Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1007/978-3-319-20550-2_5guideproceedingsArticle/Chapter ViewAbstractPublication PagesConference Proceedingsacm-pubtype
Article

That Ain't You: Blocking Spearphishing Through Behavioral Modelling

Published: 09 July 2015 Publication History

Abstract

One of the ways in which attackers steal sensitive information from corporations is by sending spearphishing emails. A typical spearphishing email appears to be sent by one of the victim's coworkers or business partners, but has instead been crafted by the attacker. A particularly insidious type of spearphishing emails are the ones that do not only claim to be written by a certain person, but are also sent by that person's email account, which has been compromised. Spearphishing emails are very dangerous for companies, because they can be the starting point to a more sophisticated attack or cause intellectual property theft, and lead to high financial losses. Currently, there are no effective systems to protect users against such threats. Existing systems leverage adaptations of anti-spam techniques. However, these techniques are often inadequate to detect spearphishing attacks. The reason is that spearphishing has very different characteristics from spam and even traditional phishing. To fight the spearphishing threat, we propose a change of focus in the techniques that we use for detecting malicious emails: instead of looking for features that are indicative of attack emails, we look for emails that claim to have been written by a certain person within a company, but were actually authored by an attacker. We do this by modelling the email-sending behavior of users over time, and comparing any subsequent email sent by their accounts against this model. Our approach can block advanced email attacks that traditional protection systems are unable to detect, and is an important step towards detecting advanced spearphishing attacks.

