Predicting User Susceptibility to Phishing Based on Multidimensional Features

Table 1

Multidimensional attribute features.

Attribute	Features	Category	Frequency	Percentage
Demographics	Age	<20	67	6.06
		20–30	720	65.16
		30–40	107	9.68
		40–50	128	11.58
		>50	83	7.51
	Education level	Below high school	61	5.52
		Vocational high school/high school	109	9.86
		Undergraduates	610	55.2
		Graduate student or above	325	29.14
	Gender	Male	555	50.23
		Female	550	49.77
	Annual income	< ¥30,000	477	43.17
		¥30,000–¥100,000	369	33.39
		¥100,000–¥200,000	178	16.11
		> ¥ 200,000	81	7.33
Personality	Personality	Conscientiousness	124	11.22
		Extraversion	18	0.016
		Agreeableness	528	47.78
		Openness	443	40.09
		Neuroticism	42	0.038
Knowledge experience	Computer knowledge	High	250	22.62
		Middle	649	58.73
		Low	206	18.64
	Network security knowledge	High	179	16.19
		Middle	583	52.76
		Low	343	31.04
	Social engineering knowledge	High	129	11.67
		Middle	568	51.14
		Low	408	36.92
Susceptibility	Phished	Yes	609	55.12
		No	496	44.88