The Experts below are selected from a list of 12 Experts worldwide ranked by ideXlab platform

Mhr. Khouzani - One of the best experts on this subject based on the ideXlab platform.

  • Malware-propagative Markov random fields
    Malware Diffusion Models for Wireless Complex Networks, 2020
    Co-Authors: Vasileios Karyotis, Mhr. Khouzani
    Abstract:

    In the previous chapter, a queuing theory based framework for Modeling Malware Diffusion was presented and analyzed, capitalizing on analytic tools for closed queuing systems. It allowed Modeling the Diffusion over both static fixed-topology networks and dynamic networks with churn. Results were presented for both nonpropagative and propagative types of networks yielding a very powerful and generic framework with numerous potentials for network designers and scientists. In this chapter, a different but still probabilistic framework for Modeling Malware Diffusion is presented. This framework differs from the previous as it utilizes models developed in statistical mechanics and more specifically Markov Random Fields to achieve Modeling accuracy, flexibility, and simplicity of application. It is founded on simple concepts and can be applied very easily, yielding rather accurate solutions, which are very close to ideal ones with high probability. Furthermore, it can be applied in more general settings than the queuing based techniques. This chapter presents the case of propagative networks and provides results for various types of complex networks, e.g. regular, random, scale-free, small-world, and random geometric, which are in agreement with relevant results in the previous chapter.

  • Chapter 10 – The road ahead
    Malware Diffusion Models for Wireless Complex Networks, 2020
    Co-Authors: Vasileios Karyotis, Mhr. Khouzani
    Abstract:

    Several different frameworks and methodologies for Modeling Malware Diffusion in wireless and, in general, complex communications networks were presented in the previous chapters. However, even though some of them model Malware Diffusion holistically and generically, employing radical mathematical tools for their purposes, several open problems regarding already explored facets of Malware Diffusion, and many unexplored aspects of the corresponding research area remain to be addressed. Especially in light of the emergence of the Network Science perspective presented in the first chapter, several of the traditional Malware Diffusion problems can be tracked in alternative forms. At the same time, as technology evolves, new problems of their own merit emerge, in various types of networks and application domains. This chapter will cover these aspects, providing a brief overview of the most notable open problems, explaining how the presented techniques can be employed toward solving them. Additional directions for future research on more general problems areas of Malware Diffusion Modeling and network security are provided, linking them with the methodologies presented in this book and explaining the potentials for fruitful results.

  • Applications of state-of-the-art Malware Modeling frameworks
    Malware Diffusion Models for Wireless Complex Networks, 2020
    Co-Authors: Vasileios Karyotis, Mhr. Khouzani
    Abstract:

    In the previous parts of the book, and especially the main part, various frameworks for Modeling Malware Diffusion have been presented. Most of them are rather generic and apply to various types of network topologies, applications, etc. However, they all focus on Malware Diffusion behavior and analysis. In this chapter, we explore their applications in other scenarios and disciplines, e.g. Malware attack strategies and information dissemination (Diffusion). The main link between other disciplines and Malware Diffusion is that whenever a form of Diffusion emerges, e.g. information flow, the corresponding dynamics can be mapped to a suitable Malware Diffusion problem and solved accordingly with the techniques presented in the previous part of the book. Thus, the presented techniques essentially correspond to more generic frameworks which can be further exploited in the corresponding applications. In addition, taking the inverse approach, obtaining inspiration from the presented applications and results can be proven valuable for devising new methods and approaches in the area of Malware propagation. In this chapter, we present some interesting applications and we briefly demonstrate how the Malware Diffusion frameworks can be used in other domains within Network Science.

Vasileios Karyotis - One of the best experts on this subject based on the ideXlab platform.

  • Malware-propagative Markov random fields
    Malware Diffusion Models for Wireless Complex Networks, 2020
    Co-Authors: Vasileios Karyotis, Mhr. Khouzani
    Abstract:

    In the previous chapter, a queuing theory based framework for Modeling Malware Diffusion was presented and analyzed, capitalizing on analytic tools for closed queuing systems. It allowed Modeling the Diffusion over both static fixed-topology networks and dynamic networks with churn. Results were presented for both nonpropagative and propagative types of networks yielding a very powerful and generic framework with numerous potentials for network designers and scientists. In this chapter, a different but still probabilistic framework for Modeling Malware Diffusion is presented. This framework differs from the previous as it utilizes models developed in statistical mechanics and more specifically Markov Random Fields to achieve Modeling accuracy, flexibility, and simplicity of application. It is founded on simple concepts and can be applied very easily, yielding rather accurate solutions, which are very close to ideal ones with high probability. Furthermore, it can be applied in more general settings than the queuing based techniques. This chapter presents the case of propagative networks and provides results for various types of complex networks, e.g. regular, random, scale-free, small-world, and random geometric, which are in agreement with relevant results in the previous chapter.

