A computationally intelligent approach to the detection of wormhole attacks in wireless sensor networks
| dc.contributor.author | Shaon, Mohammad | |
| dc.contributor.examiningcommittee | Mcleod, Robert.D (Electrical and Computer Engineering) Thulasiraman, Parimala (Computer Science) | en_US |
| dc.contributor.supervisor | Ferens, Ken (Electrical and Computer Engineering) | en_US |
| dc.date.accessioned | 2017-01-05T16:15:28Z | |
| dc.date.available | 2017-01-05T16:15:28Z | |
| dc.date.issued | 2015-07-29 | en_US |
| dc.date.issued | 2016-07-29 | en_US |
| dc.date.issued | 2016-07-29 | en_US |
| dc.degree.discipline | Electrical and Computer Engineering | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.abstract | This thesis proposes an innovative wormhole detection scheme to detect wormhole attacks using computational intelligence and an artificial neural network (ANN). The aim of the proposed research is to develop a detection scheme that can detect wormhole attacks (In-band, out of band, hidden wormhole attack, active wormhole attack) in both uniformly and non-uniformly distributed sensor networks. Furthermore, the proposed research does not require any special hardware and causes no significant network overhead throughout the network. Most importantly, the probable location of the wormhole nodes can be tracked down by the proposed ANN-based detection scheme. We evaluate the efficacy of the proposed detection scheme in terms of detection accuracy, false positive rate, and false negative rate. The performance of the proposed model is also compared with other machine learning techniques (i.e. SVM and regularized nonlinear logistic regression (LR) based detection models) based detection schemes. The simulation results show that proposed ANN-based detection model outperforms the SVM and LR based detection schemes in terms of detection accuracy, false positive rate, and false negative rates. | en_US |
| dc.description.note | February 2017 | en_US |
| dc.identifier.citation | Mohammad Nurul Afsar Shaon and Ken Ferens, “Wireless Sensor Network Wormhole Detection using an Artificial Neural Network,” ICWN, pp. 115–120, 2015. | en_US |
| dc.identifier.citation | Mohammad Nurul Afsar Shaon, Ken Ferens and Mike Ferens, “Wormhole Attack Detection Using Discrete Wavelet Transform,” ICWN, Las Vegas, pp 29-35, 2016. | en_US |
| dc.identifier.citation | Mohammad Nurul Afsar Shaon,Ken Ferens and Mike Ferens, “Wormhole Attack Detection Using Variance Fractal Dimension,” SAM, Las vegas, pp 55-62, 2016. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/31981 | |
| dc.language.iso | eng | en_US |
| dc.publisher | World Comp,14th International Conference on Wireless Networks, 2015 | en_US |
| dc.publisher | World Comp,15th International Conference on Wireless Networks, 2016 | en_US |
| dc.publisher | World Comp, International Conference on Security and Management, 2016 | en_US |
| dc.rights | open access | en_US |
| dc.subject | Artificial neural network, Wormhole attack detection,Non-uniform sensor distribution | en_US |
| dc.title | A computationally intelligent approach to the detection of wormhole attacks in wireless sensor networks | en_US |
| dc.type | master thesis | en_US |