture. 2011 2nd Int Conf Wirel Commun Veh Technol Inf Theory Aerosp Electron Syst Technol Wirel VITAE 2011. 2011;1–5

    2011

  76. [85]

    Malicious node detection via physical layer data

    Hardy TJ, Martin RK, Thomas RW. Malicious node detection via physical layer data. Conf Rec - Asilomar Conf Signals, Syst Comput. 2010;1432–6

    2010

  77. [86]

    CENDA: Camouflage Event Based Malicious Node Detection Architecture

    Pongaliur K, Li Xiao, Liu AX. CENDA: Camouflage Event Based Malicious Node Detection Architecture. 2009;848–55

    2009

  78. [87]

    Behavior analysis of malicious node in the different routing algorithms in Mobile Ad Hoc Network (MANET)

    Mishra D, Jain YK, Agrawal S. Behavior analysis of malicious node in the different routing algorithms in Mobile Ad Hoc Network (MANET). ACT 2009 - Int Conf Adv Comput Control Telecommun Technol. 2009;(i):621–3

    2009

  79. [88]

    Combined malicious node discovery and self-destruction technique for wireless sensor networks

    Curiac DI, Plastoi M, Banias O, Volosencu C, Tudoroiu R, Doboli A. Combined malicious node discovery and self-destruction technique for wireless sensor networks. Proc - 2009 3rd Int Conf Sens Technol Appl SENSORCOMM 2009. 2009;436–41

    2009

  80. [89]

    Exploiting malicious node detection for lifetime extension of sensor networks

    Behnke R, Salzmann J, Lieckfeldt D, Thurow K, Golatowski F, Timmermann D. Exploiting malicious node detection for lifetime extension of sensor networks. 2009 6th IEEE Consum Commun Netw Conf CCNC 2009. 2009;1–2

    2009

  81. [90]

    A novel reputation model for malicious node detection in wireless sensor network

    Yin G, Yang G, Wu Y, Yu X, Zuo D. A novel reputation model for malicious node detection in wireless sensor network. 2008 Int Conf Wirel Commun Netw Mob Comput WiCOM 2008. 2008;1–4

    2008

  82. [91]

    System for Defending Against Malicious Node Behavior

    Akbani R, Korkmaz T, Raju GVS. System for Defending Against Malicious Node Behavior. Commun Soc. 2008;0–4

    2008

  83. [92]

    Malicious node detection in wireless sensor networks using an autoregression technique

    Curiac DI, Banias O, Dragan F, Volosencu C, Dranga O. Malicious node detection in wireless sensor networks using an autoregression technique. 3rd Int Conf Netw Serv 2007. 2007

    2007

  84. [93]

    Network court protocol and malicious node conviction

    Li N, Lee D. Network court protocol and malicious node conviction. Proc - Int Conf Netw Protoc ICNP. 2007;1:336–7

    2007

  85. [94]

    Malicious node detection in wireless sensor networks

    Atassi A, Sayegh N, Elhajj I, Chehab A, Kayssi A. Malicious node detection in wireless sensor networks. Proc - 27th Int Conf Adv Inf Netw Appl Work WAINA 2013. 2013;00(C):456–61

    2013

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