References

[1]
Hacking attack at RSA targeted Flash flaw. http://www.ft.com/cms/s/2/96518afc-5cb1-11e0-ab7c-00144feab49a.html
[2]
Shamoon was an external attack on Saudi oil production. http://www.infosecurity-magazine.com/view/29750/shamoon-was-an-external-attack-on-saudi-oil-production/
[3]
SpamAssassin: performance. http://wiki.apache.org/spamassassin/UsingNetworkTests
[4]
Abbasi, A., Chen, H., Nunamaker, J.F.: Stylometric identification in electronic markets: scalability and robustness. J. Manage. Inform. Syst. 25, 49---78 2008
[5]
Afroz, S., Brennan, M., Greenstadt, R.: Detecting hoaxes, frauds, and deception in writing style online. In: IEEE Symposium on Security and Privacy 2012
[6]
Aloul, F., Zahidi, S., El-Hajj, W.: Two factor authentication using mobile phones. In: IEEE/ACS International Conference on Computer Systems and Applications 2009
[7]
Calix, K., Connors, M., Levy, D., Manzar, H., MCabe, G., Westcott, S.: Stylometry for e-mail author identification and authentication. In: Proceedings of CSIS Research Day, Pace University 2008
[8]
Corney, M.W.: Analysing E-mail Text Authorship for Forensic Purposes
[9]
Drucker, H., Wu, D., Vapnik, V.N.: Support vector machines for spam categorization. IEEE Trans. Neural Networks 10, 1048---1054 1999
[10]
Egele, M., Stringhini, G., Kruegel, C., Vigna, G.: COMPA: detecting compromised social network accounts. In: Symposium on Network and Distributed System Security NDSS 2013
[11]
Fette, I., Sadeh, N., Tomasic, A.: Learning to Detect Phishing Emails
[12]
Forsyth, R., Holmes, D.: Feature finding for text classification. Literary Linguist. Comput. 11, 163---174 1996
[13]
Frantzeskou, G., Stamatatos, E., Gritzalis, S., Chaski, C.E., Howald, B.S.: Identifying authorship by byte-level n-grams: the source code author profile scap method. Int. J. Digit. Evid. 2007
[14]
Hao, S., Syed, N.A., Feamster, N., Gray, A.G., Krasser, S.: Detecting spammers with SNARE: spatio-temporal network-level automatic reputation engine. In: USENIX Security Symposium 2009
[15]
Iqbal, F., Hadjidj, R., Fung, B., Debbabi, M.: A novel approach of mining write-prints for authorship attribution in e-mail forensics. Digit. Invest. 5, S42---S51 2008
[16]
Jagatic, T.N., Johnson, N.A., Jakobsson, M., Menczer, F.: Social phishing. Commun. ACM 50, 94---100 2007
[17]
John, J.P., Moshchuk, A., Gribble, S.D., Krishnamurthy, A.: Studying spamming botnets using botlab. In: USENIX Symposium on Networked Systems Design and Implementation NSDI 2009
[18]
Kakavelakis, G., Beverly, R., Young, J.: Auto-learning of SMTP TCP transport-layer features for spam and abusive message detection. In: USENIX Large Installation System Administration Conference 2011
[19]
Klimt, B., Yang, Y.: Introducing the enron corpus. In: CEAS 2004
[20]
Leiba, B.: DomainKeys Identified Mail DKIM: Using digital signatures for domain verification. In: CEAS 2007
[21]
Lin, E., Aycock, J., Mannan, M.: Lightweight client-side methods for detecting email forgery. In: Lee, D.H., Yung, M. eds. WISA 2012. LNCS, vol. 7690, pp. 254---269. Springer, Heidelberg 2012
[22]
Meyer, T., Whateley, B.: SpamBayes: effective open-source, Bayesian based, email classification system. In: CEAS 2004
[23]
Narayanan, A., Paskov, H., Gong, N.Z., Bethencourt, J., Stefanov, E., Shin, E.C.R., Song, D.: On the feasibility of internet-scale author identification. In: IEEE Symposium on Security and Privacy 2012
[24]
Pitsillidis, A., Levchenko, K., Kreibich, C., Kanich, C., Voelker, G.M., Paxson, V., Weaver, N., Savage, S.: Botnet Judo: fighting spam with itself. In: Symposium on Network and Distributed System Security NDSS 2010
[25]
Platt, J., et al.: Sequential minimal optimization: a fast algorithm for training support vector machines
[26]
Ramachandran, A., Feamster, N., Vempala, S.: Filtering spam with behavioral blacklisting. In: ACM Conference on Computer and Communications Security CCS 2007
[27]
Sahami, M., Dumais, S., Heckermann, D., Horvitz, E.: A Bayesian approach to filtering junk e-mail. In: Learning for Text Categorization 1998
[28]
Sculley, D., Wachman, G.M.: Relaxed online SVMs for spam filtering. In: ACM SIGIR Conference on Research and Development in Information Retrieval 2007
[29]
Stolfo, S.J., Hershkop, S., Hu, C.-W., Li, W.-J., Nimeskern, O., Wang, K.: Behavior-based modeling and its application to email analysis. ACM Trans. Internet Technol. TOIT 6, 187---221 2006
[30]
Stolfo, S.J., Hershkop, S., Wang, K., Nimeskern, O., Hu, C.-W.: Behavior profiling of email. In: Chen, H., Miranda, R., Zeng, D.D., Demchak, C.C., Schroeder, J., Madhusudan, T. eds. ISI 2003. LNCS, vol. 2665, pp. 74---90. Springer, Heidelberg 2003
[31]
Stringhini, G., Egele, M., Zarras, A., Holz, T., Kruegel, C., Vigna, G.: B@BEL: leveraging email delivery for spam mitigation. In: USENIX Security Symposium 2012
[32]
Stringhini, G., Holz, T., Stone-Gross, B., Kruegel, C., Vigna, G.: BotMagnifier: locating spambots on the internet. In: USENIX Security Symposium 2011
[33]
Stringhini, G., Thonnard, O.: That ain't you: detecting spearphishing emails before they are sent. arXiv preprint arXiv:1410.6629 2014
[34]
Symantec Corp. Symantec intelligence report 2013. http://www.symanteccloud.com/mlireport/SYMCINT_2013_01_January.pdf
[35]
Taylor, B.: Sender reputation in a large webmail service. In: CEAS 2006
[36]
The Radicati Group. Email Statistics Report. http://www.radicati.com/wp/wp-content/uploads/2011/05/Email-Statistics-Report-2011-2015-Executive-Summary.pdf
[37]
Thonnard, O., Bilge, L., O'Gorman, G., Kiernan, S., Lee, M.: Industrial espionage and targeted attacks: understanding the characteristics of an escalating threat. In: Balzarotti, D., Stolfo, S.J., Cova, M. eds. RAID 2012. LNCS, vol. 7462, pp. 64---85. Springer, Heidelberg 2012
[38]
Threatpost. New Email Worm Turns Back the Clock on Virus Attacks 2010. http://threatpost.com/en_us/blogs/new-email-worm-turns-back-clock-virus-attacks-090910
[39]
Trend Micro Inc., Spear-Phishing Email: Most Favored APT Attack Bait 2012
[40]
Tweedie, F., Baayern, R.: How variable may a constant be? Measures of lexical richness in perspective. Comput. Humanit. 32, 323---352 1998
[41]
Venkataraman, S., Sen, S., Spatscheck, O., Haffner, P., Song, D.: Exploiting network structure for proactive spam mitigation. In: USENIX Security Symposium 2007
[42]
Wong, M., Schlitt, W.: RFC 4408: Sender Policy Framework SPF for Authorizing Use of Domains in E-Mail, Version 1 2006. http://tools.ietf.org/html/rfc4408
[43]
Xie, Y., Yu, F., Achan, K., Panigrahy, R., Hulten, G., Osipkov, I.: Spamming botnets: signatures and characteristics. SIGCOMM Comput. Commun. Rev. 38, 171---182 2008
[44]
Yule, G.: The Statistical Study of Literary Vocabulary. Cambridge University Press, Cambridge 1944
[45]
Zalewski, M.: p0f v3 2012. http://lcamtuf.coredump.cx/p0f3/
[46]
Zhang, Y., Hong, J.I., Cranor, L.F.: Cantina: A Content-based Approach to Detecting Phishing Web Sites
[47]
Zheng, R., Li, J., Chen, H., Huang, Z.: A framework for authorship identification of online messages: writing-style features and classification techniques. J. Am. Soc. Inform. Sci. Technol. 57, 378---393 2005

