TY - GEN
T1 - An intelligent approach to detect probe request attacks in IEEE 802.11 networks
AU - Ratnayake, Deepthi N.
AU - Kazemian, Hassan B.
AU - Yusuf, Syed A.
AU - Abdullah, Azween B.
N1 - © 2011 International Federation for Information Processing. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1007/978-3-642-23957-1_42
PY - 2011/11/2
Y1 - 2011/11/2
N2 - In Wireless Local Area Networks (WLAN), beacon, probe request and response messages are unprotected, so the information is visible to sniffers. Probe requests can be sent by anyone with a legitimate Media Access Control (MAC) address, as association to the network is not required at this stage. Legitimate MAC addresses can be easily spoofed to bypass Access Point (AP) access lists. Attackers take advantage of these vulnerabilities and send a flood of probe request frames which can lead to a Denial-of-Service (DoS) to legitimate stations. This paper discusses an intelligent approach to recognise probe request attacks in WLANs. The research investigates and analyses WLAN traffic captured on a home wireless network, and uses supervised feedforward neural network with 4 input neurons, 2 hidden layers and an output neuron to determine the results. The computer simulation results demonstrate that this approach improves detection of MAC spoofing and probe request attacks considerably.
AB - In Wireless Local Area Networks (WLAN), beacon, probe request and response messages are unprotected, so the information is visible to sniffers. Probe requests can be sent by anyone with a legitimate Media Access Control (MAC) address, as association to the network is not required at this stage. Legitimate MAC addresses can be easily spoofed to bypass Access Point (AP) access lists. Attackers take advantage of these vulnerabilities and send a flood of probe request frames which can lead to a Denial-of-Service (DoS) to legitimate stations. This paper discusses an intelligent approach to recognise probe request attacks in WLANs. The research investigates and analyses WLAN traffic captured on a home wireless network, and uses supervised feedforward neural network with 4 input neurons, 2 hidden layers and an output neuron to determine the results. The computer simulation results demonstrate that this approach improves detection of MAC spoofing and probe request attacks considerably.
KW - DoS Attacks
KW - IEEE 802.11
KW - Probe Request Flooding Attacks
KW - Supervised Feedforward Neural Network
KW - Wireless
UR - http://www.scopus.com/inward/record.url?scp=80055032476&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-23957-1_42
DO - 10.1007/978-3-642-23957-1_42
M3 - Conference contribution
AN - SCOPUS:80055032476
SN - 9783642239564
T3 - IFIP Advances in Information and Communication Technology
SP - 372
EP - 381
BT - Engineering Applications of Neural Networks - 12th INNS EANN-SIG International Conference, EANN 2011 and 7th IFIP WG 12.5 International Conference, AIAI 2011, Proceedings
PB - Springer Nature
T2 - 12th INNS EANN-SIG International Conference on Engineering Applications of Neural Networks, EANN 2011
Y2 - 15 September 2011 through 18 September 2011
ER -