The DOI matches its Crossref record.
Real Time Intrusion and Wormhole Attack Detection in Internet of Things
Open accessAbstract
There are currently more objects connected to the Internet than people in the world.This gap will continue to grow, as more objects gain the ability to directly interface with the Internet.Providing security in IoT is challenging as the devices are resource constrained, the communication links are lossy, and the devices use a set of novel IoT technologies such as RPL and 6LoWPAN.Due to this it is easy to attack in IoT network.The proposed system is a novel intrusion detection system for the IoT, which is capable of detecting Wormhole attack and attacker.The proposed methods uses the location information of node and neighbor information to identify the Wormhole attack and received signal strength to identify attacker nodes.Design of such system will help in securing the IoT network and may prevents such attacks.This method is very energy efficient and only takes fixed number of UDP packets for attack detection, hence it is beneficial for resource constrained environment.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
A survey of intrusion detection in Internet of Things
Zarpelão, B. B., Miani, R. S., Kawakani, C. T., & de Alvarenga, S. C. (2017) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2017.02.009 · Cited by 1,022
A Survey on Internet of Things and Cloud Computing for Healthcare
Dang, L. M., Piran, M. J., Han, D., Min, K., & Moon, H. (2019) · Electronics · doi:10.3390/electronics8070768 · Cited by 640
A bibliometric method for analyzing IoT hacking solutions in network services
KOMBATE, Y., HOUNGUE, P., & OUYA, S. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.07.020 · Cited by 7
Hussain, B., Elmedany, W., & Sharif, M. S. (2022) · 2022 International Conference on Data Analytics for Business and Industry (ICDABI) · doi:10.1109/icdabi56818.2022.10041508 · Cited by 4
Metadata
- Authors
- Pongle, P., & Chavan, G.
- Venue
- International Journal of Computer Applications
- Publisher
- Foundation of Computer Science
- Published
- 2015-07-18
- Volume
- 121
- Issue
- 9
- Pages
- 1-9
- ISSN
- 0975-8887 (ISSN-L)
- OpenAlex
- W1569819710
- Cited by
- 213
- References
- 0
- FWCI
- 14.97
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Real Time Intrusion and Wormhole Attack Detection in Internet of Things. https://app.cochat.ai/papers/10.5120/21565-4589
- The paper
- Pongle, P., & Chavan, G. (2015). Real Time Intrusion and Wormhole Attack Detection in Internet of Things. International Journal of Computer Applications. https://doi.org/10.5120/21565-4589
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.