{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T19:59:22Z","timestamp":1766087962793,"version":"3.37.3"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T00:00:00Z","timestamp":1663891200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T00:00:00Z","timestamp":1663891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100007069","name":"Universit\u00e0 della Calabria","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100007069","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Ambient Intell Human Comput"],"published-print":{"date-parts":[[2023,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The task of identifying malicious activities in logs and predicting threats is crucial nowadays in industrial sector. In this paper, we focus on the identification of past malicious activities and in the prediction of future threats by proposing a novel technique based on the combination of Marked Temporal Point Processes (<jats:italic>MTTP<\/jats:italic>) and Neural Networks. Differently from the traditional formulation of Temporal Point Processes, our method does not make any prior assumptions on the functional form of the conditional intensity function and on the distribution of the events. Our approach is based the adoption of Neural Networks with the goal of improving the capabilities of learning arbitrary and unknown event distributions by taking advantage of the Deep Learning theory. We conduct a series of experiments using industrial data coming from gas pipelines, showing that our framework is able to represent in a convenient way the information gathered from the logs and predict future menaces in an unsupervised way, as well as classifying the past ones. The results of the experimental evaluation, showing outstanding values for precision and recall, confirm the effectiveness of our approach.<\/jats:p>","DOI":"10.1007\/s12652-022-04416-5","type":"journal-article","created":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T19:03:45Z","timestamp":1663959825000},"page":"4771-4783","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Identification and prediction of attacks to industrial control systems using temporal point processes"],"prefix":"10.1007","volume":"14","author":[{"given":"Giancarlo","family":"Fortino","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Claudia","family":"Greco","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antonella","family":"Guzzo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0562-7462","authenticated-orcid":false,"given":"Michele","family":"Ianni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,23]]},"reference":[{"issue":"15","key":"4416_CR1","doi-asserted-by":"publisher","first-page":"7050","DOI":"10.3390\/app11157050","volume":"11","author":"Z Ahmad","year":"2021","unstructured":"Ahmad Z, Khan AS, Nisar K, Haider I, Hassan R, Haque MR, Tarmizi S, Rodrigues JJPC (2021) Anomaly detection using deep neural network for iot architecture. Appl Sci 11(15):7050","journal-title":"Appl Sci"},{"key":"4416_CR2","doi-asserted-by":"crossref","unstructured":"Aleskerov E, Freisleben B, Rao B (1997) Cardwatch: a neural network based database mining system for credit card fraud detection. In: Proc. of the IEEE\/IAFE 1997 CIFEr. IEEE, pp 220\u2013226","DOI":"10.1109\/CIFER.1997.618940"},{"issue":"18","key":"4416_CR3","doi-asserted-by":"publisher","first-page":"8383","DOI":"10.3390\/app11188383","volume":"11","author":"MA Alsoufi","year":"2021","unstructured":"Alsoufi MA, Razak S, Siraj MM, Nafea I, Ghaleb FA, Saeed F, Nasser M (2021) Anomaly-based intrusion detection systems in iot using deep learning: a systematic literature review. Appl Sci 11(18):8383","journal-title":"Appl Sci"},{"key":"4416_CR4","unstructured":"Apa L, Penagos CM (2013) Compromising industrial facilities from 40 miles away. IOActive Technical White