True Attacks, Attack Attempts, or Benign Triggers? An Empirical Measurement of Network Alerts in a Security Operations Center
Limin Yang, Zhi Chen, Chenkai Wang, Zhenning Zhang, Sushruth Booma, Phuong Cao, Constantin Adam, Alexander Withers, Zbigniew Kalbarczyk, Ravishankar K. Iyer, Gang Wang
摘要
Security Operations Centers (SOCs) face the key challenge of handling excessive security alerts. While existing works have studied this problem qualitatively via user studies, there is still a lack of quantitative understanding of the impact of excessive alerts and their effectiveness and limitations in capturing true attacks. In this paper, we fill the gap by working with a real-world SOC and collecting and analyzing their network alert logs over 4 years (115 million alerts, from 2018 to 2022). To further understand how alerts are associated with true attacks, we also obtain the ground truth of 227 successful attacks in the past 20 years (11 during the overlapping period). Through analysis, we observe that SOC analysts are facing excessive alerts (24K-134K per day), but only a small percentage of the alerts (0.01%) are associated with true attacks. While the majority of true attacks can be detected within the same day, the post-attack investigation takes much longer time (53 days on average). Furthermore, we observe a significant portion of the alerts are related to "attack attempts" (attacks that did not lead to true compromises, 27%), and "benign triggers" (correctly matched security events but had business-justified explanations, 49%). Empirically, we show there are opportunities to use rare/abnormal alert patterns to help isolate signals related to true attacks. Given that enterprise SOCs rarely disclose internal data, this paper helps contextualize SOCs' pain points and refine existing problem definitions.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了最后一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper1
问问它们各自怎么用它它引用的顶会 Paper17
- Kitsune: An Ensemble of Autoencoders for Online Network Intrusion DetectionYisroel Mirsky, Tomer Doitshman, Yuval Elovici, Asaf ShabtaiNDSS 2018 · 被引用 945 次
- Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code ContributionsHammond Pearce, Baleegh Ahmad, Benjamin Tan, Brendan Dolan-Gavitt 等S&P 2022 · 被引用 725 次
- NoDoze: Combatting Threat Alert Fatigue with Automated Provenance TriageWajih Ul Hassan, Shengjian Guo, Ding Li, Zhengzhang Chen 等NDSS 2019 · 被引用 411 次
- Tactical Provenance Analysis for Endpoint Detection and Response SystemsWajih Ul Hassan, Adam Bates, Daniel MarinoS&P 2020 · 被引用 317 次
- POIROT: Aligning Attack Behavior with Kernel Audit Records for Cyber Threat HuntingSadegh M. Milajerdi, Birhanu Eshete, Rigel Gjomemo, V. N. VenkatakrishnanCCS 2019 · 被引用 313 次
相关 Paper
- 99% False Positives: A Qualitative Study of SOC Analysts' Perspectives on Security AlarmsBushra A. AlAhmadi, Louise Axon, Ivan MartinovicUSENIX Security 2022
- Detecting Credential Spearphishing in Enterprise SettingsGrant Ho, Aashish Sharma, Mobin Javed, Vern Paxson 等USENIX Security 2017 · 被引用 94 次
- Alert Alchemy: SOC Workflows and Decisions in the Management of NIDS RulesMathew Vermeer, Natalia Kadenko, Michel van Eeten, Carlos Gañán 等CCS 2023 · 被引用 16 次
- Indicator of Benignity: An Industry View of False Positive in Malicious Domain Detection and its MitigationDaiping Liu, Danyu Sun, Zhenhua Chen, Shu Wang 等NDSS 2026
- Matched and Mismatched SOCs: A Qualitative Study on Security Operations Center IssuesFaris Bugra Kokulu, Ananta Soneji, Tiffany Bao, Yan Shoshitaishvili 等CCS 2019 · 被引用 134 次
