The dendritic cell algorithm for intrusion detection

As one of the solutions to intrusion detection problems, Artificial Immune Systems (AIS) have shown their advantages. Unlike genetic algorithms, there is no one archetypal AIS, instead there are four major paradigms. Among them, the Dendritic Cell Algorithm (DCA) has produced promising results in various applications. The aim of this chapter is to demonstrate the potential for the DCA as a suitable candidate for intrusion detection problems. We review some of the commonly used AIS paradigms for intrusion detection problems and demonstrate the advantages of one particular algorithm, the DCA. In order to clearly describe the algorithm, the background to its development and a formal definition are given. In addition, improvements to the original DCA are presented and their implications are discussed, including previous work done on an online analysis component with segmentation and ongoing work on automated data preprocessing. Based on preliminary results, both improvements appear to be promising for online anomaly-based intrusion detection.Comment: Bio-Inspired Communications and Networking, IGI Global, 84-102, 201

The drivers of heuristic optimization in insect object manufacture and use

Insects have small brains and heuristics or ‘rules of thumb’ are proposed here to be a good model for how insects optimize the objects they make and use. Generally, heuristics are thought to increase the speed of decision making by reducing the computational resources needed for making decisions. By corollary, heuristic decisions are also deemed to impose a compromise in decision accuracy. Using examples from object optimization behavior in insects, we will argue that heuristics do not inevitably imply a lower computational burden or lower decision accuracy. We also show that heuristic optimization may be driven by certain features of the optimization problem itself: the properties of the object being optimized, the biology of the insect, and the properties of the function being optimized. We also delineate the structural conditions under which heuristic optimization may achieve accuracy equivalent to or better than more fine-grained and onerous optimization methods

Emergence of collective behaviour. How Individual Regulation Matters in Elaborating Team Patterns in Football

This project analysed processes leading to the emergence of collective behaviour patterns. Collective behaviour, considered as self-organized, emerges from individual activities that interplay as the activity unfolds. One aim of this project was to explore how individuals regulate their activity to participate to the elaboration of collective behaviour. Sport science literature did not consider the individual regulation as a main focus to understand team behaviour. The regulation has been assumed rather than investigated. To this end, we described the variety of informational resources used by team members during a football game. We adopted an epistemological approach that was respectful of how humans regulate their agent-environment coupling, which was the enactive approach. From this approach, sense-making is assumed to be central in delineating the dynamics of the agent-environment coupling, and the phenomenological experience of the agent was seriously considered in the study designs. The results identified various informational resources, which we ranked along a continuum from local resources to global resources. The subsequent goal was to understand the relationship between individual regulation and its consequences in the collective behaviour. Grounded in the use of a computer simulation tool, the project simulated the spatiotemporal collective behaviour of a multi-agent system built to capture the essentials of football team behaviours and to evaluate how the dynamical outcomes (i.e., the collective behaviour patterns) depend on individual adjustment modalities. These adjustment modalities were implemented in the simulation. More specifically, the simulation study generated a large amount of spatiotemporal data that are hard to capture in ecological situation with natural setting, in order to test to what extent the collective behaviour dynamical outcomes were changed when a single players changed their adjustments. The collective behaviour was characterised through metrics accounting for team spatiotemporal properties such as surface area and team stretching. The results showed a condensed behaviour associated with the local adjustment modality and a deployed behaviour associated with global adjustment modalities. A complementary study investigated the possibilities of controlling human regulation through interaction rules. The results showed that various interaction rules involved different informational resources and adjustment modality. Moreover, the results demonstrated that a local informational resource did not necessarily involve a local adjustment which describe the complexity of the regulation processes

Trusted community : a novel multiagent organisation for open distributed systems

[no abstract