Abstract
In an enterprise environment, intrusion detection systems generate many threat alerts on anomalous events every day, and these alerts may involve certain steps of a long-dormant advanced persistent threat (APT). In this paper, we present AttackMiner, an attack detection framework that combines contextual information from audit logs. Our main observation is that the same attack behavior may occur in various possible contexts, and combining various possible contextual information can provide more effective information for detecting such attacks. We utilize a combination of provenance graph causal analysis and deep learning techniques to build a graph-structure-based model that builds key patterns of attack graphs and benign graphs from audit logs. During detection, the detection system creates provenance graphs using the input audit logs. After being optimized by our customized graph optimization mechanism, it identifies whether an attack has occurred. Our evaluations on the DARPA TC dataset show that AttackMiner can successfully detect attack behaviors with high accuracy and efficiency. Through this effort, we provide security investigators with a new approach of identifying attack activity from audit logs.
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References
Adversarial tactics, techniques and common knowledge. https://attack.mitre.org/wiki/Main Page
Trace: Preventing advanced persistent threat cyberattacks(2018). https://archive.sri.com/work/projects/trace-preventing-advanced-persisten-threat-cyberattacks. (Accessed 1 April 2022)
Alsaheel, A., et al.: \(\{\)ATLAS\(\}\): A sequence-based learning approach for attack investigation. In: 30th USENIX Security Symposium (USENIX Security 21), pp. 3005–3022 (2021)
Bates, A., Tian, D.J., Butler, K.R., Moyer, T.: Trustworthy whole-system provenance for the linux kernel. In: 24th USENIX Security Symposium (USENIX Security 15), pp. 319–334 (2015)
Bilge, L., Balzarotti, D., Robertson, W., Kirda, E., Kruegel, C.: Disclosure: detecting botnet command and control servers through large-scale netflow analysis. In: Proceedings of the 28th Annual Computer Security Applications Conference, pp. 129–138 (2012)
Debnath, B., et al.: Loglens: A real-time log analysis system. In: 2018 IEEE 38th International Conference On Distributed Computing Systems (ICDCS), pp. 1052–1062. IEEE (2018)
Du, M., Li, F., Zheng, G., Srikumar, V.: Deeplog: Anomaly detection and diagnosis from system logs through deep learning. In: Proceedings of the 2017 ACM SIGSAC Conference On Computer And Communications Security, pp. 1285–1298 (2017)
Fix, E., Hodges, J.L.: Discriminatory analysis. nonparametric discrimination: Consistency properties. International Statistical Review/Revue Internationale de Statistique 57(3), 238–247 (1989)
Goel, A., Feng, W.C., Maier, D., Walpole, J.: Forensix: A robust, high-performance reconstruction system. In: 25th IEEE International Conference on Distributed Computing Systems Workshops, pp. 155–162. IEEE (2005)
Goel, A., Po, K., Farhadi, K., Li, Z., De Lara, E.: The taser intrusion recovery system. In: Proceedings of the Twentieth ACM Symposium On Operating Systems Principles, pp. 163–176 (2005)
Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems 30 (2017)
Han, X., Pasquier, T., Bates, A., Mickens, J., Seltzer, M.: Unicorn: Runtime provenance-based detector for advanced persistent threats. arXiv preprint arXiv:2001.01525 (2020)
Hassan, W.U., Bates, A., Marino, D.: Tactical provenance analysis for endpoint detection and response systems. In: 2020 IEEE Symposium on Security and Privacy (SP), pp. 1172–1189. IEEE (2020)
Hassan, W.U., et al.: Nodoze: Combatting threat alert fatigue with automated provenance triage. In: Network and Distributed Systems Security Symposium (2019)
Hassan, W.U., Noureddine, M.A., Datta, P., Bates, A.: Omegalog: High-fidelity attack investigation via transparent multi-layer log analysis. In: Network and Distributed System Security Symposium (2020)
Homayoun, S., Dehghantanha, A., Ahmadzadeh, M., Hashemi, S., Khayami, R.: Know abnormal, find evil: frequent pattern mining for ransomware threat hunting and intelligence. IEEE Trans. Emerg. Top. Comput. 8(2), 341–351 (2017)
Hutchins, E.M., Cloppert, M.J., Amin, R.M., et al.: Intelligence-driven computer network defense informed by analysis of adversary campaigns and intrusion kill chains. Lead. Issues Inf. Warfare Sec. Res. 1(1), 80 (2011)
