| Autor: |
Asaf Cidon, Neil Shah, Grant Ho, Marco Schweighauser, David Wagner, Mohamed Ibrahim |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
Deployable Machine Learning for Security Defense ISBN: 9783030596200 |
| DOI: |
10.1007/978-3-030-59621-7_1 |
| Popis: |
We present a large-scale characterization of attacker activity across 111 real-world enterprise organizations. We develop a novel forensic technique for distinguishing between attacker activity and benign activity in compromised enterprise accounts that yields few false positives and enables us to perform fine-grained analysis of attacker behavior. Applying our methods to a set of 159 compromised enterprise accounts, we quantify the duration of time attackers are active in accounts and examine thematic patterns in how attackers access and leverage these hijacked accounts. We find that attackers frequently dwell in accounts for multiple days to weeks, suggesting that delayed (non-real-time) detection can still provide significant value. Based on an analysis of the attackers’ timing patterns, we observe two distinct modalities in how attackers access compromised accounts, which could be explained by the existence of a specialized market for hijacked enterprise accounts: where one class of attackers focuses on compromising and selling account access to another class of attackers who exploit the access such hijacked accounts provide. Ultimately, our analysis sheds light on the state of enterprise account hijacking and highlights fruitful directions for a broader space of detection methods, ranging from new features that hone in on malicious account behavior to the development of non-real-time detection methods that leverage malicious activity after an attack’s initial point of compromise to more accurately identify attacks. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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