TY - JOUR
T1 - A Joint Rogue Access Point Localization and Outlier Detection Scheme Leveraging Sparse Recovery Technique
AU - Pu, Qiaolin
AU - Ng, Joseph Kee Yin
AU - Zhou, Mu
AU - Wang, Jie
PY - 2021/2
Y1 - 2021/2
N2 - With the pervasive deployments of Access Point (AP) in Wireless Local Area Network (WLAN), rogue AP has emerged as such a large threat to user's privacy, that it is expected to be detected and located accurately. Hence, in this paper, we propose a novel rogue AP localization scheme leveraging sparse recovery technique, which consists of three steps: 1) Coarse localization, which is the result of comparing the online records with clustered fingerprint database. A novel Object Weighting Affinity Propagation (OWAP) clustering method is proposed to group the offline fingerprints. When computing the similarities, unlike traditional affinity propagation clustering method which views each object equally, we utilize the prior physical coordinates information to assign weight to each object. 2) Compressive sensing (CS) kernel optimization, in which the minimum number requirement of monitors in localization system is deduced through analyzing the problem formulation theoretically, and an Equiangular Tight Frame (ETF) based monitors selection scheme is presented to achieve higher location accuracy. 3) Joint fine rogue AP localization and outlier detection through a formulation of an improved CS based sparse recovery model. It could localize the rogue AP and identify the monitors whose readings are either not available or erroneous simultaneously, which increases the localization robustness. We operate simulations as well as experiments to verify the superiority of the proposed scheme both theoretically and practically.
AB - With the pervasive deployments of Access Point (AP) in Wireless Local Area Network (WLAN), rogue AP has emerged as such a large threat to user's privacy, that it is expected to be detected and located accurately. Hence, in this paper, we propose a novel rogue AP localization scheme leveraging sparse recovery technique, which consists of three steps: 1) Coarse localization, which is the result of comparing the online records with clustered fingerprint database. A novel Object Weighting Affinity Propagation (OWAP) clustering method is proposed to group the offline fingerprints. When computing the similarities, unlike traditional affinity propagation clustering method which views each object equally, we utilize the prior physical coordinates information to assign weight to each object. 2) Compressive sensing (CS) kernel optimization, in which the minimum number requirement of monitors in localization system is deduced through analyzing the problem formulation theoretically, and an Equiangular Tight Frame (ETF) based monitors selection scheme is presented to achieve higher location accuracy. 3) Joint fine rogue AP localization and outlier detection through a formulation of an improved CS based sparse recovery model. It could localize the rogue AP and identify the monitors whose readings are either not available or erroneous simultaneously, which increases the localization robustness. We operate simulations as well as experiments to verify the superiority of the proposed scheme both theoretically and practically.
KW - clustering
KW - outlier detection
KW - Rogue Access Point localization
KW - sparse recovery
KW - Wireless LAN
UR - http://www.scopus.com/inward/record.url?scp=85100463417&partnerID=8YFLogxK
U2 - 10.1109/TVT.2021.3055263
DO - 10.1109/TVT.2021.3055263
M3 - Journal article
AN - SCOPUS:85100463417
SN - 0018-9545
VL - 70
SP - 1866
EP - 1877
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 2
ER -