Network Worm Propagation Model Based on a Campus Network Topology

Mohammed, Aliyu; Nor, Sulaiman Mohd; Marsono, Muhammad Nadzir

doi:10.1109/ithings/cpscom.2011.29

Cited by 3 publications

(2 citation statements)

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“…Initial researches on Internet worms investigated the SIR model, looking specifically at declines in the nodes of the I group; whereas the spread of vaccine programs resulted in an increase in the nodes of R group [3]. Over time, researchers have increasingly focused on changes in the number of nodes in the I group by installing security programs such as Firework [4]. Recent studies have focused on the vulnerability of nodes in the R group that get re-categorized into the S group over time, due to the adaptations in Internet worm [5].…”

Section: Related Workmentioning

confidence: 99%

Internet Worm Propagation Model Using Centrality Theory

Kwon¹,

Choi²,

Baek³

2016

Kyungpook mathematical journal

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Abstract. The emergence of various Internet worms, including the stand-alone Code Red worm that caused a distributed denial of service (DDoS), has prompted many studies on their propagation speed to minimize potential damages. Many studies, however, assume the same probabilities for initially infected nodes to infect each node during their propagation, which do not reflect accurate Internet worm propagation modelling. Thus, this paper analyzes how Internet worm propagation speed varies according to the number of vulnerable hosts directly connected to infected hosts as well as the link costs between infected and vulnerable hosts. A mathematical model based on centrality theory is proposed to analyze and simulate the effects of degree centrality values and closeness centrality values representing the connectivity of nodes in a large-scale network environment on Internet worm propagation speed.

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Section: Related Workmentioning

confidence: 99%

Internet Worm Propagation Model Using Centrality Theory

Kwon¹,

Choi²,

Baek³

2016

Kyungpook mathematical journal

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“…The infection rate and removal rate are conceptualized for the purpose of simulation based on the population considered during the experiment. Further details on the campus network worm propagation modelling can be found in [24]. The Figures 4(a, b) above shows the typical worm spread observed for a period of some days on the campus network environment.…”

Section: Worm Propagation Based On Sir Model Simulationmentioning

confidence: 99%

Malware Risk Analysis on the Campus Network with Bayesian Belief Network

Mohammed¹,

Jamil²,

Nor³

et al. 2013

IJNSA

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A security network management system is for providing clear guidelines on risk evaluation and assessmentfor enterprise networks. The threat and risk assessment is conducted to safeguard enterprise networkservices to maintain system confidentiality, integrity, and availability through effective control strategies.In this paper, based on our previous work in analyzing integrated information security management andmalware propagation on the campus network through mathematical modelling, we proposed BayesianBelief Network with inference level indicator to enable the decision maker to understand and provideappropriate mitigation decisions on the risks posed. We experimentally placed monitoring sensors on thecampus network that gives the threat alert priority levels and magnitude on the vulnerable informationassets. These methods will give a direction on the belief inferred due to malware prevalence on theinformation security assets for better understanding

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