Naomi Kuze

Controlling Large-Scale Self-Organized Networks with Lightweight Cost for Fast Adaptation to Changing Environments

Self-organization has potential for high scalability, adaptability, flexibility, and robustness, which are vital features for realizing future networks. Convergence of self-organizing control, however, is slow in some practical applications compared to control with conventional deterministic systems using global information. It is therefore important to facilitate convergence of self-organizing controls. In controlled self-organization, which introduces an external controller into self-organizing systems, the network is controlled to guide systems to a desired state. Although existing controlled self-organization schemes could achieve this feature, convergence speed for reaching an optimal or semioptimal solution is still a challenging task. We perform potential-based self-organizing routing and propose an optimal feedback method using a reduced-order model for faster convergence at low cost. Simulation results show that the proposed mechanism improves the convergence speed of potential-field construction (i.e., route construction) by at most 22.6 times with low computational and communication cost.

Enhancing Convergence with Optimal Feedback for Controlled Self-Organizing Networks.

To tackle with problems emerging with rapid growth of information networks in scale and complexity, selforganization is one of promising design principles for future networks. Convergence of self-organizing controls, however, is pointed out to be comparatively slow in some practical applications. Therefore, it is important to reveal and enhance convergence of self-organizing controls. In controlled self-organization, which introduces an external observer/controller into self-organizing systems, systems are controlled in order to guide them to the desired state. Although previous controlled self-organization schemes could achieve this feature, convergence speed for reaching an optimal or a semi-optimal solution is still a challenging task. In this paper, we take potential-based self-organizing routing and provide an optimal feedback for faster convergence using the future state of the system. Simulation results show that the convergence speed of potentials is improved by 7.3 times with a proposed mechanism.

Hierarchical Optimal Control Method for Controlling Self-Organized Networks with Light-Weight Cost

Self-organization has potential for high scalability, adaptability, flexibility, and robustness, which are vital features for realizing future networks. Convergence of self-organizing control, however, is comparatively slow in some practical applications. It is therefore important to enhance convergence of self-organizing controls without sacrificing the above advantages. Controlled self-organization is one key idea for that, which introduces an external controller into self-organizing systems to guide them to a desired state. We previously designed an external controller that provided the optimal input for fast convergence, however, it suffered from scalability issues. In this paper, we propose a hierarchical control system where a network is partitioned into some sub-networks, sub-controllers manage respective sub-networks, and the top-level deals with the global network stability. The proposed system achieves fast convergence speed with low computational and communication cost.

Self-Organizing Control Mechanism Based on Collective Decision-Making for Information Uncertainty

Because of the rapid growth in the scale and complexity of information networks, self-organizing systems are increasingly being used to realize novel network control systems that are highly scalable, adaptable, and robust. However, the uncertainty of information (with regard to incompleteness, vagueness, and dynamics) in self-organizing systems makes it difficult for them to work appropriately in accordance with the network state. In this study, we apply a model of the collective decision-making of animal groups to enable self-organizing control mechanisms to adapt to information uncertainty. Specifically, we apply a mathematical model of collective decision-making that is known as the effective leadership model (ELM). In the ELM, informed individuals (those who are experienced or well-informed) take the role of leading the others. In contrast, uninformed individuals (those who perceive only local information) follow neighboring individuals. As a result of the collective behavior of informed/uninformed individuals, the animal group achieves consensus. We consider a self-organizing control mechanism using potential-based routing with an optimal control, and propose a mechanism for determining a data-packet forwarding scheme based on the ELM. Through evaluation by simulation, we show that, in a situation in which the perceived information is incomplete and dynamic, nodes can forward data packets in accordance with the network state by applying the ELM.

Self-organizing wireless sensor networks based on biological collective decision making for treating information uncertainty

Due to the rapid growth in scale and complexity of information networks, self-organizing systems have been focused on for realizing new network control architectures that have high scalability, adaptability, and robustness. However, in self-organizing systems, the uncertainty (incompleteness, ambiguity, and dynamicity) of information observable for components in the system can lead to the slow adaptation to environmental changes and the lack of a global optimality, which complicates a practical use of self-organizing systems in industrial and business fields. In this study, we adopt the principle of collective decision making, in which a coordinated decision in a group is achieved through local interactions of components, in order to realize a network control mechanism adaptable to such information uncertainty. Specifically, we apply Effective Leadership model, which is a mathematical model of collective decision making, to a self-organizing control mechanism. In Effective Leadership model, there are two types of individuals, informed and non-informed ones, and collective decision is achieved through local interaction of them. Through simulation experiments, we reveal the advantages and characteristics of the network control mechanism based on Effective Leadership model.

