Detecting And Characterizing Lateral Phishing At Scale
PROCEEDINGS OF THE 28TH USENIX SECURITY SYMPOSIUM, pp.1273-1290, (2019)
We present the first large-scale characterization of lateral phishing attacks, based on a dataset of 113 million employee-sent emails from 92 enterprise organizations. In a lateral phishing attack, adversaries leverage a compromised enterprise account to send phishing emails to other users, benefitting from both the implicit trust and the...More
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- The security community has explored a myriad of defenses against phishing attacks.
- In a lateral phishing attack, an adversary uses a compromised enterprise account to send phishing emails to a new set of recipients.
- Listing 1 shows an anonymized example of a lateral phishing attack from the study
- In this attack, the phisher tried to lure the recipient into clicking on a link under the false pretense of a new contract.
- The attacker tried to make the deception more credible by responding to recipients who inquired about the email’s authenticity; and they actively hid their presence in the compromised user’s mailbox by deleting all traces of their phishing email
- For over a decade, the security community has explored a myriad of defenses against phishing attacks
- Our work focuses on lateral phishing attacks that employ a malicious URL embedded in the email, which is the most common exploit method identified in our dataset
- Using this value as a final threshold for this second candidate set of Organization-wide attackers, we identify 29 Organization-wide attackers where over 95% of their recipients belong to the account takeover (ATO)’s organization but less than 11% of the recipients came from the ATO’s recent contacts; a combination that suggests the attacker seeks primarily to compromise other employees, but who do not necessarily have a personal connection with the hijacked account
- In this work we presented the first large-scale characterization of lateral phishing attacks across more than 100 million employee-sent emails from 92 enterprise organizations
- We developed and evaluated a new detector that found many known lateral phishing attacks, as well as dozens of unreported attacks, while generating a low volume of false positives
- Our work showed that 14% of our randomly sampled organizations, ranging from small to large, experienced lateral phishing attacks within a seven-month time period, and that attackers succeeded in compromising new accounts at least 11% of the time
- Establishing Generalizability: As described earlier in Section 3.2, the authors split the dataset into two disjoint segments: a training dataset consisting of emails from the 52 exploratory organizations during April–June 2018 and a test dataset from 92 enterprises during July–October 2018; in § 5.2, the authors show that the detector’s performance remains the same if the test dataset contains only the emails from the 40 withheld test organizations
- Given these two datasets, the authors first trained the classifier and tuned its hyperparameters via cross validation on the training dataset (Appendix A.2).
- To ensure that any tuning or knowledge the authors derived from the training dataset did not bias or overfit the classifier, the authors did not alter any of the model’s hyperparameters or features during the evaluation on the test dataset
- For the same reasons the authors saw in the training dataset, this detector exhibited a high false negative rate, missing 57 user-reported incidents.
- Despite this strategy’s high false negative rate, the authors find that it generates virtually no false positives across a test dataset of tens-of-millions of emails.
- As the authors explored earlier in Section B.3, this result reflects the fact that the text of phishing emails exhibits frequent churn over time, causing the two text-similarity driven strategies to miss new attacks that the main approach detects
- In this work the authors presented the first large-scale characterization of lateral phishing attacks across more than 100 million employee-sent emails from 92 enterprise organizations.
- The authors uncovered and quantified several thematic recipient targeting strategies and deceptive content narratives; while some attackers engage in targeted attacks, most follow strategies that employ non-personalized phishing attacks that can be readily used across different organizations
- Despite this apparent lack of sophistication in tailoring and targeting their attacks, 31% of the dataset’s lateral phishers engaged in some form of sophisticated behavior designed to increase their success rate or mask their presence from the hijacked account’s true owner.
- The authors' work provides the first large-scale insights into an emerging, widespread form of enterprise phishing attacks, and illuminates techniques and future ideas for defending against this potent threat
- Table1: Evaluation results of our detector. ‘Detected Known Attacks’ shows the number of incidents that our detector identified, and were also reported by an employee at an organization. ‘Detected New Attacks’ shows the number of incidents that our detector identified, but were not reported by anyone. ‘Missed Attacks (FN)’ shows all incidents either reported by a user or found by any of our detection strategies, but our detector marked it as benign (false negative). Of the 22 incidents our detector misses, 12 are attachment-based attacks, a threat model which our detector explicitly does not target but which we include in our FN and Detection Rate results for completeness
- Table2: Summary of the scale and success of the lateral phishing attacks in our dataset (§ 6.1)
- Table3: Summary of recipient targeting strategies per ATO (§ 6.2)
- Table4: Distribution of the number of incidents per message tailoring category (§ 6.3). The columns correspond to how unique and specific the message’s topic pertains to the victim or organization. The rows correspond to whether the phishing email explicitly names the recipient or organization
- Table5: Top 10 most common words across all 180 lateral phishing incidents
- Detection: An extensive body of prior literature proposes numerous techniques for detecting traditional phishing attacks [1,3,13,14,43], as well as more sophisticated spearphishing attacks [8, 10, 22, 40, 46]. Hu et al studied how to use social graph metrics to detect malicious emails sent from compromised accounts . Their approach detects hijacked accounts with false positive rates between 20–40%. Unfortunately, in practice, many organizations handle tens of thousands of employee-sent emails per day, so a false positive rate of 20% would lead to thousands of false alerts each day. IdentityMailer, proposed by Stringhini et al , detects lateral phishing attacks by training behavior models based on timing patterns, metadata, and stylometry for each user. If a new email deviates from an employee’s behavioral model, their system flags it as an attack. While promising, their approach produces false positive rates in the range of 1–10%, which is untenable in practice given the high volume of benign emails and low base rate of phishing. Additionally, their system requires training a behavioral model for each employee, incurring expensive technical debt to operate at scale.
- This work was supported in part by the Hewlett Foundation through the Center for Long-Term Cybersecurity, NSF grants CNS1237265 and CNS-1705050, an NSF GRFP Fellowship, the Irwin Mark and Joan Klein Jacobs Chair in Information and Computer Science (UCSD), by generous gifts from Google and Facebook, a Facebook Fellowship, and operational support from the UCSD Center for Networked Systems
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- 1. Number of trees: 50–500, in steps of 50 (i.e., 50, 100, 150,..., 450, 500)
- 2. Maximum tree depth: 10–100, in steps of 10
- 3. Minimum leaf size: 1, 2, 4, 8
- 4. Downsampling ratio of (benign / attack) emails: 10, 50, 100, 200