Paper"},{"issue":"6","key":"4416_CR5","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1016\/j.ress.2006.03.008","volume":"92","author":"T Aven","year":"2007","unstructured":"Aven T (2007) A unified framework for risk and vulnerability analysis covering both safety and security. Reliab Eng Syst Saf 92(6):745\u2013754","journal-title":"Reliab Eng Syst Saf"},{"key":"4416_CR6","volume-title":"Security log management: identifying patterns in the chaos","author":"J Babbin","year":"2006","unstructured":"Babbin J (2006) Security log management: identifying patterns in the chaos. Elsevier, Amsterdam"},{"key":"4416_CR7","doi-asserted-by":"crossref","unstructured":"Barbar\u00e1 D, Li Y, Couto J, Lin J-L, Jajodia S (2003) Bootstrapping a data mining intrusion detection system. In: Proceedings of the 2003 ACM symposium on applied computing, pp 421\u2013425","DOI":"10.1145\/952532.952616"},{"key":"4416_CR8","unstructured":"Bieber P, Blanquart J-P, Descargues G, Dulucq M, Fourastier Y, Hazane E, Julien M, L\u00e9onardon L, Sarouille G (2012) Security and safety assurance for aerospace embedded systems. In: Embedded real time software and systems (ERTS2012)"},{"key":"4416_CR9","doi-asserted-by":"publisher","first-page":"245","DOI":"10.2307\/253535","volume":"65","author":"PL Brockett","year":"1998","unstructured":"Brockett PL, Xia X, Derrig RA (1998) Using Kohonen\u2019s self-organizing feature map to uncover automobile bodily injury claims fraud. J Risk Insurance 65:245\u2013274","journal-title":"J Risk Insurance"},{"key":"4416_CR10","unstructured":"Checkoway S, McCoy D, Kantor B, Anderson D, Shacham H, Savage S, Koscher K, Czeskis A, Roesner F, Kohno T et\u00a0al. (2011) Comprehensive experimental analyses of automotive attack surfaces. In: USENIX security symposium, vol 4, San Francisco, p 2021"},{"key":"4416_CR11","unstructured":"Chen K, Lu SC, Teng HS (1990) Adaptive real-time anomaly detection using inductively generated sequential patterns. In: Fifth intrusion detection workshop. SRI International, Menlo Park, CA"},{"key":"4416_CR12","doi-asserted-by":"crossref","unstructured":"Cockram TJ, Lautieri SR (2007) Combining security and safety principles in practice. In: 2007 2nd Institution of engineering and technology international conference on system safety. IET, pp 159\u2013164","DOI":"10.1049\/cp:20070458"},{"key":"4416_CR13","volume-title":"An introduction to the theory of point processes","author":"DJ Daley","year":"2003","unstructured":"Daley DJ, Vere-Jones D (2003) An introduction to the theory of point processes. Springer-Verlag, Berlin (ISBN 978-0-387-21564-8)"},{"key":"4416_CR14","doi-asserted-by":"crossref","unstructured":"Dilek S, \u00c7ak\u0131r H, Ayd\u0131n M (2015) Applications of artificial intelligence techniques to combating cyber crimes: a review. arXiv preprint arXiv:1502.03552","DOI":"10.5121\/ijaia.2015.6102"},{"issue":"10","key":"4416_CR15","doi-asserted-by":"publisher","first-page":"4580","DOI":"10.3390\/app11104580","volume":"11","author":"A Djenna","year":"2021","unstructured":"Djenna A, Harous S, Saidouni DE (2021) Internet of things meet internet of threats: New concern cyber security issues of critical cyber infrastructure. Appl Sci 11(10):4580","journal-title":"Appl Sci"},{"key":"4416_CR16","doi-asserted-by":"crossref","unstructured":"Du N, Dai H, Trivedi RS, Upadhyay U, Gomez-Rodriguez M, Song L (2016) Recurrent marked temporal point processes: Embedding event history to vector. In: Proceedings of the 22nd ACM SIGKDD","DOI":"10.1145\/2939672.2939875"},{"issue":"143","key":"4416_CR17","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1080\/14786448708628471","volume":"23","author":"FY Edgeworth","year":"1887","unstructured":"Edgeworth FY (1887) Xli on discordant observations. Lond Edinb Dublin Philos Mag J Sci 23(143):364\u2013375","journal-title":"Lond Edinb Dublin Philos Mag J