Jointly beam stealing attackers detection and localization without training: an image processing viewpoint

doi:10.1007/s11704-022-1550-6

Front. Comput. Sci.

2023, Vol. 17

Issue (3) : 173704 https://doi.org/10.1007/s11704-022-1550-6

RESEARCH ARTICLE

Jointly beam stealing attackers detection and localization without training: an image processing viewpoint

Yaoqi YANG¹, Xianglin WEI²(

), Renhui XU¹(

), Weizheng WANG³, Laixian PENG¹, Yangang WANG²

¹. College of Communication Engineer, Army Engineering University of PLA, Nanjing 210000, China
². The 63rd Research Institute, National University of Defense Technology, Nanjing 210007, China
³. Department of Computer Science, City University of Hong Kong, Hong Kong 999077, China

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Abstract

Recently revealed beam stealing attacks could greatly threaten the security and privacy of IEEE 802.11ad communications. The premise to restore normal network service is detecting and locating beam stealing attackers without their cooperation. Current consistency-based methods are only valid for one single attacker and are parameter-sensitive. From the viewpoint of image processing, this paper proposes an algorithm to jointly detect and locate multiple beam stealing attackers based on RSSI (Received Signal Strength Indicator) map without the training process involved in deep learning-based solutions. Firstly, an RSSI map is constructed based on interpolating the raw RSSI data for enabling high-resolution localization while reducing monitoring cost. Secondly, three image processing steps, including edge detection and segmentation, are conducted on the constructed RSSI map to detect and locate multiple attackers without any prior knowledge about the attackers. To evaluate our proposal’s performance, a series of experiments are conducted based on the collected data. Experimental results have shown that in typical parameter settings, our algorithm’s positioning error does not exceed 0.41 m with a detection rate no less than 91%.

Keywords beam-stealing attacks detection localization image processing

Corresponding Author(s): Xianglin WEI,Renhui XU

Just Accepted Date: 08 April 2022 Issue Date: 27 October 2022

Cite this article:

Yaoqi YANG,Xianglin WEI,Renhui XU, et al. Jointly beam stealing attackers detection and localization without training: an image processing viewpoint[J]. Front. Comput. Sci., 2023, 17(3): 173704.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-022-1550-6
https://academic.hep.com.cn/fcs/EN/Y2023/V17/I3/173704

Tab.1 A comparison of this work and existing algorithms.

Fig.1 Directional and omnidirectional beam stealing attackers. (a) Directional attack scenario; (b) omnidirectional attack scenario

Notation	Description
$? 1$	The data set collected in the interested area
$? 2$	The data set in the interested area after interpolation
$M E$	RSSI map in an interested area with no attacker
$M D$	RSSI map in an interested area with only one directional antenna
$M O$	RSSI map in an interested area with only one omnidirectional antenna
$H E$	Normalized gray matrix corresponding to the RSSI map without an attacker
$H D$	Normalized gray matrix corresponding to RSSI map with only one directional antenna
$H O$	Normalized gray matrix corresponding to RSSI map with only one omnidirectional antenna
$H D i (g)$	Matrix after preprocessing of RSSI map with only one directional antenna
$H O i (g)$	Matrix after preprocessing of RSSI map with only one omnidirectional antenna
$N$	Total number of attackers
$T S$	Scale transformation function
$T R$	Direction transformation function
$T M$	Position transformation function
$H$	Matrix of each image after preprocessing
$σ i$	The distance between the pre-processed image and the i-th training image
$N D$	Number of detected directional attackers
$N O$	Number of detected omnidirectional attackers
$θ$	The vertical lobe width of the antenna (when $θ$ =0, it means that no attacker is detected)
$M$	RSSI map in the scene to be detected
$(x, y)$	The position coordinates of the attacker in the RSSI map (when $(x, y)$ = $?$ , it means that no attacker has been detected)
$d$	The distance between two attackers

Tab.2 Notations

Fig.2 The principle of the 2nd Bernstein Bezier interpolation

Fig.3 The working flow of attack detection and localization

Fig.4 Experimental scenario. (a) Structure of experiment scene; (b) experimental setting scenario

Parameters	Meaning
$α$	Detection rate
$λ D$	Identification rate for directional attacks
$λ O$	Identification rate of omnidirectional attacks
$μ (i)$	Positioning error in $i$ th experiment
$μ A$	Average positioning error

Tab.3 Performance metrics

Fig.5 RSSI map built on the collected raw data

Fig.6 RSSI map built using interpolated data set

Fig.7 Interpolated RSSI maps with different deployment densities of the monitors. (a) 12 monitors; (b) 33 monitors; (c) 66 monitors; (d) 132 monitors

Tab.4 Monitor deployment cost comparison.

Fig.8 RSSI maps using different interpolation methods. (a) without interpolation; (b) bilinear interpolation; (c) nearest-neighbor interpolation; (d) proposed interpolation

Algorithm	Bilinear	Nearest-neighbor	Proposed
$S S E$	$3.74 × 10 ? 27$	$1.17 × 10 ? 21$	$3.74 × 10 ? 35$
$R - s q u a r e$	$0.93$	$0.89$	$0.99$
$R M S E$	$5.32 × 10 ? 15$	$2.97 × 10 ? 12$	$5.32 × 10 ? 19$
$A d j u s t e d R - s q u a r e$	$0.86$	$0.79$	$0.98$

Tab.5 Comparison results

Fig.9 The correctness verification of Algorithm 2 with

d

= 0.6 m. (a) RSSI map; (b) remove signals; (c) smoothing process; (d) OSTU process; (e) edge detection; (f) segmentation

Fig.10 The correctness verification of Algorithm 2 with

d

= 0.3 m. (a) RSSI map; (b) remove signals; (c) smoothing process; (d) OSTU process; (e) edge detection; (f) segmentation

Fig.11 The correctness verification of Algorithm 2 with

d

= 0.15 m. (a) RSSI map; (b) remove signals; (c) smoothing process; (d) OSTU process; (e) edge detection; (f) segmentation

Strategy	$θ$ of antenna 1	$θ$ of antenna 2
S1	47°(D)	47°(D)
S2	47°(D)	120°(D)
S3	120°(D)	120°(D)
S4	47°(D)	47°(O)
S5	47°(D)	120°(O)
S6	120°(D)	47°(O)
S7	120°(D)	120°(O)
S8	47°(O)	47°(O)
S9	47°(O)	120°(O)
S10	120°(O)	120°(O)

Tab.6 Proposed attack strategies

Fig.12 Algorithm 2’s detection rate in different strategies. (a) Detection rate for S1 to S5; (b) detection rate for S6 to S10

Fig.13 ROC curves for different attack strategies. (a) ROC curve for S1 to S5; (b) ROC curve for S6 to S10

Fig.14 The identification performance of Algorithm 2. (a)

λ D

under S1 to S3; (b)

λ D

under S4 to S7; (c)

λ O

under S4 to S7; (d)

λ O

under S8 to S10

Fig.15 ROC curves of the identification performance. (a) ROC curve for

λ D

; (b) ROC curve for

λ O

Fig.16 The positioning error of Algorithm 2 in 10 scenarios. (a) Positioning error under S1 to S5; (b) positioning error under S6 to S10

Fig.17 The tracking results of two moving attackers in

S 8

. (a) Directional attacker tracking; (b) omnidirectional attacker tracking

Tab.7 Segmentation algorithm comparison results

Tab.8 Edge detection operator comparison results

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