  Social engineering (SE) aims at deceiving users into performing actions that
may compromise their security and privacy. These threats exploit weaknesses in
human's decision making processes by using tactics such as pretext, baiting,
impersonation, etc. On the web, SE attacks include attack classes such as
scareware, tech support scams, survey scams, sweepstakes, etc., which can
result in sensitive data leaks, malware infections, and monetary loss. For
instance, US consumers lose billions of dollars annually due to various SE
attacks. Unfortunately, generic social engineering attacks remain understudied,
compared to other important threats, such as software vulnerabilities and
exploitation, network intrusions, malicious software, and phishing. The few
existing technical studies that focus on social engineering are limited in
scope and mostly focus on measurements rather than developing a generic
defense. To fill this gap, we present SEShield, a framework for in-browser
detection of social engineering attacks. SEShield consists of three main
components: (i) a custom security crawler, called SECrawler, that is dedicated
to scouting the web to collect examples of in-the-wild SE attacks; (ii) SENet,
a deep learning-based image classifier trained on data collected by SECrawler
that aims to detect the often glaring visual traits of SE attack pages; and
(iii) SEGuard, a proof-of-concept extension that embeds SENet into the web
browser and enables real-time SE attack detection. We perform an extensive
evaluation of our system and show that SENet is able to detect new instances of
SE attacks with a detection rate of up to 99.6% at 1% false positive, thus
providing an effective first defense against SE attacks on the web.