Sci"},{"key":"4416_CR18","doi-asserted-by":"crossref","unstructured":"Emamian V, Kaveh M, Tewfik AH (2000) Robust clustering of acoustic emission signals using the Kohonen network. In: 2000 IEEE ICASSP, vol 6. IEEE, pp 3891\u20133894","DOI":"10.1109\/ICASSP.2000.860253"},{"key":"4416_CR19","unstructured":"Ertoz L, Steinbach M, Kumar V (2002) A new shared nearest neighbor clustering algorithm and its applications. In: Workshop on clustering high dimensional data and its applications at 2nd SIAM international conference on data mining, pp 105\u2013115"},{"key":"4416_CR20","first-page":"226","volume":"96","author":"M Ester","year":"1996","unstructured":"Ester M, Kriegel H-P, Sander J, Xiaowei X et al (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd 96:226\u2013231","journal-title":"In Kdd"},{"key":"4416_CR22","doi-asserted-by":"crossref","unstructured":"Fortino G, Guzzo A, Ianni M, Leotta F, Mecella M (2020) Exploiting marked temporal point processes for predicting activities of daily living. In: 2020 IEEE international conference on human-machine systems (ICHMS). IEEE, pp 1\u20136","DOI":"10.1109\/ICHMS49158.2020.9209398"},{"key":"4416_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2021.107567","volume":"96","author":"G Fortino","year":"2021","unstructured":"Fortino G, Guzzo A, Ianni M, Leotta F, Mecella M (2021) Predicting activities of daily living via temporal point processes: approaches and experimental results. Comput Electr Eng 96:107567","journal-title":"Comput Electr Eng"},{"key":"4416_CR23","doi-asserted-by":"publisher","first-page":"2483","DOI":"10.1109\/JIOT.2017.2767291","volume":"5","author":"M Frustaci","year":"2018","unstructured":"Frustaci M, Pace P, Aloi G, Fortino G (2018) Evaluating critical security issues of the iot world: present and future challenges. IEEE Internet Things J 5:2483\u20132495","journal-title":"IEEE Internet Things J"},{"key":"4416_CR24","doi-asserted-by":"crossref","unstructured":"Greensmith J (2015) Securing the internet of things with responsive artificial immune systems. In: Proceedings of the 2015 annual conference on genetic and evolutionary computation, pp 113\u2013120","DOI":"10.1145\/2739480.2754816"},{"key":"4416_CR25","unstructured":"Gr\u00f8tan TO, Jaatun MG, Knut \u00d8ien, Onshus T (2007) The sesa method for assessing secure remote access to safety instrumented systems. SINTEF Report A, 1626"},{"issue":"5","key":"4416_CR26","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1016\/S0306-4379(00)00022-3","volume":"25","author":"S Guha","year":"2000","unstructured":"Guha S, Rastogi R, Shim K (2000) Rock: a robust clustering algorithm for categorical attributes. Inf Syst 25(5):345\u2013366","journal-title":"Inf Syst"},{"key":"4416_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106658","volume":"96","author":"A Gumaei","year":"2020","unstructured":"Gumaei A, Hassan MM, Shamsul Huda M, Rafiul Hassan M, Camacho D, Del Ser J, Fortino G (2020) A robust cyberattack detection approach using optimal features of SCADA power systems in smart grids. Appl Soft Comput 96:106658","journal-title":"Appl Soft Comput"},{"key":"4416_CR28","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.future.2020.06.054","volume":"113","author":"A Guzzo","year":"2020","unstructured":"Guzzo A, Ianni M, Pugliese A, Sacc\u00e0 D (2020) Modeling and efficiently detecting security-critical sequences of actions. Futur Gener Comput Syst 113:196\u2013206","journal-title":"Futur Gener Comput Syst"},{"issue":"1","key":"4416_CR29","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1093\/biomet\/58.1.83","volume":"58","author":"AG Hawkes","year":"1971","unstructured":"Hawkes AG (1971) Spectra of some self-exciting and mutually exciting point processes. Biometrika 58(1):83\u201390","journal-title":"Biometrika"},{"issue":"3","key":"4416_CR30","doi-asserted-by":"publisher","first-page":"493","DOI":"10.2307\/3212693","volume":"11","author":"AG Hawkes","year":"1974","unstructured":"Hawkes AG, Oakes D (1974) A cluster process representation of a self-exciting process. J Appl Probab 11(3):493\u2013503","journal-title":"J Appl Probab"},{"key":"4416_CR32","doi-asserted-by":"crossref","unstructured":"Ianni M, Masciari E (2022) Some experiments on high performance anomaly detection. In: 2022 30th Euromicro international conference on parallel, distributed and network-based processing (PDP), pp 226\u2013229. 