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2018-02-02
Rogers, R., Apeh, E., Richardson, C. J..  2016.  Resilience of the Internet of Things (IoT) from an Information Assurance (IA) perspective. 2016 10th International Conference on Software, Knowledge, Information Management Applications (SKIMA). :110–115.

Internet infrastructure developments and the rise of the IoT Socio-Technical Systems (STS) have frequently generated more unsecure protocols to facilitate the rapid intercommunication between the plethoras of IoT devices. Whereas, current development of the IoT has been mainly focused on enabling and effectively meeting the functionality requirement of digital-enabled enterprises we have seen scant regard to their IA architecture, marginalizing system resilience with blatant afterthoughts to cyber defence. Whilst interconnected IoT devices do facilitate and expand information sharing; they further increase of risk exposure and potential loss of trust to their Socio-Technical Systems. A change in the IoT paradigm is needed to enable a security-first mind-set; if the trusted sharing of information built upon dependable resilient growth of IoT is to be established and maintained. We argue that Information Assurance is paramount to the success of IoT, specifically its resilience and dependability to continue its safe support for our digital economy.

2015-05-06
Derhab, A., Bouras, A., Bin Muhaya, F., Khan, M.K., Yang Xiang.  2014.  Spam Trapping System: Novel security framework to fight against spam botnets. Telecommunications (ICT), 2014 21st International Conference on. :467-471.

In this paper, we inspire from two analogies: the warfare kill zone and the airport check-in system, to tackle the issue of spam botnet detection. We add a new line of defense to the defense-in-depth model called the third line. This line is represented by a security framework, named the Spam Trapping System (STS) and adopts the prevent-then-detect approach to fight against spam botnets. The framework exploits the application sandboxing principle to prevent the spam from going out of the host and detect the corresponding malware bot. We show that the proposed framework can ensure better security against malware bots. In addition, an analytical study demonstrates that the framework offers optimal performance in terms of detection time and computational cost in comparison to intrusion detection systems based on static and dynamic analysis.