Alkasassbeh, M., et. al. (2016). Detecting Distributed Denial of Service Attacks Using Data Mining Techniques. International Journal of Advanced Computer Science and Applications. Vol 7, No. 1, pp. 436-445.

-- A paper detailing different types of DDoS attacks and implementing a method to detect these attacks. Received a data set from this paper for use in our own research

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Baker, Chris (2016, Oct. 20). Recent IoT-based Attacks: What Is the Impact On Managed DNS Operators? Retrieved from http://dyn.com/blog/recent-iot-based-attacks-what-is-the-impact-on-managed-dns-operators/

-- This source offers a deeper understanding of DNS exhaustive attacks using IoT-based attacks (bot nets).

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Bouchaffra, D., Govindaraju, V., & Srihari, S. (1998). A methodology for deriving probabilistic correctness measures from recognizers. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Santa Barbara.

-- A method of measuring "correctness"  for Bayesian models to be used in our paper on tiered HMMs

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Cappé, O., Moulines, E., & Ryden, T. (2006). Inference in Hidden Markov Models. New York, NY: Springer Science+Business Media, Inc

--A book explaining in great detail the different algorithms of Hidden Markov Models, as well as their applications and statistical theory related to them.

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Cherepanov, A. (2017, June 12). Industroyer: biggest threat to industrial control systems since Stuxnet. (WeLiveSecurity) Retrieved June 2017, from https://www.welivesecurity.com/2017/06/12/industroyer-biggest-threat-industrial-control-systems-since-stuxnet/

-- An attack on a power grid using out-of-date computers to gain access to a company's entire power network using protocols not securely adapted for use over the internet

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D. Anstee, Interviewee, Chief Security Technologist - Arbor Networks. [Interview]. 26 April 2017.

-- Discusses the rise in DDoS attacks in recent years in both size and frequency, as well as the effect of IoT devices on these attacks due to their usefulness in botnets and lack of security

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G. Veerendra, "Hacking the internet of things (IoT) a case study on DTH vulnerabilities," 17 October 2016. [Online]. Available: http://www.secpod.com/resource/whitepapers/Hacking-IoT-A-Case-Study-on-Tata-Sky-DTH-Vulnerabilities.pdf. [Accessed June 2017].

-- explains various different attacks centered around IoT devices, as well as the ways in which IoT security can be improved in the future

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Ghahramani, Z. (2001) An Introduction to Hidden Markov Models and Bayesian Networks. International Journal of Pattern Recognition and Artificial Intelligence. 15(1):9-42.

-- Introduces Hidden Markov Models and goes in-depth into the math behind them.

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Krishnaprasad, P. (2017, May). Capturing Attacks on IoT Devices with a Multi-Purpose IoT Honeypot. Indian Institute of Technology Kanpur. 

-- Goes into many common honeypots used for obtaining network attacks and, more specifically, IoT attacks. Presents results into the types of IoT attacks that are most common and what protocols they utilize.

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Kumar, Mohit. (2016, Nov. 09). DDoS Attack Takes Down Central Heating System Amidst Winter In Finland. Retrieved from http://thehackernews.com/2016/11/heating-system-hacked.html

-- An example of how scary IoT attacks can be. The short of it is that an IoT bot net was able to DDoS a central heating system in Finland leaving tenants with no heat for two week in the dead of winter, a deadly situation given the climate.

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Li, C., Qin, Z. Novak, E., & Li, Q. (2017). Securing SDN Infrastructure of IoT-Fog Networks from MitM Attacks. IEEE Internet of Things Journal, PP, 1 - 1. doi:10.1109/JIOT.2017.2685596  

-- Explains vulnerabilities of IoT-Fog networks and details a few different attacks on these networks. Also gives an alternative solution to detecting these specific types of attacks

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Li, J., Najmi, A., & Gray, R. (Feb. 2000). Image Classification by a Two-Dimensional Markov Model. IEEE Transactions on Signal Processing. Vol. 48, No. 2 

---Very good introduction/explanation of hidden markov models, and goes in-depth into 2-D HMMs as applied to image processing

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Lin, J, et al. (2016). A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications. IEEE Internet of Things Journal. DOI 10.1109/JIOT.2017.2683200

--in depth explanation of IoT and fog networks, as well as the different applications and security challenges associated with IoT

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Liu, T., Lamiere, J., & Yang, L. (2014). Proper Initialization of Hidden Markov Models for Industrial Applications. IEEE China Summit and International Conference on Signal and Information Processing.

-- sets guidelines for proper initialization of HMMs due to many industrial applications incorrectly applying HMMs

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Mayzaud, A., Badonnel, R., & Chrisment, I. (2016). A Taxonomy of Attacks in RPL-based Internet of Things. International Journal of Network Security, Vol. 18, No. 3, pp. 459-473.

-- Talks about a lot of different types of IoT attacks on networks. Has some nice graphical representations of the hierarchy of attacks mentioned in the paper.

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Pongle, P., & Chavan, G. (2015). A Survey: Attacks on RPL and 6LoWPAN in IoT. IEEE 2015 International Conference on Pervasive Computing, 1-6, 2015.

-- Talks about multiple types of IoT attacks on RPL. Specifcally, selective forwarding, sinkhole, sybil, hello flooding, wormhole, clone ID, blackhole, DoS, alteration and spoofing, version, local repair, neighbor, and DIS attacks. Well organized with each attack easily identified and explained.

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Velmurugan, T., & Santhanam, T. (2010). Computational Complexity between K-Means and K-Medoids Clustering Algorithms for Normal and Uniform Distributions of Data Points. Journal of Computer Science, 363-368.

-- Explains the process for clustering data sets with K means and K medoids, as well as the differences between the two processes.

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Ying Liu (2011). Hidden hierarchical Markov fields for image modeling. UWSpace. http://hdl.handle.net/10012/5772

-- Goes in-depth with mathematics behind Hidden Markov Fields and applies it all to image modeling/processing.

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Young, Bob (2016). A Taxonomy of IoT Attacks. Retrieved from https://www.linkedin.com/pulse/taxonomy-iot-attacks-bob-young

-- Very nice article detailing the four intentions behind every IoT attacks: Attacks on Data, Control, Controller, and Network.

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Zhang, K., et. al. (2015). Exploiting Mobile Social Behaviors for Sybil Detection. IEEE Conference on Computer Communications (INFOCOM)

-- Paper going over a unique Sybil attack detection method utilizing semi-supervised HMMs