10.1109\/PDP55904.2022.00042","DOI":"10.1109\/PDP55904.2022.00042"},{"key":"4416_CR31","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.future.2019.07.077","volume":"102","author":"M Ianni","year":"2020","unstructured":"Ianni M, Masciari E, Mazzeo GM, Mezzanzanica M, Zaniolo C (2020) Fast and effective big data exploration by clustering. Futur Gener Comp Syst 102:84\u201394","journal-title":"Futur Gener Comp Syst"},{"issue":"3","key":"4416_CR33","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/0304-4149(79)90008-5","volume":"8","author":"V Isham","year":"1979","unstructured":"Isham V, Westcott M (1979) A self-correcting point process. Stochastic Process Appl 8(3):335\u2013347","journal-title":"Stochastic Process Appl"},{"key":"4416_CR34","volume-title":"Algorithms for clustering data","author":"AK Jain","year":"1988","unstructured":"Jain AK, Dubes RC (1988) Algorithms for clustering data. Prentice-Hall Inc, New Jersey"},{"issue":"5","key":"4416_CR35","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1016\/j.jcss.2014.02.005","volume":"80","author":"J Jang-Jaccard","year":"2014","unstructured":"Jang-Jaccard J, Nepal S (2014) A survey of emerging threats in cybersecurity. J Comput Syst Sci 80(5):973\u2013993","journal-title":"J Comput Syst Sci"},{"issue":"6\u20137","key":"4416_CR36","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1016\/S0167-8655(00)00131-8","volume":"22","author":"M-F Jiang","year":"2001","unstructured":"Jiang M-F, Tseng S-S, Chih-Ming S (2001) Two-phase clustering process for outliers detection. Pattern Recogn Lett 22(6\u20137):691\u2013700","journal-title":"Pattern Recogn Lett"},{"key":"4416_CR37","unstructured":"Johnsen SO (2012) Resilience at interfaces: improvement of safety and security in distributed control systems by web of influence. In: Information management & computer security"},{"key":"4416_CR38","doi-asserted-by":"crossref","unstructured":"Johnson CW (2012) Cybersafety: on the interactions between cybersecurity and the software engineering of safety-critical systems. In: Achieving system safety, pp 85\u201396","DOI":"10.1007\/978-1-4471-2494-8_8"},{"key":"4416_CR39","first-page":"1","volume":"92","author":"K Kent","year":"2006","unstructured":"Kent K, Souppaya M (2006) Guide to computer security log management. NIST Spec Publ 92:1\u201372","journal-title":"NIST Spec Publ"},{"key":"4416_CR40","doi-asserted-by":"crossref","unstructured":"Kingman JFC (1993) Poisson processes, vole\u00a03 of Oxford Studies in Probability. Clarendon Press. Oxford Science Publications","DOI":"10.1093\/oso\/9780198536932.001.0001"},{"issue":"9","key":"4416_CR41","doi-asserted-by":"publisher","first-page":"1464","DOI":"10.1109\/5.58325","volume":"78","author":"T Kohonen","year":"1990","unstructured":"Kohonen T (1990) The self-organizing map. Proc IEEE 78(9):1464\u20131480","journal-title":"Proc IEEE"},{"key":"4416_CR42","doi-asserted-by":"crossref","unstructured":"Kornecki AJ, Zalewski J (2010) Safety and security in industrial control. In: Proceedings of the sixth annual workshop on cyber security and information intelligence research, pp 1\u20134","DOI":"10.1145\/1852666.1852754"},{"key":"4416_CR43","doi-asserted-by":"crossref","unstructured":"Koscher K, Czeskis A, Roesner F, Patel S, Kohno T, Checkoway S, McCoy D, Kantor B, Anderson D, Shacham H