%0 Journal Article %1 suman2016method %A Su Man, Nam %A Tae Ho, Cho %D 2016 %J Advances in Vision Computing: An International Journal (AVC) %K En-route Filtering Network Networks Protocols Security Sensor Simulation Statistical Wireless models %N 3 %P 8 %R 10.5121/avc.2016.3302 %T A METHOD FOR DETECTING FALSE POSITIVE AND FALSE NEGATIVE ATTACKS USING SIMULATION MODELS IN STATISTICAL ENROUTE FILTERING BASED WSNS %U http://aircconline.com/avc/V3N3/3316avc02.pdf %V 3 %X In wireless sensor networks, adversaries compromise sensor nodes to damage the network though potential threats such as false positive and false negative attacks. The false positive attacks cause energy drain and false alarms, and false negative attacks generate information loss. To address the false positive attacks in the sensor network, a statistical en-route filtering (SEF) detects the false report in intermediate nodes. Even though the scheme detects the false report against the false positive attack, it is difficult to detect false MACs in a legitimate report against the false negative attack in the SEF-based WSN. Our proposed method effectively detects the false positive and false negative attacks in the sensor network through a simulation model. The experimental results indicate that the proposed method increase detection power while maintaining the energy consumption of the network against the false positive and false negative attacks.

@article{suman2016method, abstract = {In wireless sensor networks, adversaries compromise sensor nodes to damage the network though potential threats such as false positive and false negative attacks. The false positive attacks cause energy drain and false alarms, and false negative attacks generate information loss. To address the false positive attacks in the sensor network, a statistical en-route filtering (SEF) detects the false report in intermediate nodes. Even though the scheme detects the false report against the false positive attack, it is difficult to detect false MACs in a legitimate report against the false negative attack in the SEF-based WSN. Our proposed method effectively detects the false positive and false negative attacks in the sensor network through a simulation model. The experimental results indicate that the proposed method increase detection power while maintaining the energy consumption of the network against the false positive and false negative attacks. }, added-at = {2018-02-02T13:08:51.000+0100}, author = {Su Man, Nam and Tae Ho, Cho}, biburl = {https://www.bibsonomy.org/bibtex/281194799aaf22b0f4ed32a118e99dd8d/vinston}, doi = {10.5121/avc.2016.3302}, ee = {https://doi.org/10.1007/978-3-642-15231-3_32}, interhash = {70c4808af69147e30b72c8f34e5a620c}, intrahash = {81194799aaf22b0f4ed32a118e99dd8d}, journal = {Advances in Vision Computing: An International Journal (AVC)}, keywords = {En-route Filtering Network Networks Protocols Security Sensor Simulation Statistical Wireless models}, month = {September}, number = 3, pages = 8, timestamp = {2018-02-02T13:08:51.000+0100}, title = {A METHOD FOR DETECTING FALSE POSITIVE AND FALSE NEGATIVE ATTACKS USING SIMULATION MODELS IN STATISTICAL ENROUTE FILTERING BASED WSNS}, url = {http://aircconline.com/avc/V3N3/3316avc02.pdf}, volume = 3, year = 2016 }