Empirical analysis of weapons of influence, life domains, and demographic-targeting in modern spam: an age-comparative perspective

被引:0
作者
Daniela Seabra Oliveira
Tian Lin
Harold Rocha
Donovan Ellis
Sandeep Dommaraju
Huizi Yang
Devon Weir
Sebastian Marin
Natalie C. Ebner
机构
[1] University of Florida,Department of Electrical and Computer Engineering
[2] University of Florida,Department of Psychology
[3] Department of Computer & Information Science & Engineering,undefined
来源
Crime Science | / 8卷
关键词
Spam; Influence; Life domains; Older adults; Young adults; Targeting;
D O I
暂无
中图分类号
学科分类号
摘要
Spam has been increasingly used for malware distribution. This paper analyzed modern spam from an age-comparative perspective to (i) discover the extent to which psychological weapons of influence and life domains were represented in today’s spam emails and (ii) clarify variations in the use of these weapons and life domains by user demographics. Thirty five young and 32 older participants forwarded 18,605 emails from their spam folder to our study email account. A random set of 961 emails were submitted to qualitative content coding and quantitative statistical analysis. Reciprocation was the most prevalent weapon; financial, leisure, and independence the most prevalent life domains. Older adults received health and independence-related spam emails more frequently, while young adults received leisure and occupation-related spam emails more often. These age differences show a level of targeting by user demographics in current spam campaigns. This targeting shows the need for age-tailored demographic warnings highlighting the presence of influence and pretexting (life domains) for suspicious emails for improved response to cyber-attacks that could result from spam distribution. The insights from this study and the produced labeled dataset of spam messages can inform the development of the next generation of such solutions, especially those based on machine learning.
引用
收藏
相关论文
共 35 条
[1]  
Caputo DD(2014)Going spear phishing: Exploring embedded training and awareness IEEE Security & Privacy 12 28-38
[2]  
Pfleeger SL(1999)Support vector machines for spam categorization IEEE Transactions on Neural Networks 10 1048-1054
[3]  
Freeman JD(1990)Age differences in decision making: A process methodology for examining strategic information processing Journal of Gerontology: Psychological Sciences 45 75-78
[4]  
Johnson ME(2010)Teaching johnny not to fall for phish ACM Transactions on Internet Technology (TOIT) 10 7-29
[5]  
Drucker H(2011)Age differences in risky choice: A meta-analysis Annals of the New York Academy of Sciences 1235 18-15
[6]  
Wu D(2014)Meta-analysis of the age-related positivity effect: Age differences in preferences for positive over negative information Psychology and Aging 1 1-753
[7]  
Vapnik VN(2006)Development and structural dynamics of personal life investment in old age Psychology and Aging 21 737-75
[8]  
Johnson M(2011)Understanding scam victims: Seven principles for systems security Communications of ACM 54 70-339
[9]  
Kumaraguru P(2003)Aging and vocabulary score: A meta-analysis Psychology and Aging 18 332-249
[10]  
Sheng S(1997)Meta-analyses of age-cognition relations in adulthood: Estimates of linear and nonlinear age effects and structural models Psychological Bulletin 122 231-331