et\u00a0al (2010) Experimental security analysis of a modern automobile. In: 2010 IEEE symposium on security and privacy. IEEE, pp 447\u2013462","DOI":"10.1109\/SP.2010.34"},{"key":"4416_CR44","doi-asserted-by":"crossref","unstructured":"Kumar V (2005) Parallel and distributed computing for cybersecurity. IEEE Distrib Syst Online 6(10)","DOI":"10.1109\/MDSO.2005.53"},{"key":"4416_CR45","unstructured":"Lee A, Brewer T (2009)Smart grid cyber security strategy and requirements. Draft Interagency Report NISTIR, 7628"},{"key":"4416_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2020.102761","volume":"168","author":"M Mahbub","year":"2020","unstructured":"Mahbub M (2020) Progressive researches on iot security: an exhaustive analysis from the perspective of protocols, vulnerabilities, and preemptive architectonics. J Netw Comput Appl 168:102761","journal-title":"J Netw Comput Appl"},{"key":"4416_CR47","doi-asserted-by":"crossref","unstructured":"Mahoney MV, Chan PK (2003) Learning rules for anomaly detection of hostile network traffic. In: Third IEEE international conference on data mining. IEEE, pp 601\u2013604","DOI":"10.1109\/ICDM.2003.1250987"},{"issue":"3","key":"4416_CR48","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1109\/TSE.2010.60","volume":"37","author":"PK Manadhata","year":"2010","unstructured":"Manadhata PK, Wing JM (2010) An attack surface metric. IEEE Trans Softw Eng 37(3):371\u2013386","journal-title":"IEEE Trans Softw Eng"},{"issue":"12","key":"4416_CR49","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1016\/j.sigpro.2003.07.018","volume":"83","author":"M Markou","year":"2003","unstructured":"Markou M, Singh S (2003a) Novelty detection: a review-part 1: statistical approaches. Signal Process 83(12):2481\u20132497","journal-title":"Signal Process"},{"issue":"12","key":"4416_CR50","doi-asserted-by":"publisher","first-page":"2499","DOI":"10.1016\/j.sigpro.2003.07.019","volume":"83","author":"M Markou","year":"2003","unstructured":"Markou M, Singh S (2003b) Novelty detection: a review-part 2: neural network based approaches. Signal Process 83(12):2499\u20132521","journal-title":"Signal Process"},{"issue":"4","key":"4416_CR51","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1145\/382912.382923","volume":"3","author":"J McHugh","year":"2000","unstructured":"McHugh J (2000) Testing intrusion detection systems: a critique of the 1998 and 1999 darpa intrusion detection system evaluations as performed by Lincoln laboratory. ACM Trans Inf Syst Security (TISSEC) 3(4):262\u2013294","journal-title":"ACM Trans Inf Syst Security (TISSEC)"},{"key":"4416_CR52","unstructured":"Meixell B, Forner E (2013) Out of control: demonstrating scada exploitation. Black Hat, p 2013"},{"key":"4416_CR53","unstructured":"Morgan S (2020) Cybercrime to cost the world \\$10.5 trillion annually by 2025, Nov 2020. https:\/\/cybersecurityventures.com\/hackerpocalypse-cybercrime-report-2016\/"},{"key":"4416_CR56","doi-asserted-by":"crossref","unstructured":"Morris T, Gao W (2014) Industrial control system traffic data sets for intrusion detection research. In: International conference on critical infrastructure protection. Springer, pp 65\u201378","DOI":"10.1007\/978-3-662-45355-1_5"},{"issue":"2","key":"4416_CR55","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.ijcip.2011.06.005","volume":"4","author":"T Morris","year":"2011","unstructured":"Morris T, Srivastava A, Reaves B, Gao W, Pavurapu K, Reddi R (2011) A control system testbed to validate critical infrastructure protection concepts. Int J Crit Infrastruct Prot 4(2):88\u2013103","journal-title":"Int J Crit Infrastruct Prot"},{"key":"4416_CR54","unstructured":"Morris TH, Thornton Z, Turnipseed I (2015) Industrial control system simulation and data logging for intrusion detection system research. In: 7th annual southeastern cyber security