  • Chapter 10 – The road ahead
    Malware Diffusion Models for Wireless Complex Networks, 2020
    Co-Authors: Vasileios Karyotis, Mhr. Khouzani
    Abstract:

    Several different frameworks and methodologies for Modeling Malware Diffusion in wireless and, in general, complex communications networks were presented in the previous chapters. However, even though some of them model Malware Diffusion holistically and generically, employing radical mathematical tools for their purposes, several open problems regarding already explored facets of Malware Diffusion, and many unexplored aspects of the corresponding research area remain to be addressed. Especially in light of the emergence of the Network Science perspective presented in the first chapter, several of the traditional Malware Diffusion problems can be tracked in alternative forms. At the same time, as technology evolves, new problems of their own merit emerge, in various types of networks and application domains. This chapter will cover these aspects, providing a brief overview of the most notable open problems, explaining how the presented techniques can be employed toward solving them. Additional directions for future research on more general problems areas of Malware Diffusion Modeling and network security are provided, linking them with the methodologies presented in this book and explaining the potentials for fruitful results.

  • Applications of state-of-the-art Malware Modeling frameworks
    Malware Diffusion Models for Wireless Complex Networks, 2020
    Co-Authors: Vasileios Karyotis, Mhr. Khouzani
    Abstract:

    In the previous parts of the book, and especially the main part, various frameworks for Modeling Malware Diffusion have been presented. Most of them are rather generic and apply to various types of network topologies, applications, etc. However, they all focus on Malware Diffusion behavior and analysis. In this chapter, we explore their applications in other scenarios and disciplines, e.g. Malware attack strategies and information dissemination (Diffusion). The main link between other disciplines and Malware Diffusion is that whenever a form of Diffusion emerges, e.g. information flow, the corresponding dynamics can be mapped to a suitable Malware Diffusion problem and solved accordingly with the techniques presented in the previous part of the book. Thus, the presented techniques essentially correspond to more generic frameworks which can be further exploited in the corresponding applications. In addition, taking the inverse approach, obtaining inspiration from the presented applications and results can be proven valuable for devising new methods and approaches in the area of Malware propagation. In this chapter, we present some interesting applications and we briefly demonstrate how the Malware Diffusion frameworks can be used in other domains within Network Science.

Yoichi Shinoda - One of the best experts on this subject based on the ideXlab platform.

  • Modeling Malware Diffusion in Wireless Networks with Nodes' Heterogeneity and Mobility
    2010 Proceedings of 19th International Conference on Computer Communications and Networks, 2010
    Co-Authors: Hoai-nam Nguyen, Yoichi Shinoda
    Abstract:

    Advances in wireless communications along with improvements in hardware have allowed smart phones to interact with each other easier in an open environment where virus can diffuse. While many studies have modeled the spread of Malware, little has been done to take into account different types of devices that may concurrently exist in a mixed wireless network. In this paper, we have therefore developed an analytical model that incorporates diversity of entity as well as interactions between different classes of network items to investigate their impacts on the spread of virus propagation. Besides, our model also investigates the dynamics of viral dissemination in such networks that accounts for nodes' mobility. The proposed model is able to depict the variations and dynamics of Malware in a heterogeneous wireless ad hoc network. A formula to calculate the possible average number of newly infected devices in the considered system is also derived as well as the conditions for the stability of the network. On this number, we find that heterogeneity of nodes has impact on Malware propagation; that is: Malware has a wider extent. The relationship between mobility and Malware dispersal is also investigated in our model. We also conduct numerical simulations to understand changes and the equilibrium of a network under Malware propagation.

Hoai-nam Nguyen - One of the best experts on this subject based on the ideXlab platform.

  • Modeling Malware Diffusion in Wireless Networks with Nodes' Heterogeneity and Mobility
    2010 Proceedings of 19th International Conference on Computer Communications and Networks, 2010
    Co-Authors: Hoai-nam Nguyen, Yoichi Shinoda
    Abstract:

    Advances in wireless communications along with improvements in hardware have allowed smart phones to interact with each other easier in an open environment where virus can diffuse. While many studies have modeled the spread of Malware, little has been done to take into account different types of devices that may concurrently exist in a mixed wireless network. In this paper, we have therefore developed an analytical model that incorporates diversity of entity as well as interactions between different classes of network items to investigate their impacts on the spread of virus propagation. Besides, our model also investigates the dynamics of viral dissemination in such networks that accounts for nodes' mobility. The proposed model is able to depict the variations and dynamics of Malware in a heterogeneous wireless ad hoc network. A formula to calculate the possible average number of newly infected devices in the considered system is also derived as well as the conditions for the stability of the network. On this number, we find that heterogeneity of nodes has impact on Malware propagation; that is: Malware has a wider extent. The relationship between mobility and Malware dispersal is also investigated in our model. We also conduct numerical simulations to understand changes and the equilibrium of a network under Malware propagation.