Tomotaka Kimura

Location-aware utility-based routing for store-carry-forward message delivery

One of the most important technical problems in store-carry-forward routing is to reduce the number of message copies in networks without increasing the message delivery delay. In order to solve this problem, we focus on utility-based routing schemes, where for a message, utility of a node indicates its proximity to the destination node of the message. Utility-based routing schemes are promising when relay nodes, i.e., nodes with the message (copy), have sufficient opportunities to encounter other nodes. On the other hand, when relay nodes are in extremely sparse areas of nodes and they have few opportunities to encounter other nodes, the routing schemes do not work effectively. This observation naturally leads us to propose a location-aware utility-based routing scheme. The proposed scheme combines a utility-based routing scheme with location-aware probabilistic forwarding, where the forwarding probability is determined based on both node utility and node density at the contact location. With several simulation scenarios, we evaluate the performance of the proposed scheme in terms of the mean number of copies in the network and the mean message delivery delay.

Behavior analysis of self-evolving botnets

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 and simulations.

Probabilistic store-carry-forward message delivery based on node density estimation

We propose a probabilistic store-carry-forward message delivery scheme based on node density estimation. In our scheme, when a node with a message copy encounters another node, the former forwards its copy to the latter with a certain probability. The forwarding probability is determined depending on a node density at the contact location where two nodes encounter. More specifically, when the node density is high, the forwarding probability is set to be low. This policy is designed to avoid excess message copy transmissions in a high node-density area. In general, nodes frequently encounter each other in high node-density areas and message copies rapidly spread over the nodes. In order to determine whether the node density is high or not, each node estimates the node density distribution over the whole network based on the contact location information. The information is collected by each node and exchanged among nodes. With simulation experiments, we evaluate the performance of our scheme in terms of the mean delivery delay and the number of forwarded message copies.

Dynamic access-point selection method using Markov approximation

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 new access-point 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 proposed method provides an optimal channel and access-point 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.

Density-aware store-carry-forward routing with adaptive forwarding probability control

Store-carry-forward routing is a promising solution for achieving end-to-end delivery of messages in intermittently connected mobile ad-hoc networks. To reduce the number of message copies without increasing the message delivery delay, a probabilistic store-carry-forward routing scheme based on node density has been proposed so far. In this routing scheme, the forwarding probability is determined depending on node density and it is set to be small in high node-density areas. Although this routing scheme can suppress the speed of disseminating message copies over high node-density areas, the number of forwarded message copies increases gradually as time goes by. This means that a large delivery delay of a message causes excessively many copies to be disseminated. To solve this problem, we propose a routing scheme with an adaptive forwarding probability control, where the forwarding probability is reduced every time its copy is forwarded to a node. With simulation experiments, we evaluate the performance of our scheme in terms of the mean delivery delay and the mean number of forwarded message copies.

Suppressive Fair Buffer Management Policy for Intermittently Connected Mobile Ad Hoc Networks

We propose a suppressive fair buffer management policy for intermittently connected mobile ad-hoc networks. So far, several buffer management policies have been considered. These existing buffer management policies assume that all stored messages can be replaced when nodes encounter each other. Buffer management policies, however, can prioritize messages stored at the receiving side over those at the sending side. By doing this, message transmissions are suppressive, and thus energy consumption in terms of sending messages is reduced. Moreover, our proposed policy gives relay messages with a small number of message copies to high priority. Specifically, our proposed policy maintains a sharing of buffer spaces that is as fair as possible. In this paper, we reveal how the suppression of receiving messages affects the system performance compared with existing buffer management policies. Through simulation experiments, we show that the suppressive fair buffer management policy improves energy consumption without largely degrading the delivery failure probability and the mean delivery delay.

Adaptive access-point and channel selection method using Markov approximation

This article proposes an access-point and channel selection method for Internet of Things environments. Recently, the number of wireless nodes has increased with the growth of Internet of Things technologies. In order to accommodate traffic generated by the wireless nodes, we need to utilize densely placed wireless access-points. This article introduces a joint optimization problem of access-point and channel selection for such an environment. The proposed method deals with the optimization problem, using Markov approximation which adapts 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. The proposed method searches optimal solution for the access-point and channel selection problem on the time-reversible continuous-time Markov chain. Simulation experiments demonstrate the effectiveness of the proposed method.

Stochastic modeling of self-evolving botnets with vulnerability discovery

Abstract Machine learning techniques have been actively studied and achieved significant performance improvements in various kinds of tasks. 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 discover unknown 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 and the botnets evolve autonomously. We provide a stochastic epidemic model for the self-evolving botnets, and show its behaviors through numerical and simulation experiments.

Simultaneous mixed vertical and horizontal handwritten Japanese character line detection

Teachers consume considerable time and their energy in process of marking examination sheets. To reduce this burden on teachers, we have been developing an automatic marking system. To mark examination sheets automatically, handwritten character lines are extracted, and then the characters on those lines are recognized. In this paper, we discuss how character lines are extracted from Japanese handwritten examination sheets without ruled lines. Japanese characters can be written vertically and horizontally, so examination sheets written in Japanese are consisted of mixed vertical and horizontal (MVH) character lines. Conventional character line extraction methods cannot deal with MVH lines, because they have been developed to consider only horizontal character lines. This paper focuses on the simultaneous detection of MVH character lines. The result of experiments using appropriate examination sheet images shows that our method can detect MVH character lines effectively.

Artificial Neural Network Based Sinhala Character Recognition

Sinhala is the main language spoken by the majority of the population of Sri Lanka. There is a clear need for an optical character recognition (OCR) system for the Sinhala language. However, the language contains very similar characters, which makes it very difficult to distinguish them except on feature analysis. The character recognition rates of previous systems proposed for Sinhala character recognition are low, and so further improvement is needed. Consequently, in this paper, we propose a new Sinhala character recognition method that uses character geometry features and artificial neural network (ANN). The results of experiments conducted using various documentary images of the Sinhala language indicate that the proposed method has better character recognition performance than conventional methods.