summit, pp 3\u20134"},{"key":"4416_CR57","doi-asserted-by":"crossref","unstructured":"Otey M, Parthasarathy S, Ghoting A, Li G, Narravula S, Panda D (2003) Towards nic-based intrusion detection. In: Proc. of ACM SIGKDD, pp 723\u2013728","DOI":"10.1145\/956750.956847"},{"key":"4416_CR58","unstructured":"Pires AM, Santos-Pereira C (2005) Using clustering and robust estimators to detect outliers in multivariate data. In: Proceedings of the international conference on robust statistics"},{"key":"4416_CR59","doi-asserted-by":"crossref","unstructured":"Ramadas M, Ostermann S, Tjaden B (2003) Detecting anomalous network traffic with self-organizing maps. In: Int. workshop on recent advances in intrusion detection. Springer, pp 36\u201354","DOI":"10.1007\/978-3-540-45248-5_3"},{"issue":"6","key":"4416_CR60","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1016\/S0167-4048(02)01009-X","volume":"21","author":"EE Schultz","year":"2002","unstructured":"Schultz EE (2002) A framework for understanding and predicting insider attacks. Comput Secur 21(6):526\u2013531","journal-title":"Comput Secur"},{"key":"4416_CR61","doi-asserted-by":"crossref","unstructured":"Smith KA, Woo F, Ciesielski V, Ibrahim R (2002) Matching data mining algorithm suitability to data characteristics using a self-organizing map. In: Hybrid information systems. Springer, pp 169\u2013179","DOI":"10.1007\/978-3-7908-1782-9_13"},{"key":"4416_CR62","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2020.102630","volume":"161","author":"SM Tahsien","year":"2020","unstructured":"Tahsien SM, Karimipour H, Spachos P (2020) Machine learning based solutions for security of internet of things (iot): a survey. J Netw Comput Appl 161:102630","journal-title":"J Netw Comput Appl"},{"key":"4416_CR63","unstructured":"Xiao S, Yan J, Farajtabar M, Song L, Yang X, Zha H (2017a) Joint modeling of event sequence and time series with attentional twin recurrent neural networks. ArXiv, abs\/1703.08524"},{"key":"4416_CR64","doi-asserted-by":"crossref","unstructured":"Xiao S, Yan J, Yang X, Zha H, Chu SM (2017b) Modeling the intensity function of point process via recurrent neural networks. In: Proceedings of the 31st AAAI conference on artificial intelligence, pp 1597\u20131603","DOI":"10.1609\/aaai.v31i1.10724"},{"key":"4416_CR65","unstructured":"Yan J (2019) Recent advance in temporal point process: from machine learning perspective. SJTU technical report"},{"key":"4416_CR66","doi-asserted-by":"crossref","unstructured":"Yan J, Xu H, Li L (2019) Modeling and applications for temporal point processes. In: Proceedings of the 25th ACM SIGKDD, pp 3227-3228","DOI":"10.1145\/3292500.3332298"},{"key":"4416_CR67","volume-title":"Understanding big data: analytics for enterprise class hadoop and streaming data","author":"P Zikopoulos","year":"2011","unstructured":"Zikopoulos P, Eaton C et al (2011) Understanding big data: analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media, New York"}],"container-title":["Journal of Ambient Intelligence and Humanized Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-022-04416-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12652-022-04416-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-022-04416-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T16:37:00Z","timestamp":1701103020000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12652-022-04416-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,23]]},"references-count":67,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["4416"],"URL":"https:\/\/doi.org\/10.1007\/s12652-022-04416-5","relation":{},"ISSN":["1868-5137","1868-5145"],"issn-type":[{"type":"print","value":"1868-5137"},{"type":"electronic","value":"1868-5145"}],"subject":[],"published":{"date-parts":[[2022,9,23]]},"assertion":[{"value":"17 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}