Information fusion in the immune system

Biologically-inspired methods such as evolutionary algorithms and neural networks are proving useful in the field of information fusion. Artificial immune systems (AISs) are a biologically-inspired approach which take inspiration from the biological immune system. Interestingly, recent research has shown how AISs which use multi-level information sources as input data can be used to build effective algorithms for realtime computer intrusion detection. This research is based on biological information fusion mechanisms used by the human immune system and as such might be of interest to the information fusion community. The aim of this paper is to present a summary of some of the biological information fusion mechanisms seen in the human immune system, and of how these mechanisms have been implemented as AISs.

'Danger Theory: The Link between AIS and IDS?'

Abstract We present ideas about creating a next generation Intrusion Detection System (IDS) based on the latest immunological theories. The central challenge with computer security is determining the difference between normal and potentially harmful activity. For half a century, developers have protected their systems by coding rules that identify and block specific events. However, the nature of current and future threats in conjunction with ever larger IT systems urgently requires the development of automated and adaptive defensive tools. A promising solution is emerging in the form of Artificial Immune Systems (AIS): The Human Immune System (HIS) can detect and defend against harmful and previously unseen invaders, so can we not build a similar Intrusion Detection System (IDS) for our computers? Presumably, those systems would then have the same beneficial properties as HIS like error tolerance, adaptation and self-monitoring. Current AIS have been successful on test systems, but the algorithms rely on self-nonself discrimination, as stipulated in classical immunology. However, immunologist are increasingly finding fault with traditional self-nonself thinking and a new 'Danger Theory' (DT) is emerging. This new theory suggests that the immune system reacts to threats based on the correlation of various (danger) signals and it provides a method of 'grounding' the immune response, i.e. linking it directly to the attacker. Little is currently understood of the precise nature and correlation of these signals and the theory is a topic of hot debate. It is the aim of this research to investigate this correlation and to translate the DT into the realms of computer security, thereby creating AIS that are no longer limited by self-nonself discrimination. It should be noted that we do not intend to defend this controversial theory per se, although as a deliverable this project will add to the body of knowledge in this area. Rather we are interested in its merits for scaling up AIS applications by overcoming self-nonself discrimination problems.

'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.

Sensing Danger: Innate Immunology for Intrusion Detection

The immune system provides an ideal metaphor for anomaly detection in general and computer security in particular. Based on this idea, artificial immune systems have been used for a number of years for intrusion detection, unfortunately so far with little success. However, these previous systems were largely based on immunological theory from the 1970s and 1980s and over the last decade our understanding of immunological processes has vastly improved. In this paper we present two new immune inspired algorithms based on the latest immunological discoveries, such as the behaviour of Dendritic Cells. The resultant algorithms are applied to real world intrusion problems and show encouraging results. Overall, we believe there is a bright future for these next generation artificial immune algorithms

Investigating a Hybrid Metaheuristic For Job Shop Rescheduling

Previous research has shown that artificial immune systems can be used to produce robust schedules in a manufacturing environment. The main goal is to develop building blocks (antibodies) of partial schedules that can be used to construct backup solutions (antigens) when disturbances occur during production. The building blocks are created based upon underpinning ideas from artificial immune systems and evolved using a genetic algorithm (Phase I). Each partial schedule (antibody) is assigned a fitness value and the best partial schedules are selected to be converted into complete schedules (antigens). We further investigate whether simulated annealing and the great deluge algorithm can improve the results when hybridised with our artificial immune system (Phase II). We use ten fixed solutions as our target and measure how well we cover these specific scenarios.

The Application of a Dendric Cell Algorithm to a Robotic Classifier

The dendritic cell algorithm is an immune-inspired technique for processing time-dependant data. Here we propose it as a possible solution for a robotic classification problem. The dendritic cell algorithm is implemented on a real robot and an investigation is performed into the effects of varying the migration threshold median for the cell population. The algorithm performs well on a classification task with very little tuning. Ways of extending the implementation to allow it to be used as a classifier within the field of robotic security are suggested.

Articulation and Clarification of the Dendric 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.

