An Unsupervised Detection Method for Multiple Abnormal Wi-Fi Access Points in Large-Scale Wireless Network

Figures & data

Figure 1. The framework of our model.

Figure 2. The detection method of DBSCAN.

Table

Algorithm 1: Anomalous APs detection by using DBSCAN algorithm

Input: $D=\left\{x_1,x_2,\dots ,x_m\right\}$ Progress: 1: $\mathrm{\Omega }=\mathrm{\varnothing };k=0;\mathrm{\Gamma }=D;C=\mathrm{\varnothing }$ 2: for $i=0,1,2\dots m$ do 3: if $\left|N_{\epsilon }\left(x_i\right)\right|>MinPts$ then 4: $\mathrm{\Omega }=\mathrm{\Omega }\cup \left\{x_i\right\}$ 5: end if 6: end for 7: while $\mathrm{\Omega }\ne \mathrm{\varnothing }$ do 8: $\mathrm{\forall }o\in \mathrm{\Omega },\hspace{0.17em}{\mathrm{\Omega }}_{cur}=\left\{o\right\}$ 9: $k=k+1;C_k=\left\{o\right\};\mathrm{\Gamma }=\mathrm{\Gamma }-\left\{o\right\}$ 10: while ${\mathrm{\Omega }}_{cur}\ne \mathrm{\varnothing }$ do 11: if $\left|N_{\epsilon }\left(o{ }^{\mathrm{\prime }}\right)\right|>MinPts$ then 12: $\mathrm{\Delta }=N_{\epsilon }\left(o{ }^{\mathrm{\prime }}\right)\cap \mathrm{\Gamma };C_k=C_k\cup \mathrm{\Delta };\mathrm{\Gamma }=\mathrm{\Gamma }-\mathrm{\Delta }$ 13: ${\mathrm{\Omega }}_{cur}={\mathrm{\Omega }}_{cur}\cup \left(\mathrm{\Delta }\cap \mathrm{\Omega }\right)-o^{\prime }$ 14: end if 15: end while 16: $\mathrm{\Omega }=\mathrm{\Omega }-C_k$ 17: end while Output: $C=\left\{C_1,C_2,\dots ,C_k\right\}$

Figure 3. The deployment structure of APMS.

Table 1. The parameters of the algorithm

Parameters	Value
collecting days	89
collecting points every day	288
eps	15
min_samples	0.5 * collecting days
random times	50

Figure 4. The feature plane calculated from the sample data.

Figure 5. The distance from sample to feature plane in 3-D coordinate system.

Figure 6. The anomalous detection method based on the distance.

Figure 7. Algorithm stability based on date and time.

Figure 8. The comparison of standard deviation between 2-D model and 3-D model.

Table 2. The comparison of accuracy between 2-D model and 3-D model

Model	Total samples	Metric 1			Metric 2
		True	False	ACC	True	False	ACC
2-D Model	25632	18115	7517	71%	17863	7769	70%
3-D Model	25632	21770	3862	85%	24920	712	97%