Fall Research 2017: Week 1

This first week of the Fall semester, I continued my literary review of publications regarding DDoS attacks and defense from the summer and continued with Unity's VR tutorials. One article that I read was "Critical Infrastructure Network DDoS Defense, via Cognitive Learning" by T. Booth and K. Andersson. This publication showed how cognitive learning can be used to significantly mitigate any effects of DDoS network attacks, against the critical infrastructure. One of the things mentioned in the publication is a new DDoS trend that has become more prevalent. This trend is the use of mobile phone BotNets to launch attacks. BotNets are a network of private computers infected with malicious software and controlled as a group without the owners' knowledge, typically to send spam. I consider this to be important because it involves the most current technology being used as a cyber weapon. Additionally, the key of the cognitive anti-DDoS solution is that it would be easy to implement, inexpensive, and significantly mitigate DDoS attacks.

I also read through "A Collaborative DDoS Defence Framework Using Network Function Virtualization" by B. Rashidi et al. This publication talks about the increasing volume of DDoS attacks, especially SYN Flood attacks, the most common type of DDoS. They also discuss the emerging of network function virtualization (NFV) technology. This tech introduces new opportunities to decrease the amount of proprietary hardware needed to launch and operate network services. This is helpful for their proposed DDoS defense mechanism named CoFence, which facilitates a “domain-helps-domain” collaboration network among NFV-based domain networks, essentially using resource sharing to combat DDoS attacks. I found this to be an interesting idea to incorporate into our game due to its relevance and ease of creating game components for. We could create multiple resource sharing machines that must be connected by the player in the correct order to allow a simulated network to not be overflowed.