Keromytis, A.D.: Transparent computing engagement 3 data release (2018). https://github.com/darpa-i2o/Transparent-Computing
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference On Machine Learning, pp. 1188–1196 (2014)
Li, Y., Gu, C., Dullien, T., Vinyals, O., Kohli, P.: Graph matching networks for learning the similarity of graph structured objects. In: International Conference on Machine Learning, pp. 3835–3845. PMLR (2019)
Liaw, A., Wiener, M., et al.: Classification and regression by randomforest. R news 2(3), 18–22 (2002)
Liu, F., Wen, Y., Zhang, D., Jiang, X., Xing, X., Meng, D.: Log2vec: A heterogeneous graph embedding based approach for detecting cyber threats within enterprise. In: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, pp. 1777–1794 (2019)
Liu, Y., et al.: Towards a timely causality analysis for enterprise security. In: NDSS (2018)
Manzoor, E., Milajerdi, S.M., Akoglu, L.: Fast memory-efficient anomaly detection in streaming heterogeneous graphs. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1035–1044 (2016)
Milajerdi, S.M., Eshete, B., Gjomemo, R., Venkatakrishnan, V.: Poirot: Aligning attack behavior with kernel audit records for cyber threat hunting. In: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, pp. 1795–1812 (2019)
Milajerdi, S.M., Gjomemo, R., Eshete, B., Sekar, R., Venkatakrishnan, V.: Holmes: real-time apt detection through correlation of suspicious information flows. In: 2019 IEEE Symposium on Security and Privacy (SP), pp. 1137–1152. IEEE (2019)
Oprea, A., Li, Z., Yen, T.F., Chin, S.H., Alrwais, S.: Detection of early-stage enterprise infection by mining large-scale log data. In: 2015 45th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, pp. 45–56. IEEE (2015)
Parveen, P., McDaniel, N., Hariharan, V.S., Thuraisingham, B., Khan, L.: Unsupervised ensemble based learning for insider threat detection. In: 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing, pp. 718–727. IEEE (2012)
Pasquier, T., et al.: Practical whole-system provenance capture. In: Proceedings of the 2017 Symposium on Cloud Computing, pp. 405–418 (2017)
Paszke, A., et al.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d’Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32, pp. 8024–8035. Curran Associates, Inc. (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf
Pohly, D.J., McLaughlin, S., McDaniel, P., Butler, K.: Hi-fi: collecting high-fidelity whole-system provenance. In: Proceedings of the 28th Annual Computer Security Applications Conference, pp. 259–268 (2012)
Rehurek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks. Citeseer (2010)
Shen, Y., Mariconti, E., Vervier, P.A., Stringhini, G.: Tiresias: Predicting security events through deep learning. In: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, pp. 592–605 (2018)
Song, W., Yin, H., Liu, C., Song, D.: Deepmem: Learning graph neural network models for fast and robust memory forensic analysis. In: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, pp. 606–618 (2018)
Suykens, J.A., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
Wang, Q., et al.: You are what you do: Hunting stealthy malware via data provenance analysis. In: NDSS (2020)
Wang, S., et al.: Heterogeneous graph matching networks for unknown malware detection. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 3762–3770. AAAI Press (2019)
Xu, X., Liu, C., Feng, Q., Yin, H., Song, L., Song, D.: Neural network-based graph embedding for cross-platform binary code similarity detection. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 363–376 (2017)
Zhu, T., et al.: General, efficient, and real-time data compaction strategy for apt forensic analysis. IEEE Trans. Inf. Forensics Secur. 16, 3312–3325 (2021)
Acknowledgment
This work is supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDC02040200.
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Pan, Y. et al. (2023). AttackMiner: A Graph Neural Network Based Approach for Attack Detection from Audit Logs. In: Li, F., Liang, K., Lin, Z., Katsikas, S.K. (eds) Security and Privacy in Communication Networks. SecureComm 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 462. Springer, Cham. https://doi.org/10.1007/978-3-031-25538-0_27
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