A Panacea to Soft Computing Approach for Sinkhole Attack Classification in a Wireless Sensor Networks Environment
| dc.creator | Nwankwo, Kenneth E., Abdulhamid, Shafi’i Mohammad, Ojeniyi, Joseph A., Misra, Sanjay, Jonathan, Oluranti, Ahuja, Ravin | |
| dc.date | 2021 | |
| dc.date.accessioned | 2025-04-15T11:55:13Z | |
| dc.description | Small sensor nodes with the capability to sense and process data make up a wireless sensor network (WSN). This environment has limitations of low energy, low computational power and simple routing protocols; making is susceptible to attacks such as sinkhole attack. This attack happens when the enemy node in the network camouflages as a genuine node nearest to the base station, thereby have information sent by a source node to another destination node travel through it, giving it chance to alter, drop or delay information from reaching to the base station as intended. In our paper, the research developed a sinkhole detection technique, an enhancement of ant colony optimization by including a hash table in the ant colony optimization technique to advance sinkhole attack detection and reduce fa1se alarm rate in a wireless sensor network. An increase in the detection rate of 96% was achieved and result out performed other related research works when compared and further research discussed. | |
| dc.format | application/pdf | |
| dc.identifier | http://eprints.covenantuniversity.edu.ng/18268/ | |
| dc.identifier.uri | https://repository.covenantuniversity.edu.ng/handle/123456789/48763 | |
| dc.language | en | |
| dc.subject | QA Mathematics, QA75 Electronic computers. Computer science | |
| dc.title | A Panacea to Soft Computing Approach for Sinkhole Attack Classification in a Wireless Sensor Networks Environment | |
| dc.type | Conference or Workshop Item |
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