Cited By

View all
  • (2024)Constructs of Deceit: Exploring Nuances in Modern Social Engineering AttacksDetection of Intrusions and Malware, and Vulnerability Assessment10.1007/978-3-031-64171-8_6(107-127)Online publication date: 17-Jul-2024
  • (2023)“It may take ages”: Understanding Human-Centred Lateral Phishing Attack Detection in OrganisationsProceedings of the 2023 European Symposium on Usable Security10.1145/3617072.3617116(344-355)Online publication date: 16-Oct-2023
  • (2023)Overview of Social Engineering Protection and Prevention MethodsComputer Security. ESORICS 2023 International Workshops10.1007/978-3-031-54204-6_4(64-83)Online publication date: 25-Sep-2023
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image Guide Proceedings
DIMVA 2015: Proceedings of the 12th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment - Volume 9148
July 2015
336 pages
ISBN:9783319205496

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 09 July 2015

Qualifiers

  • Article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 09 Nov 2024

Other Metrics

Citations

Cited By

View all
  • (2024)Constructs of Deceit: Exploring Nuances in Modern Social Engineering AttacksDetection of Intrusions and Malware, and Vulnerability Assessment10.1007/978-3-031-64171-8_6(107-127)Online publication date: 17-Jul-2024
  • (2023)“It may take ages”: Understanding Human-Centred Lateral Phishing Attack Detection in OrganisationsProceedings of the 2023 European Symposium on Usable Security10.1145/3617072.3617116(344-355)Online publication date: 16-Oct-2023
  • (2023)Overview of Social Engineering Protection and Prevention MethodsComputer Security. ESORICS 2023 International Workshops10.1007/978-3-031-54204-6_4(64-83)Online publication date: 25-Sep-2023
  • (2022)Automated Detection of Doxing on TwitterProceedings of the ACM on Human-Computer Interaction10.1145/35551676:CSCW2(1-24)Online publication date: 11-Nov-2022
  • (2022)RAIDER: Reinforcement-Aided Spear Phishing DetectorNetwork and System Security10.1007/978-3-031-23020-2_2(23-50)Online publication date: 9-Dec-2022
  • (2022)Spear Phishing Email Detection with Multiple Reputation Features and Sample EnhancementScience of Cyber Security10.1007/978-3-031-17551-0_34(522-538)Online publication date: 10-Aug-2022
  • (2022)LogoMotive: Detecting Logos on Websites to Identify Online Scams - A TLD Case StudyPassive and Active Measurement10.1007/978-3-030-98785-5_1(3-29)Online publication date: 28-Mar-2022
  • (2021)Detecting Telephone-based Social Engineering Attacks using Scam SignaturesProceedings of the 2021 ACM Workshop on Security and Privacy Analytics10.1145/3445970.3451152(67-73)Online publication date: 28-Apr-2021
  • (2021)Multi Layer Detection Framework for Spear-Phishing AttacksInformation Systems Security10.1007/978-3-030-92571-0_3(38-56)Online publication date: 16-Dec-2021
  • (2021)Proactive Detection of Phishing Kit TrafficApplied Cryptography and Network Security10.1007/978-3-030-78375-4_11(257-286)Online publication date: 21-Jun-2021
  • Show More Cited By

View Options

View options

Get Access

Login options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media