Jamie Twycross

Immune system approaches to intrusion detection --- a review

The use of artificial immune systems in intrusion detection is an appealing concept for two reasons. First, the human immune system provides the human body with a high level of protection from invading pathogens, in a robust, self-organised and distributed manner. Second, current techniques used in computer security are not able to cope with the dynamic and increasingly complex nature of computer systems and their security. It is hoped that biologically inspired approaches in this area, including the use of immune-based systems will be able to meet this challenge. Here we review the algorithms used, the development of the systems and the outcome of their implementation. We provide an introduction and analysis of the key developments within this field, in addition to making suggestions for future research.

Immune System Approaches to Intrusion Detection – A Review

The use of artificial immune systems in intrusion detection is an appealing concept for two reasons. Firstly, the human immune system provides the human body with a high level of protection from invading pathogens, in a robust, self-organised and distributed manner. Secondly, current techniques used in computer security are not able to cope with the dynamic and increasingly complex nature of computer systems and their security. It is hoped that biologically inspired approaches in this area, including the use of immune-based systems will be able to meet this challenge. Here we collate the algorithms used, the development of the systems and the outcome of their implementation. It provides an introduction and review of the key developments within this field, in addition to making suggestions for future research.

A thermodynamic switch modulates abscisic acid receptor sensitivity

Abscisic acid (ABA) is a key hormone regulating plant growth, development and the response to biotic and abiotic stress. ABA binding to pyrabactin resistance (PYR)/PYR1‐like (PYL)/Regulatory Component of Abscisic acid Receptor (RCAR) intracellular receptors promotes the formation of stable complexes with certain protein phosphatases type 2C (PP2Cs), leading to the activation of ABA signalling. The PYR/PYL/RCAR family contains 14 genes in Arabidopsis and is currently the largest plant hormone receptor family known; however, it is unclear what functional differentiation exists among receptors. Here, we identify two distinct classes of receptors, dimeric and monomeric, with different intrinsic affinities for ABA and whose differential properties are determined by the oligomeric state of their apo forms. Moreover, we find a residue in PYR1, H60, that is variable between family members and plays a key role in determining oligomeric state. In silico modelling of the ABA activation pathway reveals that monomeric receptors have a competitive advantage for binding to ABA and PP2Cs. This work illustrates how receptor oligomerization can modulate hormonal responses and more generally, the sensitivity of a ligand‐dependent signalling system.

Articulation and clarification of the dendritic cell algorithm

The Dendritic Cell algorithm (DCA) is inspired by recent work in innate immunity. In this paper a formal description of the DCA is given. The DCA is described in detail, and its use as an anomaly detector is illustrated within the context of computer security. A port scan detection task is performed to substantiate the influence of signal selection on the behaviour of the algorithm. Experimental results provide a comparison of differing input signal mappings.

Dendritic Cells for Anomaly Detection

Artificial immune systems, more specifically the negative selection algorithm, have previously been applied to intrusion detection. The aim of this research is to develop an intrusion detection system based on a novel concept in immunology, the Danger Theory. Dendritic Cells (DCs) are antigen presenting cells and key to the activation of the human immune system. DCs perform the vital role of combining signals from the host tissue and correlate these signals with proteins known as antigens. In algorithmic terms, individual DCs perform multi-sensor data fusion based on time-windows. The whole population of DCs asynchronously correlates the fused signals with a secondary data stream. The behaviour of human DCs is abstracted to form the DC Algorithm (DCA), which is implemented using an immune inspired framework, libtissue. This system is used to detect context switching for a basic machine learning dataset and to detect outgoing portscans in real-time. Experimental results show a significant difference between an outgoing portscan and normal traffic.

Towards a conceptual framework for innate immunity

Innate immunity now occupies a central role in immunology. However, artificial immune system models have largely been inspired by adaptive not innate immunity. This paper reviews the biological principles and properties of innate immunity and, adopting a conceptual framework, asks how these can be incorporated into artificial models. The aim is to outline a meta-framework for models of innate immunity.

MODULAR ASSEMBLY OF CELL SYSTEMS BIOLOGY MODELS USING P SYSTEMS

In this paper we propose an extension of a systems/synthetic biology modelling framework based on P systems that explicitly includes modularity. Modularisation in cellular systems can be produced by chemical specificity, spatial localisation and/or temporal modulation within cellular compartments. The first two of these modularisation features, the focus of this paper, can be easily specified and analysed in P systems using sets of rewriting rules to describe chemical specificity and membranes to represent spatial localisation. Our methodology enables the assembly of cell systems biology models by combining modules which represent functional subsystems. A case study consisting of a bacterial colony system is presented to illustrate our approach.

Stochastic and deterministic multiscale models for systems biology: an auxin-transport case study

BackgroundStochastic and asymptotic methods are powerful tools in developing multiscale systems biology models; however, little has been done in this context to compare the efficacy of these methods. The majority of current systems biology modelling research, including that of auxin transport, uses numerical simulations to study the behaviour of large systems of deterministic ordinary differential equations, with little consideration of alternative modelling frameworks.ResultsIn this case study, we solve an auxin-transport model using analytical methods, deterministic numerical simulations and stochastic numerical simulations. Although the three approaches in general predict the same behaviour, the approaches provide different information that we use to gain distinct insights into the modelled biological system. We show in particular that the analytical approach readily provides straightforward mathematical expressions for the concentrations and transport speeds, while the stochastic simulations naturally provide information on the variability of the system.ConclusionsOur study provides a constructive comparison which highlights the advantages and disadvantages of each of the considered modelling approaches. This will prove helpful to researchers when weighing up which modelling approach to select. In addition, the paper goes some way to bridging the gap between these approaches, which in the future we hope will lead to integrative hybrid models.

An immune-based approach to document classification

The human immune system is a complex adaptive system which has provided inspiration for a range of innovative problem solving techniques in areas such as computer security, knowledge management and information retrieval. In this paper the construction and performance of a novel immune-based learning algorithm is explored whose distributed, dynamic and adaptive nature offers many potential advantages over more traditional models. Through a process of cooperative coevolution a classifier is generated which consists of a set of detectors whose local dynamics enable the system as a whole to group positive and negative examples of a concept. The immune-based learning algorithm is first validated on a standard dataset. Then, combined with an HTML feature extractor, it is tested on a web-based document classification task and found to outperform traditional classification paradigms. Further applications in content filtering, recommendation systems and user profile generation are also directly relevant to the work presented.

The Infobiotics Workbench

Summary: The Infobiotics Workbench is an integrated software suite incorporating model specification, simulation, parameter optimization and model checking for Systems and Synthetic Biology. A modular model specification allows for straightforward creation of large-scale models containing many compartments and reactions. Models are simulated either using stochastic simulation or numerical integration, and visualized in time and space. Model parameters and structure can be optimized with evolutionary algorithms, and model properties calculated using probabilistic model checking. Availability: Source code and binaries for Linux, Mac and Windows are available at http://www.infobiotics.org/infobiotics-workbench/; released under the GNU General Public License (GPL) version 3. Contact: Natalio.Krasnogor@nottingham.ac.uk