Title
To Get Lost Is To Learn The Way: An Analysis Of Multi-Step Social Engineering Attacks On The Web
Abstract
Web-based social engineering (SE) attacks manipulate users to perform specific actions, such as downloading malware and exposing personal information. Aiming to effectively lure users, some SE attacks, which we call multi-step SE attacks, constitute a sequence of web pages starting from a landing page and require browser interactions at each web page. Also, different browser interactions executed on a web page often branch to multiple sequences to redirect users to different SE attacks. Although common systems analyze only landing pages or conduct browser interactions limited to a specific attack, little effort has been made to follow such sequences of web pages to collect multi-step SE attacks. We propose STRAYSREEP, a system to automatically crawl a sequence of web pages and detect diverse multi-step SE attacks. We evaluate the effectiveness of STRAYSHEEP'S three modules (landing-page-collection, web-crawling, and SE-detection) in terms of the rate of collected landing pages leading to SE attacks, efficiency of web crawling to reach more SE attacks, and accuracy in detecting the attacks. Our experimental results indicate that STRAYSREEP can lead to 20% more SE attacks than Alexa top sites and search results of trend words, crawl five times more efficiently than a simple crawling module, and detect SE attacks with 95.5% accuracy. We demonstrate that STRAYSHEEP can collect various SE attacks, not limited to a specific attack. We also clarify attackers' techniques for tricking users and browser interactions, redirecting users to attacks.
Year
DOI
Venue
2021
10.1587/transfun.2020CIP0005
IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES
Keywords
DocType
Volume
social engineering attacks, browser automation, web crawler
Journal
E104A
Issue
ISSN
Citations 
1
0916-8508
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Takashi Koide112.39
Daiki Chiba23812.66
Mitsuaki Akiyama315633.87
Katsunari Yoshioka414722.92
Tsutomu Matsumoto51156197.58