Self-Organized Specialization and Controlled Emergence in Organic Computing Systems

In this chapter we studied a first approach to generate suitable rule sets for solving classification problems on systems of autonomous, memory constrained components. It was shown that a multi agent system that uses interacting Pittsburgh-style classifier systems can evolve appropiate rule sets. The system evolves specialists for parts of the classification problem and cooperation between them. In this way the components overcome their restricted memory size and are able to solve the entire problem. It was shown that the communication topology between the components strongly influences the average number of components that a request has to pass until it is classified. It was also shown that the introduction of communication costs into the fitness function leads to a more even distribution of knowledge between the components and reduces the communication overhead without influencing the classification performance very much. If the system is used to generate rule sets to solve classification tasks on real hardware systems, communication cost in the training phase can thus lead to a better knowledge distribution and small communication cost. That is, in this way the system will be more robust against the loss of single components and longer reliable in case of limited energy resources

A Multi Agent System for Flow-Based Intrusion Detection Using Reputation and Evolutionary Computation

The rising sophistication of cyber threats as well as the improvement of physical computer network properties present increasing challenges to contemporary Intrusion Detection (ID) techniques. To respond to these challenges, a multi agent system (MAS) coupled with flow-based ID techniques may effectively complement traditional ID systems. This paper develops: 1) a scalable software architecture for a new, self-organized, multi agent, flow-based ID system; and 2) a network simulation environment suitable for evaluating implementations of this MAS architecture and for other research purposes. Self-organization is achieved via 1) a reputation system that influences agent mobility in the search for effective vantage points in the network; and 2) multi objective evolutionary algorithms that seek effective operational parameter values. This paper illustrates, through quantitative and qualitative evaluation, 1) the conditions for which the reputation system provides a significant benefit; and 2) essential functionality of a complex network simulation environment supporting a broad range of malicious activity scenarios. These results establish an optimistic outlook for further research in flow-based multi agent systems for ID in computer networks

Decentralized Packet Clustering in Router-Based Networks

Different types of decentralized clustering problems have been studied so far for networks and multi-agent systems. In this paper we introduce a new type of a decentralized clustering problem for networks. The so called Decentralized Packet Clustering (DPC) problem is to find for packets that are sent around in a network a clustering. This clustering has to be done by the routers using only few computational power and only a small amount of memory. No direct information transfer between the routers is allowed. We investigate the behavior of new a type of decentralized k-means algorithm — called DPClust — for solving the DPC problem. DPClust has some similarities with ant based clustering algorithms. We investigate the behavior of DPClust for different clustering problems and for networks that consist of several subnetworks. The amount of packet exchange between these subnetworks is limited. Networks with different connection topologies for the subnetworks are considered. A dynamic situation where the packet exchange rates between the subnetworks varies over time is also investigated. The proposed DPC problem leads to interesting research problems for network clustering

An Artificial Neural Network-based Decision-Support System for Integrated Network Security

As large-scale Cyber attacks become more sophisticated, local network defenders should employ strength-in-numbers to achieve mission success. Group collaboration reduces individual efforts to analyze and assess network traffic. Network defenders must evolve from an isolated defense in sector policy and move toward a collaborative strength-in-numbers defense policy that rethinks traditional network boundaries. Such a policy incorporates a network watch ap-proach to global threat defense, where local defenders share the occurrence of local threats in real-time across network security boundaries, increases Cyber Situation Awareness (CSA) and provides localized decision-support. A single layer feed forward artificial neural network (ANN) is employed as a global threat event recommender system (GTERS) that learns expert-based threat mitigation decisions. The system combines the occurrence of local threat events into a unified global event situation, forming a global policy that allows the flexibility of various local policy interpretations of the global event. Such flexibility enables a Linux based network defender to ignore windows-specific threats while focusing on Linux threats in real-time. In this thesis, the GTERS is shown to effectively encode an arbitrary policy with 99.7% accuracy based on five threat-severity levels and achieves a generalization accuracy of 96.35% using four distinct participants and 9-fold cross-validation

Systematic Generalization and Emergent Structures in Transformers Trained on Structured Tasks

Transformer networks have seen great success in natural language processing and machine vision, where task objectives such as next word prediction and image classification benefit from nuanced context sensitivity across high-dimensional inputs. However, there is an ongoing debate about how and when transformers can acquire highly structured behavior and achieve systematic generalization. Here, we explore how well a causal transformer can perform a set of algorithmic tasks, including copying, sorting, and hierarchical compositions of these operations. We demonstrate strong generalization to sequences longer than those used in training by replacing the standard positional encoding typically used in transformers with labels arbitrarily paired with items in the sequence. We search for the layer and head configuration sufficient to solve these tasks, then probe for signs of systematic processing in latent representations and attention patterns. We show that two-layer transformers learn reliable solutions to multi-level problems, develop signs of task decomposition, and encode input items in a way that encourages the exploitation of shared computation across related tasks. These results provide key insights into how attention layers support structured computation both within a task and across multiple tasks.Comment: 18 page

Quality-of-service management in IP networks

Quality of Service (QoS) in Internet Protocol (IF) Networks has been the subject of active research over the past two decades. Integrated Services (IntServ) and Differentiated Services (DiffServ) QoS architectures have emerged as proposed standards for resource allocation in IF Networks. These two QoS architectures support the need for multiple traffic queuing systems to allow for resource partitioning for heterogeneous applications making use of the networks. There have been a number of specifications or proposals for the number of traffic queuing classes (Class of Service (CoS)) that will support integrated services in IF Networks, but none has provided verification in the form of analytical or empirical investigation to prove that its specification or proposal will be optimum. Despite the existence of the two standard QoS architectures and the large volume of research work that has been carried out on IF QoS, its deployment still remains elusive in the Internet. This is not unconnected with the complexities associated with some aspects of the standard QoS architectures. [Continues.

Balancing Selfishness and Efficiency in Mobile Ad-hoc Networks:An Agent-based Simulation

We study wireless ad-hoc networks from an agent-based perspective. In our model agents with different strategies such as being selfish, tit-for-tat or battery-based compete and cooperate. If only different levels of selfishness are allowed then being selfish is clearly the dominant strategy. However, introduction of more advanced strategies allows to some extent to combat selfishness. In particular we present a battery-based approach and a hybrid of battery-based and tit-for-tat approaches. The findings give hope that the introduction of widely available ad-hoc networks might at some point be possible. Even when users are given full control of their devices, effective strategies allow for the networks overall to be effective and feasible

HUC-HISF: A Hybrid Intelligent Security Framework for Human-centric Ubiquitous Computing

制度:新 ; 報告番号:乙2336号 ; 学位の種類:博士(人間科学) ; 授与年月日:2012/1/18 ; 早大学位記番号:新584