ICITR - 2016

Permanent URI for this collectionhttp://192.248.9.226/handle/123/14728

Browse

Browsing ICITR - 2016 by Author "Kimura, T"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    item: Conference-Full-text
    Joint access-point and channel selection method using markov approximation
    (Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa, Sri Lanka, 2016-12) Kimura, T; Hirata, K; Muraguchi, M; Fernando, KSD
    In recent years, access-points have been densely placed at public spaces. Users can each select an access-point from among such access-points so as to enhance communication quality. Access-point selection methods have thus become an important technical issue. This paper proposes a joint accesspoint and channel selection method using Markov approximation, which adapt to dynamic changes in network conditions. Markov approximation is a distributed optimization framework, where a network is optimized by individual behavior of users forming a time-reversible continuous-time Markov chain. Our method provides an optimal access-point and channel selection strategy according to a time-reversible continuous-time Markov chain, aiming at maximizing the total throughput of users. Simulation experiments demonstrate the effectiveness of the proposed method.
  • Thumbnail Image
    item:
    Stochastic modeling for self-evolving botnets
    (Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa, Sri Lanka, 2016-12) Hirat, K; Kudo, T; Kimura, T; Inoue, Y; Aman, H; Fernando, KSD
    Machine learning techniques have been achieving significant performance improvements in various kinds of tasks, and they are getting applied in many research fields. While we benefit from such techniques in many ways, they can be a serious security threat to the Internet if malicious attackers become able to utilize them to detect software vulnerabilities. This paper introduces a new concept of self-evolving botnets, where computing resources of infected hosts are exploited to discover unknown vulnerabilities in non-infected hosts. We propose a stochastic epidemic model that incorporates such a feature of botnets, and show its behaviors through numerical experiments.