An Improved Worm Mitigation Model for Evaluating the Spread of Aggressive Network Worms

Onwubiko, Cyril; Lenaghan, Andrew; Hebbes, L.

doi:10.1109/eurcon.2005.1630303

Cited by 6 publications

(3 citation statements)

References 6 publications

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“…Later, in (Zou et al, 2002) the RCS model was extended to include host disinfection (the "R" in the SIR model). In the model of (Onwubiko, 2005) for random scanning worms, the states of the SIR model are extended to include susceptible nodes that are removed from the network or simply quarantined. In (Zou et al, 2006), mathematical models and simulations for current and future scanning strategies are presented without considering human interaction (i.e.…”

Section: Related Workmentioning

confidence: 99%

A spatial stochastic model for worm propagation: scale effects

et al. 2007

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Section: Related Workmentioning

confidence: 99%

A spatial stochastic model for worm propagation: scale effects

et al. 2007

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“…In another example, a model where susceptible hosts can become infected and then go back to a susceptible state (e.g., as a result of resetting a system where the propagation code resides in the main memory), is called a SIS model [16]. Other models take into account the fact that nodes can be isolated (e.g., powered down or quarantined) in an attempt to mitigate the worm propagation (e.g., [20]). Furthermore, there are models that attempt to take into account various non-uniformities of the underlying networks: for example, worm propagation may be influenced by bandwidth variations and congestion [16,21,22] or by the non-uniform behaviour of the worm itself (e.g., a worm with varying scan rate) [14].…”

Section: Introductionmentioning

confidence: 98%

Treating scalability and modelling human countermeasures against local preference worms via gradient models

et al. 2008

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A network worm is a specific type of malicious software that self propagates by exploiting application vulnerabilities in network-connected systems. Worm propagation models are mathematical models that attempt to capture the propagation dynamics of scanning worms as a means to understand their behaviour. It turns out that the emerged scalability in worm propagation plays an important role in order to describe the propagation in a realistic way. On the other hand human-based countermeasures also drastically affect the propagation in time and space. This work elaborates on a recent propagation model (Avlonitis et al. in J Comput Virol 3, 87-92, 2007) that makes use of Partial Differential Equations in order to treat correctly scalability and non-uniform behaviour (e.g., local preference worms). The aforementioned gradient model is extended in order to take into account human-based countermeasures that influence the propagation of local-preference worms in the Internet. Certain aspects of scalability emerged in random and local preference strategies are also discussed by means of random field considerations. As a result the size of a critical network that needs to be studied in order to describe the global propagation of a scanning worm is estimated. Finally, we present simulation results that validate the proposed analytical results and demonstrate the higher propagation rate of local preference worms compared with random scanning worms.

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“…These models can also describe the spread behavior of computer worms. Usually, they are referred as dynamic systems represented by differential equations; for example, in the cases shown in (Changchun et al, 2002;Yang-Chenxi, 2003;Tao et al, 2007;Onwubiko et al, 2005;Juan et al, 2010;Hincapié-Ospina, 2007;Tassier, 2005) they are used to represent SI, SIR and SIRS models (Hincapié-Ospina, 2007). However, before considering the dynamic modeling, it is necessary to explain commonly used concepts in specialized literature.…”

Section: Commonly Used Concepts In Modeling Computer Worms Epidemicsmentioning

confidence: 99%

Basic definitions for discrete modeling of computer worms epidemics

et al. 2015

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The information technologies have evolved in such a way that communication between computers or hosts has become common, so much that the worldwide organization (governments and corporations) depends on it; what could happen if these computers stop working for a long time is catastrophic. Unfortunately, networks are attacked by malware such as viruses and worms that could collapse the system. This has served as motivation for the formal study of computer worms and epidemics to develop strategies for prevention and protection; this is why in this paper, before analyzing epidemiological models, a set of formal definitions based on set theory and functions is proposed for describing 21 concepts used in the study of worms. These definitions provide a basis for future qualitative research on the behavior of computer worms, and quantitative for the study of their epidemiological models.Keywords: Epidemic, computer worm, basic definitions, hosts, network, state. RESUMENLas tecnologías de la información han evolucionado de tal manera que la comunicación entre computadoras o hosts se ha vuelto algo común, hasta el punto que la organización a nivel mundial (gobiernos y grandes empresas) depende de esto; lo que pasaría si estas computadoras dejaran de funcionar por un periodo de tiempo largo es catastrófico. Desgraciadamente, las redes de hosts son blanco de ataques por malware como virus y gusanos informáticos que podrían colapsar el sistema. Esto ha servido como motivación para el estudio formal de los gusanos informáticos y sus epidemias con el fin de idear estrategias de prevención y protección; por ello en el presente artículo, y previo al análisis de modelos epidemiológicos, se proponen un conjunto de definiciones formales basadas en la teoría de conjuntos y funciones que permiten describir 21 conceptos utilizados en el estudio de los gusanos informáticos. Estas definiciones servirán de base para futuras investigaciones cualitativas sobre el comportamiento de los gusanos informáticos, y cuantitativas para el estudio de sus modelos epidemiológicos.Palabras clave: Epidemia, gusano informático, definiciones básicas, red de computadoras, estado.

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An Improved Worm Mitigation Model for Evaluating the Spread of Aggressive Network Worms

Cited by 6 publications

References 6 publications

A spatial stochastic model for worm propagation: scale effects

A spatial stochastic model for worm propagation: scale effects

Treating scalability and modelling human countermeasures against local preference worms via gradient models

Basic definitions for discrete modeling of computer worms epidemics

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