Integrating Innate and Adaptive Immunity for Intrusion Detection

Network Intrusion Detection Systems (NIDS) monitor a net- work with the aim of discerning malicious from benign activity on that network. While a wide range of approaches have met varying levels of success, most IDS’s rely on having access to a database of known attack signatures which are written by security experts. Nowadays, in order to solve problems with false positive alerts, correlation algorithms are used to add additional structure to sequences of IDS alerts. However, such techniques are of no help in discovering novel attacks or variations of known attacks, something the human immune system (HIS) is capable of doing in its own specialised domain. This paper presents a novel immune algorithm for application to an intrusion detection problem. The goal is to discover packets containing novel variations of attacks covered by an existing signature base.

Modelling Immunological Memory

Accurate immunological models offer the possibility of performing highthroughput experiments in silico that can predict, or at least suggest, in vivo phenomena. In this chapter, we compare various models of immunological memory. We first validate an experimental immunological simulator, developed by the authors, by simulating several theories of immunological memory with known results. We then use the same system to evaluate the predicted effects of a theory of immunological memory. The resulting model has not been explored before in artificial immune systems research, and we compare the simulated in silico output with in vivo measurements. Although the theory appears valid, we suggest that there are a common set of reasons why immunological memory models are a useful support tool; not conclusive in themselves.

An Agent-based Classification Model

The major function of this model is to access the UCI Wisconsin Breast Cancer data-set[1] and classify the data items into two categories, which are normal and anomalous. This kind of classification can be referred as anomaly detection, which discriminates anomalous behaviour from normal behaviour in computer systems. One popular solution for anomaly detection is Artificial Immune Systems (AIS). AIS are adaptive systems inspired by theoretical immunology and observed immune functions, principles and models which are applied to problem solving. The Dendritic Cell Algorithm (DCA)[2] is an AIS algorithm that is developed specifically for anomaly detection. It has been successfully applied to intrusion detection in computer security. It is believed that agent-based modelling is an ideal approach for implementing AIS, as intelligent agents could be the perfect representations of immune entities in AIS. This model evaluates the feasibility of re-implementing the DCA in an agent-based simulation environment called AnyLogic, where the immune entities in the DCA are represented by intelligent agents. If this model can be successfully implemented, it makes it possible to implement more complicated and adaptive AIS models in the agent-based simulation environment.

Dendritic Cells for SYN Scan Detection

Artificial immune systems have previously been applied to the problem of intrusion detection. The aim of this research is to develop an intrusion detection system based on the function of Dendritic Cells (DCs). DCs are antigen presenting cells and key to the activation of the human immune system, behaviour which has been abstracted to form the Dendritic Cell Algorithm (DCA). In algorithmic terms, individual DCs perform multi-sensor data fusion, asynchronously correlating the fused data signals with a secondary data stream. Aggregate output of a population of cells is analysed and forms the basis of an anomaly detection system. In this paper the DCA is applied to the detection of outgoing port scans using TCP SYN packets. Results show that detection can be achieved with the DCA, yet some false positives can be encountered when simultaneously scanning and using other network services. Suggestions are made for using adaptive signals to alleviate this uncovered problem.

Dendritic Cells for Real-Time Anomaly Detection

Dendritic Cells (DCs) are innate immune system cells which have the power to activate or suppress the immune system. The behaviour of human DCs is abstracted to form an algorithm suitable for anomaly detection. We test this algorithm on the real-time problem of port scan detection. Our results show a significant difference in artificial DC behaviour for an outgoing portscan when compared to behaviour for normal processes.

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.

Experimenting with Innate Immunity

libtissue is a software system for implementing and testing AIS algorithms on real-world computer security problems. AIS algorithms are implemented as a collection of cells, antigen and signals interacting within a tissue compartment. Input data to the tissue comes in the form of realtime events generated by sensors monitoring a system under surveillance, and cells are actively able to affect the monitored system through response mechanisms. libtissue is being used by researchers on a project at the University of Nottingham to explore the application of a range of immune-inspired algorithms to problems in intrusion detection. This talk describes the architecture and design of libtissue, along with the implementation of a simple algorithm and its application to a computer security problem.

Libtissue - Implementing Innate Immunity

In a previous paper the authors argued the case for incorporating ideas from innate immunity into artificial immune systems (AISs) and presented an outline for a conceptualframework for such systems. A number of key general properties observed in the biological innate and adaptive immune systems were highlighted, and how such properties might be instantiated in artificial systems was discussed in detail. The next logical step is to take these ideas and build a software system with which AISs with these properties can be implemented and experimentally evaluated. This paper reports on the results of that step - the libtissue system.