Page 270 - Artificial Intelligence in the Age of Neural Networks and Brain Computing

262 CHAPTER 12 Computational Intelligence in the Time

[2] D. Evans, The Internet of Things. How the Next Evolution of the Internet Is Changing
Everything, CISCO White Book, 2011.
[3] P. Marwedel, Embedded Systems Design, Springer, 2011, p. 241.
[4] C. Alippi, Intelligence for Embedded Systems: A Methodological Approach, Springer,
Switzerland, 2014, p. 283.
[5] US-CERT- United States Computer Emergency Readiness Team, Alert (TA16e288A) -
Heightened DDoS Threat Posed by Mirai and Other Botnets, November 2015. Tech.
rep. https://www.us-cert.gov/ncas/alerts/TA16-288A.
[6] C. Alippi, R. Fantacci, D. Marabissi, M. Roveri, A cloud to the ground: the new Frontier
of intelligent and autonomous networks of things, IEEE Communication Magazine 54
(12) (December 2016) 14e20.
[7] Contiki: The Open Source OS for the Internet of Things. http://www.contiki-os.org.
[8] ARM, Introduction to the mbed OS 5 Handbook. https://docs.mbed.com/docs/mbed-os-
handbook/en/latest.
[9] Google Inc. Android Things. https://developer.android.com/things.
[10] C. Alippi, G. Anastasi, M. Di Francesco, M. Roveri, An adaptive sampling algorithm for
effective energy management in wireless sensor networks with energy-hungry sensors,
IEEE-Transactions on Instrumentation and Measurement 59 (2) (February 2010)
335e344.
[11] C. Alippi, R. Polikar, Guest editorial, IEEE neural networks and learning systems, Spe-
cial Issue on “Learning in Nonstationary and Evolving Environments” 25 (1) (January
2014) 9e11.
[12] L.I. Kuncheva, Classifier ensembles for changing environments, in: Proc. 5th Int. Work-
shop Multiple Classifier Systems, 2004, pp. 1e15.
[13] C. Alippi, G. Boracchi, M. Roveri, Hierarchical change-detection tests, IEEE Transac-
tions on Neural Networks and Learning Systems 28 (2) (2017) 246e258.
[14] J. Gama, I. Zliobaite, A. Bifet, M. Pechenizkiy, A. Bouchachia, A survey on concept
drift adaptation, ACM Computing Survey 46 (4) (April 2014) 1e44.
[15] M. Basseville, I.V. Nikiforov, Detection of abrupt changes: theory and application,
Prentice Hall Englewood Cliffs 104 (1993) 529.
[16] R. Isermann, Fault-Diagnosis Systems: Introduction from Fault Detection to Fault
Tolerance, Springer, 2006.
[17] V. Reppa, M.M. Polycarpou, C.G. Panayiotou, Distributed sensor fault diagnosis for a
network of interconnected cyber-physical systems, IEEE Transactions on Control of
Networked Systems 2 (1) (2015) 15e23.
[18] C. Alippi, S. Ntalampiras, M. Roveri, Model-free fault detection and isolation in large-
scale cyber-physical systems, IEEE Transactions on Emerging Topics in Computational
Intelligence (February 2016) 61e71.
[19] C. Alippi, M. Roveri, F. Trovo, A self-building and cluster-based cognitive fault diag-
nosis system for sensor network, IEEE Transactions on Neural Networks and Learning
Systems 25 (6) (June 2014) 1021e1032.
[20] R. Sommer, V. Paxson, Outside the closed world: on using machine learning for
network intrusion detection, in: Proceedings of the 2010 IEEE Symposium on Security
and Privacy, 2010, pp. 305e316.
[21] C. Sinclair, L. Pierce, S. Matzner, An application of machine learning to network intru-
sion detection, in: Proceedings of 15th Annual Computer Security Applications Confer-
ence, 1999, pp. 371e377.