Fast Worm Containment Using Feedback Control

Dantu, Ram; Cangussu, João W.; Patwardhan, Satyajit

doi:10.1109/tdsc.2007.1002

Cited by 34 publications

(12 citation statements)

References 16 publications

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“…Contributions span from reliability [10] to security [11], software testing [12], scheduling [13], thread-pool management [14], and more. The fil rouge of virtually all the documented research is that by closing feedback loops around a computing (embedded) system, desired properties-such as a prescribed throughput-can be enforced online.…”

Section: Related Work and Motivationmentioning

confidence: 99%

Power Optimization in Embedded Systems via Feedback Control of Resource Allocation

Maggio

Hoffmann

Santambrogio

et al. 2013

IEEE Trans. Contr. Syst. Technol.

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Embedded systems often operate in so variable conditions that design can only be carried out for some worst-case scenario. This leads to over-provisioned resources, and undue power consumption. Feedback control is an effective (and not yet fully explored) way to tailor resource usage online, thereby making the system behave and consume as if it was optimized for each specific utilization case. A control-theoretical methodology\ud is here proposed to complement architecture design in a view to said tailoring. Experimental results show that a so addressed architecture meets performance requirements, while consuming less power than any fixed (i.e., uncontrolled) one capable of at taining the same goals. Also, the methodology naturally induces computationally lightweight control laws

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Section: Related Work and Motivationmentioning

confidence: 99%

Power Optimization in Embedded Systems via Feedback Control of Resource Allocation

Maggio

Hoffmann

Santambrogio

et al. 2013

IEEE Trans. Contr. Syst. Technol.

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show abstract

“…As traffic is not confined to a particular system and it is related to networks, it is essential that the proposed scheme must be of type network-based worm detection system. In the Internet there must be a provision to detect worms such as Cyber center [8], network telescope [25] and SANSISC [15]. The detections systems can be spread across WWW in order to successfully detect the presence of worms successfully.…”

Section: Worm Detectionmentioning

confidence: 99%

C-Worm Traffic Detection using Power Spectral Density and Spectral Flatness Measure

Mergu¹,

Kumar²

2012

IOSRJCE

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“…Kim and Karp proposed a scheme to automatically generate the worm payload signature through analyzing the network traffic payload [45]. Besides the detection, there is also extensive research on post-detection defense including containment, throttling, and filtering [15,[79][80][81][82][83][84][85].…”

Section: Related Workmentioning

confidence: 99%