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Wednesday, 31 January 2018

Malware Propagation in Large-Scale Networks(2015)

Malware Propagation in Large-Scale Networks(2015)

Malware is pervasive in networks, and poses a critical threat to network security. However, we have very limited understanding of malware behavior in networks to date. In this paper, we investigate how malware propagates in networks from a global perspective. We formulate the problem, and establish a rigorous two layer epidemic model for malware propagation from network to network. Based on the proposed model, our analysis indicates that the distribution of a given malware follows exponential distribution, power law distribution with a short exponential tail, and power law distribution at its early, late and final stages, respectively. Extensive experiments have been performed through two real-world global scale malware data sets, and the results confirm our theoretical findings.
  1. The epidemic theory plays a leading role in malware propagation modelling. The current models for malware spread fall in two categories: the epidemiology model and the control theoretic model.
  2. The control system theory based models try to detect and contain the spread of malware. The epidemiology models are more focused on the number of compromised hosts and their distributions, and they have been explored extensively in the computer science community.
  3. Zou et al. used a susceptible-infected (SI) model to predict the growth of Internet worms at the early stage.
  4. Gao and Liu recently employed a susceptible-infected-recovered (SIR) model to describe mobile virus propagation.
  1. One critical condition for the epidemic models is a large vulnerable population because their principle is based on differential equations.
  2. As pointed by Willinger et al. the findings, which we extract from a set of observed data, usually reflect parts of the studied objects. It is more reliable to extract the-oretical results from appropriate models with confirmation from sufficient real world data set experiments.
  1. In this paper, we study the distribution of malware in terms of networks (e.g., autonomous systems, ISP domains, and abstract networks of smartphones who share the same vulnerabilities) at large scales.
  2. In this kind of setting, we have a sufficient volume of data at a large enough scale to meet the requirements of the SI model. Different from the traditional epidemic models, we break our model into two layers.
  3. First of all, for a given time since the breakout of a malware, we calculate how many networks have been compromised based on the SI model.
  4. Secondly, for a compromised net-work, we calculate how many hosts have been compromised since the time that the network was compromised.
  • Our rigorous analysis, we find that the distribution of a given malware follows an exponential distribution at its early stage, and obeys a power law distribution with a short exponential tail at its late stage, and finally converges to a power law distribution.
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