DocumentCode :
160604
Title :
Analyzing social and stylometric features to identify spear phishing emails
Author :
Dewan, Prasun ; Kashyap, Arti ; Kumaraguru, Ponnurangam
Author_Institution :
Indraprastha Inst. of Inf. Technol., New Delhi, India
fYear :
2014
fDate :
23-25 Sept. 2014
Firstpage :
1
Lastpage :
13
Abstract :
Targeted social engineering attacks in the form of spear phishing emails, are often the main gimmick used by attackers to infiltrate organizational networks and implant state-of-the-art Advanced Persistent Threats (APTs). Spear phishing is a complex targeted attack in which, an attacker harvests information about the victim prior to the attack. This information is then used to create sophisticated, genuine-looking attack vectors, drawing the victim to compromise confidential information. What makes spear phishing different, and more powerful than normal phishing, is this contextual information about the victim. Online social media services can be one such source for gathering vital information about an individual. In this paper, we characterize and examine a true positive dataset of spear phishing, spam, and normal phishing emails from Symantec´s enterprise email scanning service. We then present a model to detect spear phishing emails sent to employees of 14 international organizations, by using social features extracted from LinkedIn. Our dataset consists of 4,742 targeted attack emails sent to 2,434 victims, and 9,353 non targeted attack emails sent to 5,912 non victims; and publicly available information from their LinkedIn profiles. We applied various machine learning algorithms to this labeled data, and achieved an overall maximum accuracy of 97.76% in identifying spear phishing emails. We used a combination of social features from LinkedIn profiles, and stylometric features extracted from email subjects, bodies, and attachments. However, we achieved a slightly better accuracy of 98.28% without the social features. Our analysis revealed that social features extracted from LinkedIn do not help in identifying spear phishing emails. To the best of our knowledge, this is one of the first attempts to make use of a combination of stylometric features extracted from emails, and social features extracted from an online social network to detect targeted spear phishin- emails.
Keywords :
computer crime; learning (artificial intelligence); organisational aspects; social networking (online); unsolicited e-mail; APT; LinkedIn profiles; Symantec enterprise e-mail scanning service; advanced persistent threats; attack emails; complex targeted attack; confidential information; contextual information; e-mail attachments; e-mail bodies; e-mail subjects; information gathering; international organization employees; labeled data; machine learning algorithms; normal phishing emails; online social media services; organizational network infiltration; overall maximum accuracy; publicly available information; social engineering attacks; social feature analysis; social feature extraction; spams; spear phishing e-mail identification; stylometric feature analysis; stylometric feature extraction; Accuracy; Feature extraction; LinkedIn; Media; Organizations; Unsolicited electronic mail;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electronic Crime Research (eCrime), 2014 APWG Symposium on
Conference_Location :
Birmingham, AL
Print_ISBN :
978-1-4799-6509-0
Type :
conf
DOI :
10.1109/ECRIME.2014.6963160
Filename :
6963160
Link To Document :
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