Reference: | [1] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, u. Kaiser, and I. Polosukhin, “Attention is All You Need,” in Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017. [2] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805, 2018. [3] B. Binde, R. McRee, and T. J. O’Connor, “Assessing outbound traffic to uncover advanced persistent threat,” SANS Institute. Whitepaper, vol. 16, 2011. [4] S. Morgan, “2021 Report: Cyberwarfare in the C-Suite,” Cybersecurity Ventures, Tech. Rep., January 2021. [5] “MITRE ATT&CK,” 2021. [Online]. Available: https://attack.mitre.org/. [6] R. McMillan, “Definition: Threat Intelligence,” 2013. [Online]. Available: https://www.gartner.com/en/documents/2487216 [7] G. Husari, E. Al-Shaer, M. Ahmed, B. Chu, and X. Niu, “Ttpdrill: Automatic and accurate extraction of threat actions from unstructured text of cti sources,” in Proceedings of the 33rd Annual Computer Security Applications Conference, 2017, pp. 103–115. [8] G. Ayoade, S. Chandra, L. Khan, K. Hamlen, and B. Thuraisingham, “Automated threat report classification over multi-source data,” in 2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC). IEEE, 2018, pp. 236–245. [9] T. T. Thein, Y. Ezawa, S. Nakagawa, K. Furumoto, Y. Shiraishi, M. Mohri, Y. Takano, and M. Morii, “Paragraph-based Estimation of Cyber Kill Chain Phase from Threat Intelligence Reports,” Journal of Information Processing, vol. 28, pp. 1025–1029, 2020. [10] V. Legoy, M. Caselli, C. Seifert, and A. Peter, “Automated Retrieval of ATT&CK Tactics and Techniques for Cyber Threat Reports,” arXiv preprint arXiv:2004.14322, 2020. [11] “Wireshark · Go Deep.” 2021. [Online]. Available: https://www.wireshark.org/. [12] “Snort - Network Intrusion Detection & Prevention System,” 2021. [Online]. Available: https://snort.org/. [13] E. M. Hutchins, M. J. Cloppert, R. M. Amin et al., “Intelligence-driven computer network defense informed by analysis of adversary campaigns and intrusion kill chains,” Leading Issues in Information Warfare & Security Research, vol. 1, no. 1, p. 80, 2011. [14] B. E. Strom, A. Applebaum, D. P. Miller, K. C. Nickels, A. G. Pennington, and C. B. Thomas, “MITRE ATT&CK™: Design and Philosophy,” The MITRE Corporation, Tech. Rep., 2018. [15] I. Mokube and M. Adams, “Honeypots: concepts, approaches, and challenges,” in Proceedings of the 45th annual southeast regional conference, 2007, pp. 321–326. [16] “The Honeynet Project,” 1999. [Online]. Available: https://www.honeynet.org/ [17] Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov, “Roberta: A robustly optimized bert pretraining approach,” arXiv preprint arXiv:1907.11692, 2019. [18] Z. Lan, M. Chen, S. Goodman, K. Gimpel, P. Sharma, and R. Soricut, “Albert: A lite bert for self-supervised learning of language representations,” arXiv preprint arXiv:1909.11942, 2019. [19] V. Sanh, L. Debut, J. Chaumond, and T. Wolf, “Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter,” arXiv preprint arXiv:1910.01108, 2019. [20] X. Jiao, Y. Yin, L. Shang, X. Jiang, X. Chen, L. Li, F. Wang, and Q. Liu, “Tinybert: Distilling bert for natural language understanding,” arXiv preprint arXiv:1909.10351, 2019. [21] J. Lee, W. Yoon, S. Kim, D. Kim, S. Kim, C. H. So, and J. Kang, “Biobert: a pre-trained biomedical language representation model for biomedical text mining,” Bioinformatics, vol. 36, no. 4, pp. 1234–1240, 2020. [22] K. Huang, J. Altosaar, and R. Ranganath, “Clinicalbert: Modeling clinical notes and predicting hospital readmission,” arXiv preprint arXiv:1904.05342, 2019. [23] I. Beltagy, K. Lo, and A. Cohan, “Scibert: A pretrained language model for scientific text,” arXiv preprint arXiv:1903.10676, 2019. [24] “Common Attack Pattern Enumeration and Classification.” [Online]. Available: https://capec.mitre.org/index.html. [25] S. Barnum, “Standardizing cyber threat intelligence information with the structured threat information expression (stix),” Mitre Corporation, vol. 11, pp. 1–22, 2012. [26] S. Caltagirone, A. Pendergast, and C. Betz, “The diamond model of intrusion analysis,” Center For Cyber Intelligence Analysis and Threat Research Hanover Md, Tech. Rep., 2013. [27] R.-H. Hwang, M.-C. Peng, V.-L. Nguyen, and Y.-L. Chang, “An LSTM-based deep learning approach for classifying malicious traffic at the packet level,” Applied Sciences, vol. 9, no. 16, p. 3414, 2019. [28] Y. Yu, H. Yan, H. Guan, and H. Zhou, “DeepHTTP: semantics-structure model with attention for anomalous HTTP traffic detection and pattern mining,” arXiv preprint arXiv:1810.12751, 2018. [29] L. Han, Y. Sheng, and X. Zeng, “A packet-length-adjustable attention model based on bytes embedding using flow-WGAN for smart cybersecurity,” IEEE Access, vol. 7, pp. 82 913–82 926, 2019. [30] T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” arXiv preprint arXiv:1310.4546, 2013. [31] E. L. Goodman, C. Zimmerman, and C. Hudson, “Packet2Vec: Utilizing Word2Vec for Feature Extraction in Packet Data,” arXiv preprint arXiv:2004.14477, 2020. [32] F. Dehghani, N. Movahhedinia, M. R. Khayyambashi, and S. Kianian, “Real-time traffic classification based on statistical and payload content features,” in 2010 2nd international workshop on intelligent systems and applications. IEEE, 2010, pp. 1–4. [33] G. Betarte, Á. Pardo, and R. Martínez, “Web application attacks detection using machine learning techniques,” in 2018 17th ieee International Conference on Machine Learning and Applications (icmla). IEEE, 2018, pp. 1065–1072. [34] H. Liu, B. Lang, M. Liu, and H. Yan, “CNN and RNN based payload classification methods for attack detection,” Knowledge-Based Systems, vol. 163, pp. 332–341, 2019. [35] e. a. Falcon, WA, “Pytorch lightning,” GitHub. Note: https://github.com/PyTorchLightning/pytorch-lightning, vol. 3, 2019. [36] T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz, J. Davison, S. Shleifer, P. von Platen, C. Ma, Y. Jernite, J. Plu, C. Xu, T. L. Scao, S. Gugger, M. Drame, Q. Lhoest, and A. M. Rush, “Transformers: State-of-the-art natural language processing,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Online: Association for Computational Linguistics, Oct. 2020, pp. 38–45. [Online]. Available: https://www.aclweb.org/anthology/2020.emnlp-demos.6 [37] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011. [38] P. Qi, Y. Zhang, Y. Zhang, J. Bolton, and C. D. Manning, “Stanza: A Python Natural Language Processing Toolkit for Many Human Languages,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 2020. |