Hierarchical Optimal Control Method for Controlling Large-Scale Self-Organizing Networks

Self-organization has the potential for high scalability, adaptability, flexibility, and robustness, which are vital features for realizing future networks. The convergence of self-organizing control, however, is slow in some practical applications in comparison with control by conventional deterministic systems using global information. It is therefore important to facilitate the convergence of self-organizing controls. In controlled self-organization, which introduces an external controller into self-organizing systems, the network is controlled to guide systems to a desired state. Although existing controlled self-organization schemes could achieve the same state, it is difficult for an external controller to collect information about the network and to provide control inputs to the network, especially when the network size is large. This is because the computational cost for designing the external controller and for calculating the control inputs increases rapidly as the number of nodes in the network becomes large. Therefore, we partition a network into several sub-networks and introduce two types of controllers, a central controller and several sub-controllers that control the network in a hierarchical manner. In this study, we propose a hierarchical optimal feedback mechanism for self-organizing systems and apply this mechanism to potential-based self-organizing routing. Simulation results show that the proposed mechanism improves the convergence speed of potential-field construction (i.e., route construction) up to 10.6-fold with low computational and communication costs.

Detection of vulnerability scanning using features of collective accesses based on information collected from multiple honeypots

Attacks against websites are increasing rapidly with the expansion of web services. An increasing number of diversified web services make it difficult to prevent such attacks due to many known vulnerabilities in websites. To overcome this problem, it is necessary to collect the most recent attacks using decoy web honeypots and to implement countermeasures against malicious threats. Web honeypots collect not only malicious accesses by attackers but also benign accesses such as those by web search crawlers. Thus, it is essential to develop a means of automatically identifying malicious accesses from mixed collected data including both malicious and benign accesses. Specifically, detecting vulnerability scanning, which is a preliminary process, is important for preventing attacks. In this study, we focused on classification of accesses for web crawling and vulnerability scanning since these accesses are too similar to be identified. We propose a feature vector including features of collective accesses, e.g., intervals of request arrivals and the dispersion of source port numbers, obtained with multiple honeypots deployed in different networks for classification. Through evaluation using data collected from 37 honeypots in a real network, we show that features of collective accesses are advantageous for vulnerability scanning and crawler classification.

Crawler classification using ant-based clustering scheme

Attacks against websites are increasing rapidly with the expansion of web services. More and more diversified web services make it difficult to prevent such attacks due to many known vulnerabilities in websites. To overcome this problem, it is necessary to collect latest attacks using decoy web honeypots and to implement countermeasures against malicious threats. Web honeypots collect not only malicious accesses by attackers but also benign accesses such as those by web search crawlers. Thus, it is essential to develop a means of identifying malicious accesses automatically from mixed collected data including both malicious and benign accesses. In this study, we have focused on detection of crawlers whose accesses has been increasing rapidly. A related study proposed a crawler detection scheme in which crawlers are identified based on the features of well-known crawlers such as Google crawlers. However, the diversity of crawler accesses has been increasing rapidly, and adapting to that diversity is a challenging task. Therefore, we have adapted AntTree, a bio-inspired clustering scheme that has high scalability and adaptability, for crawler detection. Through our evaluations using data collected in a real network, we show that AntTree can detect crawlers more precisely than a conventional scheme.

A predictive mechanism for enhancing adaptability of self-organised routing

To tackle problems emerging with rapid growth of information networks in scale and complexity, bio-inspired self-organisation is a promising design principle for future networks. However, self-organising systems fall into local optima or converge slowly under some environmental conditions. This can make self-organising systems slow to adapt to environmental change, despite robustness against environmental change being an important feature expected from self-organisation. To adapt to dynamically changing conditions while retaining its distributed nature, each component predicts the future state of its neighbours from past behaviour, and proceeds according to the predicted states. We take AntNet, an ant-based routing protocol, and add a mechanism to accelerate path convergence with prediction. Simulation results show that introducing our predictive mechanism reduces recovery time by up to 60%.

Proposal and Evaluation of Ant-Based Routing with Autonomous Zoning for Convergence Improvement

To tackle problems emerging with rapid growth of information networks in scale and complexity, bio-inspired self organization is considered one of promising design principles of a new generation network which is scalable, robust, adaptive, and sustainable. However, self-organizing systems would fall into a local optimum or never converge under some environmental conditions. Controlled or guided self-organization is a novel concept attracting many researchers in these years, where loose and moderate control is imposed on a self-organizing system to push it toward a desired state. In this paper, we take Ant Net, an ant-based routing protocol, as an example and consider a mechanism to accelerate convergence by limiting the search space. The proposed mechanism is compared with Ant Net and HOPNET from viewpoints of the convergence time, path length, and control overhead. Simulation results show that our proposal can accelerate convergence of ant-based routing to a shorter path than Ant Net and with lower control overhead than HOPNET.