{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T05:47:02Z","timestamp":1769924822472,"version":"3.49.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319600796","type":"print"},{"value":"9783319600802","type":"electronic"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-3-319-60080-2_19","type":"book-chapter","created":{"date-parts":[[2017,6,1]],"date-time":"2017-06-01T14:40:06Z","timestamp":1496328006000},"page":"250-268","source":"Crossref","is-referenced-by-count":15,"title":["Learning Representations for Log Data in Cybersecurity"],"prefix":"10.1007","author":[{"given":"Ignacio","family":"Arnaldo","sequence":"first","affiliation":[]},{"given":"Alfredo","family":"Cuesta-Infante","sequence":"additional","affiliation":[]},{"given":"Ankit","family":"Arun","sequence":"additional","affiliation":[]},{"given":"Mei","family":"Lam","sequence":"additional","affiliation":[]},{"given":"Costas","family":"Bassias","sequence":"additional","affiliation":[]},{"given":"Kalyan","family":"Veeramachaneni","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,6,2]]},"reference":[{"key":"19_CR1","unstructured":"Adversarial tactics, techniques and common knowledge. https:\/\/attack.mitre.org"},{"key":"19_CR2","unstructured":"KDD Cup 99. http:\/\/kdd.ics.uci.edu\/databases\/kddcup99\/kddcup99.html"},{"key":"19_CR3","unstructured":"Malware capture facility project. http:\/\/mcfp.weebly.com\/"},{"key":"19_CR4","unstructured":"VirusTotal. https:\/\/www.virustotal.com"},{"key":"19_CR5","doi-asserted-by":"crossref","unstructured":"Beigi, E.B., Jazi, H.H., Stakhanova, N., Ghorbani, A.A.: Towards effective feature selection in machine learning-based botnet detection approaches. In: 2014 IEEE Conference on Communications and Network Security, pp. 247\u2013255 (2014)","DOI":"10.1109\/CNS.2014.6997492"},{"issue":"1","key":"19_CR6","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"19_CR7","unstructured":"Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR time series classification archive (2015)"},{"key":"19_CR8","doi-asserted-by":"crossref","unstructured":"Draper-Gil, G., Lashkari, A.H., Mamun, M.S.I., Ghorbani, A.A.: Characterization of encrypted and VPN traffic using time-related features. In: Proceedings of the 2nd International Conference on Information Systems Security and Privacy, ICISSP, vol. 1, pp. 407\u2013414 (2016)","DOI":"10.5220\/0005740704070414"},{"key":"19_CR9","doi-asserted-by":"crossref","unstructured":"Garc\u00eda, S., Uhl\u00ed\u0159, V., Rehak, M.: Identifying and modeling botnet C&C behaviors. In: Proceedings of the 1st International Workshop on Agents and CyberSecurity, ACySE 2014, NY, USA, pp. 1:1\u20131:8. ACM, New York (2014)","DOI":"10.1145\/2602945.2602949"},{"issue":"5","key":"19_CR10","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1002\/sec.800","volume":"7","author":"S Garcia","year":"2014","unstructured":"Garcia, S., Zunino, A., Campo, M.: Survey on network-based botnet detection methods. Secur. Commun. Netw. 7(5), 878\u2013903 (2014)","journal-title":"Secur. Commun. Netw."},{"issue":"8","key":"19_CR11","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"19_CR12","unstructured":"Jiang, H., Nagra, J., Ahammad, P.: Sok: applying machine learning in security-a survey. arXiv preprint arXiv:1611.03186 (2016)"},{"key":"19_CR13","unstructured":"Kim, S., Smyth, P., Luther, S.: Modeling waveform shapes with random effects segmental hidden Markov models. In: Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, UAI 2004, pp. 309\u2013316. AUAI Press, Arlington (2004)"},{"key":"19_CR14","unstructured":"Nanopoulos, A., Alcock, R., Manolopoulos, Y.: Information processing and technology. In: Feature-based Classification of Time-series Data, pp. 49\u201361. Nova Science Publishers Inc, Commack (2001)"},{"key":"19_CR15","unstructured":"Plohmann, D., Yakdan, K., Klatt, M., Bader, J., Gerhards-Padilla, E.: A comprehensive measurement study of domain generating malware. In: 25th USENIX Security Symposium (USENIX Security 2016), pp. 263\u2013278. USENIX Association, Austin (2016)"},{"key":"19_CR16","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez, J.J., Alonso, C.J.: