Optimal Frameworks for Detecting Anomalies in Sensor-Intensive Heterogeneous Networks

References

• Akoglu L, Tong H, Koutra D (2015) Graph based anomaly detection and description: A survey. Data Mining Knowledge Discovery 29(3):626–688.Crossref, Google Scholar
• Antonelli D, Bruno G, Chiusano S (2013) Anomaly detection in medical treatment to discover unusual patient management. IIE Trans. Healthcare Systems Engrg. 3(2):69–77.Crossref, Google Scholar
• Bhuyan MH, Bhattacharyya DK, Kalita JK (2014) Network anomaly detection: Methods, systems and tools. IEEE Comm. Surveys Tutorials 16(1):303–336.Crossref, Google Scholar
• Brice P, Jiang W, Wan G (2011) A cluster-based context-tree model for multivariate data streams with applications to anomaly detection. INFORMS J. Comput. 23(3):364–376.Link, Google Scholar
• Cui G, Zhou J, Yang C, Liu Z (2020) Adaptive graph encoder for attributed graph embedding. Proc. ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 976–985.Google Scholar
• Ding K, Li J, Bhanushali R, Liu H (2019) Deep anomaly detection on attributed networks. Proc. SIAM Internat. Conf. Data Mining (Society for Industrial and Applied Mathematics, Philadelphia), 594–602.Google Scholar
• Durrett R (2006) Random Graph Dynamics (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
• Feng Z, Fu J, Du D, Li F, Sun S (2017) A new approach of anomaly detection in wireless sensor networks using support vector data description. Internat. J. Distributed Sensor Networks 13(1):1–14.Crossref, Google Scholar
• Gao H, Huang H (2018) Deep attributed network embedding. Lang J, ed. Proc. Twenty-Seventh Internat. Joint Conf. Artificial Intelligence (AAAI Press, Palo Alto, CA), 3364–3370.Google Scholar
• Georgescu S-C (2012) Hbmoa applied to design a water distribution network for a town of 50000 inhabitants. UPB Scientific Bull. Series D: Mechanical Engrg. 74(1):91–102.Google Scholar
• Giustolisi O, Simone A, Ridolfi L (2017) Network structure classification and features of water distribution systems. Water Resources Res. 53(4):3407–3423.Crossref, Google Scholar
• Gogoi P, Bhattacharyya D, Borah B, Kalita J (2011) A survey of outlier detection methods in network anomaly identification. Comput. J. 54(4):570–588.Crossref, Google Scholar
• Gulli A, Pal S (2017) Deep Learning with Kera (Packt Publishing, Birmingham, UK).Google Scholar
• Habeeb R, Nasaruddin F, Gani A, Targio Hashem I, Ahmed E, Imran M (2019) Real-time big data processing for anomaly detection: A survey. Internat. J. Inform. Management 45:289–307.Crossref, Google Scholar
• Hamilton WL, Ying R, Leskovec J (2017a) Inductive representation learning on large graphs. Preprint, submitted June 7, https://arxiv.org/abs/1706.02216.Google Scholar
• Hamilton WL, Ying R, Leskovec J (2017b) Representation learning on graphs: Methods and applications. Preprint, submitted September 17, https://arxiv.org/abs/1709.05584.Google Scholar
• Henderson K, Gallagher B, Li L, Akoglu L, Eliassi-Rad T, Tong H, Faloutsos C (2011) It’s who you know: Graph mining using recursive structural features. Proc. ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 663–671.Google Scholar
• Kulldorff M (1997) A spatial scan statistic. Comm. Statist. Theory Methods 26(6):1481–1496.Crossref, Google Scholar
• Lee S-C, Faloutsos C, Chae D-K, Kim S-W (2017) Fraud detection in comparison-shopping services: Patterns and anomalies in user click behaviors. IEICE Trans. Inform. Systems E100.D(10):2659–2663.Crossref, Google Scholar
• Li J, Dani H, Hu X, Liu H (2017) Radar: Residual analysis for anomaly detection in attributed networks. Sierra C, ed. Internat. Joint Conf. Artificial Intelligence (International Joint Conferences on Artificial Intelligence, Somerset, NJ), 2152–2158.Google Scholar
• Liu S, Qu Q, Wang S (2018) Heterogeneous anomaly detection in social diffusion with discriminative feature discovery. Inform. Sci. (439-440):1–18.Google Scholar
• Liu F, Ting K, Zhou Z-H (2012) Isolation-based anomaly detection. ACM Trans. Knowledge Discovery Data 6(1):1–39.Crossref, Google Scholar
• Liu S, Liu Y, Ni L, Fan J, Li M (2010) Toward mobility-based clustering. Proc. ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 919–927.Google Scholar
• Moshtaghi M (2013) Anomaly detection in heterogeneous sensed data. Unpublished PhD thesis, Department of Computing and Information Systems, Melbourne School of Engineering, The University of Melbourne, Melbourne, Australia.Google Scholar
• NREL (2016) Bay Area synthetic network. Accessed April 15, 2021, https://egriddata.org/dataset/bay-area-synthetic-network.Google Scholar
• Parra L, Sendra S, Lloret J, Bosch I (2015) Development of a conductivity sensor for monitoring groundwater resources to optimize water management in smart city environments. Sensors (Basel) 15(9):20990–21015.Crossref, Google Scholar
• Perozzi B, Akoglu L (2016) Scalable anomaly ranking of attributed neighborhoods. 16th SIAM Internat. Conf. Data Mining (Society for Industrial and Applied Mathematics, Philadelphia), 207–215.Google Scholar
• Priebe C, Conroy J, Marchette D, Park Y (2005) Scan statistics on Enron graphs. Comput. Math. Organ. Theory 11(3):229–247.Crossref, Google Scholar
• Ranshous S, Shen S, Koutra D, Harenberg S, Faloutsos C, Samatova N (2015) Anomaly detection in dynamic networks: A survey. Wiley Interdisciplinary Rev. Comput. Stat. 7(3):223–247.Crossref, Google Scholar
• Rossi RA, Ahmed NK (2015) The network data repository with interactive graph analytics and visualization. Proc. Twenty-Ninth AAAI Conf. Artificial Intelligence (AAAI Press, Palo Alto, CA), 4292–4293Google Scholar
• Seo Y, Loukas A, Perraudin N (2019) Discriminative structural graph classification. Preprint, submitted May 31, https://arxiv.org/abs/1905.13422.Google Scholar
• Speakman I, McFowland E, Neill D (2015) Scalable detection of anomalous patterns with connectivity constraints. J. Comput. Graphical Statist. 24(4):1014–1033.Crossref, Google Scholar
• Tango T, Takahashi K (2005) A flexibly shaped spatial scan statistic for detecting clusters. Internat. J. Health Geographics 4(11):1–15.Google Scholar
• Theissler A (2017) Detecting known and unknown faults in automotive systems using ensemble-based anomaly detection. Knowledge-Based Systems 123:163–173.Crossref, Google Scholar
• Wang G, Jiao S, Gao X (2009) The application of information fusion technology in the monitoring system of mine pump. First Internat. Conf. Inform. Sci. Engrg (IEEE, Piscataway, NJ), 3876–3879.Google Scholar
• Yang Q, Barria JA, Green TC (2011) Communication infrastructures for distributed control of power distribution networks. IEEE Trans. Indust. Inform. 7(2):316–327.Crossref, Google Scholar
• Yelundur A, Campbell K (2013) Robust parametric empirical Bayes based anomaly detection for flight safety events. Integrated Comm. Navigation Surveillance Conf. (IEEE, Piscataway, NJ).Google Scholar