@conference {334,
	title = {Automatic Feedback Control for Graceful Degradation of Real-Time Services in the Face of an Attack},
	booktitle = {2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC)},
	year = {2018},
	month = {Oct},
	abstract = {<p>Distributed denial of service (DDoS) attacks continue to pose a serious threat to various businesses and consumers. With the growth in the number of devices connected to the internet, these attacks continue to grow in number. Despite the availability of security tools, the attacks continue to happen and are causing various businesses to sweat. These tools may take anywhere from a few hours to a few days to counter the attacks, which is unacceptable. In this paper, we put forth a novel feedback control mechanism to minimize the effect of volumetric attacks such as DDoS. During an attack, the feedback control model detects and reduces the impact of the attack by maintaining the service level agreements (SLA) of the network service. The controller makes intelligent decisions to ensure the quality of service (QoS) metrics are gracefully degraded by tuning the micro-firewall rules such as the committed information rate and burst size. A proportional Integral (PI) controller is used as a closed-loop feedback controller to maintain the stability of the network in spite of an attack. This proposed architecture is verified in a lab setup, and we observe that we are able to minimize the degradation of the real-time service so that the user{\textquoteright}s quality of experience (QoE) is preserved. We validate the proposed architecture with a model generated by using the system identification technique. Results from the setup show that the closed-loop feedback control model stabilizes the network in real-time.</p>
},
	keywords = {authorisation, automatic feedback control, closed loop systems, closed-loop feedback control model, Computer crime, computer network management, computer network security, DDoS, Distributed Denial of Service attacks, feedback control, Graceful Degradation, Internet, Mathematical model, network service, Packet loss, PI control, proportional Integral controller, QoE, QoS, quality of experience, quality of service, Real Time Services, real-time service, real-time systems, Resilient, service level agreements, service metrics, SLA, state space, Streaming media, System Identification, volumetric attacks},
	doi = {10.1109/CIC.2018.00-37},
	author = {Vempati, Jagannadh and Ram Dantu}
}
@conference {271,
	title = {PhD Forum: A System Identification Approach to Monitoring Network Traffic Security},
	booktitle = {2015 IEEE 2nd International Conference on Cyber Security and Cloud Computing},
	year = {2015},
	month = {Nov},
	abstract = {<p>Network security is a growing area of interest for cyber systems, especially given the increasing number of attacks on companies each year. Though there are a vast amount of tools already available, System Identification (SI) complements intrusion detection systems to help manage network traffic stability. SI is the science of building mathematical models of dynamic systems. This paper introduces the use of SI for modeling network traffic and utilizes a linear time invariant model to analyze performance of real connections and attack instances. We generated several ARX models where each represented a different threat state in the network. We utilized the KDD CUP 1999{\textquoteright}s DARPA dataset to analyze the performance when dealing with different attacks. Results show that the average model fit was 84.14\% when determining if the system was experiencing normal traffic. This value is promising because it shows how well the system is able to determine a network state in a given time when fed input.</p>
},
	keywords = {ARX, ARX model, autoregressive processes, Computational modeling, computer network security, Computer security, cyber system, dynamic system, Electronic mail, intrusion detection system, linear time invariant model, Mathematical model, Models, network traffic security, network traffic stability, Predictive models, Security, Silicon, System Identification, system identification approach, telecommunication traffic},
	doi = {10.1109/CSCloud.2015.85},
	author = {Mayo, Quentin and Bryce, Renee and Ram Dantu}
}