Interval and dynamic time warping-based decision trees. In: Proceedings of the 2004 ACM Symposium on Applied Computing, SAC 2004, NY, USA, pp. 548\u2013552. ACM, New York (2004)","DOI":"10.1145\/967900.968015"},{"key":"19_CR17","doi-asserted-by":"crossref","unstructured":"Sak, H., Senior, A.W., Beaufays, F.: Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition. CoRR abs\/1402.1128 (2014)","DOI":"10.21437\/Interspeech.2014-80"},{"issue":"3","key":"19_CR18","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.cose.2011.12.012","volume":"31","author":"A Shiravi","year":"2012","unstructured":"Shiravi, A., Shiravi, H., Tavallaee, M., Ghorbani, A.A.: Toward developing a systematic approach to generate benchmark datasets for intrusion detection. Comput. Secur. 31(3), 357\u2013374 (2012)","journal-title":"Comput. Secur."},{"key":"19_CR19","doi-asserted-by":"crossref","unstructured":"Sood, A., Enbody, R.: Targeted Cyber Attacks: Multi-staged Attacks Driven by Exploits and Malware, 1st edn. Syngress Publishing, Burlington (2014)","DOI":"10.1016\/B978-0-12-800604-7.00001-2"},{"key":"19_CR20","doi-asserted-by":"crossref","unstructured":"Staudemeyer, R.C., Omlin, C.W.: Evaluating performance of long short-term memory recurrent neural networks on intrusion detection data. In: Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference, SAICSIT 2013, NY, USA, pp. 218\u2013224. ACM, New York (2013)","DOI":"10.1145\/2513456.2513490"},{"issue":"3","key":"19_CR21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.13052\/jcsm2245-1439.421","volume":"4","author":"M Stevanovic","year":"2016","unstructured":"Stevanovic, M., Pedersen, J.M.: On the use of machine learning for identifying botnet network traffic. J. Cyber. Secur. Mobility 4(3), 1\u201332 (2016)","journal-title":"J. Cyber. Secur. Mobility"},{"key":"19_CR22","unstructured":"Tuor, A., Kaplan, S., Hutchinson, B., Nichols, N., Robinson, S.: Deep learning for unsupervised insider threat detection in structured cybersecurity data streams (2017)"},{"key":"19_CR23","doi-asserted-by":"crossref","unstructured":"Veeramachaneni, K., Arnaldo, I., Korrapati, V., Bassias, C., Li, K.: AI $$^2$$ : training a big data machine to defend. In: 2016 IEEE 2nd International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing (HPSC), and IEEE International Conference on Intelligent Data and Security (IDS), pp. 49\u201354 (2016)","DOI":"10.1109\/BigDataSecurity-HPSC-IDS.2016.79"},{"key":"19_CR24","unstructured":"Wang, Z., Oates, T.: Imaging time-series to improve classification and imputation. In: Proceedings of the 24th International Conference on Artificial Intelligence, IJCAI 2015, pp. 3939\u20133945. AAAI Press (2015)"},{"key":"19_CR25","unstructured":"Woodbridge, J., Anderson, H.S., Ahuja, A., Grant, D.: Predicting domain generation algorithms with long short-term memory networks. arXiv preprint arXiv:1611.00791 (2016)"},{"key":"19_CR26","doi-asserted-by":"crossref","unstructured":"Xi, X., Keogh, E., Shelton, C., Wei, L., Ratanamahatana, C.A.: Fast time series classification using numerosity reduction. In: Proceedings of the 23rd International Conference on Machine Learning, ICML 2006, NY, USA, pp. 1033\u20131040. ACM, New York (2006)","DOI":"10.1145\/1143844.1143974"},{"key":"19_CR27","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.cose.2013.04.007","volume":"39","author":"D Zhao","year":"2013","unstructured":"Zhao, D., Traore, I., Sayed, B., Lu, W., Saad, S., Ghorbani, A., Garant, D.: Botnet detection based on traffic behavior analysis and flow intervals. Comput. Secur. 39, 2\u201316 (2013)","journal-title":"Comput. Secur."}],"container-title":["Lecture Notes in Computer Science","Cyber Security Cryptography and Machine Learning"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-60080-2_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:29:11Z","timestamp":1750285751000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-60080-2_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319600796","9783319600802"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-60080-2_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017]]}}}