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The human immune system protects the human body against various pathogens like e.g. biological viruses and bacteria. Artificial immune systems reuse the architecture, organization, and workflows of the human immune system for various problems in computer science. In the network security, the artificial immune system is used to secure a network and its nodes against intrusions like viruses, worms, and trojans. However, these approaches are far away from production where they are academic proof-of-concept implementations or use only a small part to protect against a certain intrusion. This article discusses the required steps to bring artificial immune systems into production in the network security domain. It furthermore figures out the challenges and provides the description and results of the prototype of an artificial immune system, which is SANA called.

This document is the final proceedings for the International Conference on Artificial Immune Systems (1st), ICARIS 2002, held 9-11 Sep 2002.

This research lays the groundwork for a network intrusion detection system that can operate with only knowledge of normal network traffic, using a process known as anomaly detection. Real-valued negative selection (RNS) is a specific anomaly detection algorithm that can be used to perform two-class classification when only one class is available for training. Researchers have shown fundamental problems with the most common detector shape, hyperspheres, in high-dimensional space. The research contained herein shows that the second most common detector type, hypercubes, can also cause problems due to biasing certain features in high dimensions. To address these problems, a new detector shape, the hypersteinmetz solid, is proposed, the goal of which is to provide a tradeoff between the problems plaguing hyperspheres and hypercubes. In order to investigate the potential benefits of the hypersteinmetz solid, an effective RNS detector size range is determined. Then, the relationship between content coverage of a dataset and classification accuracy is investigated. Subsequently, this research shows the tradeoffs that take place in high-dimensional data when hypersteinmetzes are chosen over hyperspheres or hypercubes. The experimental results show that detector shape is the dominant factor toward classification accuracy in high-dimensional RNS.

This research lays the groundwork for a network intrusion detection system that can operate with only knowledge of normal network traffic, using a process known as anomaly detection. Real-valued negative selection (RNS) is a specific anomaly detection algorithm that can be used to perform two-class classification when only one class is available for training. Researchers have shown fundamental problems with the most common detector shape, hyperspheres, in high-dimensional space. The research contained herein shows that the second most common detector type, hypercubes, can also cause problems due to biasing certain features in high dimensions. To address these problems, a new detector shape, the hypersteinmetz solid, is proposed, the goal of which is to provide a tradeoff between the problems plaguing hyperspheres and hypercubes. In order to investigate the potential benefits of the hypersteinmetz solid, an effective RNS detector size range is determined. Then, the relationship between content coverage of a dataset and classification accuracy is investigated. Subsequently, this research shows the tradeoffs that take place in high-dimensional data when hypersteinmetzes are chosen over hyperspheres or hypercubes. The experimental results show that detector shape is the dominant factor toward classification accuracy in high-dimensional RNS.

This research lays the groundwork for a network intrusion detection system that can operate with only knowledge of normal network traffic, using a process known as anomaly detection. Real-valued negative selection (RNS) is a specific anomaly detection algorithm that can be used to perform two-class classification when only one class is available for training. Researchers have shown fundamental problems with the most common detector shape, hyperspheres, in high-dimensional space. The research contained herein shows that the second most common detector type, hypercubes, can also cause problems due to biasing certain features in high dimensions. To address these problems, a new detector shape, the hypersteinmetz solid, is proposed, the goal of which is to provide a tradeoff between the problems plaguing hyperspheres and hypercubes. In order to investigate the potential benefits of the hypersteinmetz solid, an effective RNS detector size range is determined. Then, the relationship between content coverage of a dataset and classification accuracy is investigated. Subsequently, this research shows the tradeoffs that take place in high-dimensional data when hypersteinmetzes are chosen over hyperspheres or hypercubes. The experimental results show that detector shape is the dominant factor toward classification accuracy in high-dimensional RNS.

Over the past three years we have developed a novel neural-endocrine system capable of being deployed on actual sailing robots. The initial project plan was to develop a combined neural-immune-endocrine system, but after staffing issues in the first year of the project, work was refocussed to focus on the neural endocrine aspects. Whilst the primary focus was developing a system capable of sailing deployment, along the way, we have developed and investigated the application of neural-endocrine on land-based robotic units as well as land based swarm systems. This allowed to us to continue the work on the neural-endocrine architecture whilst at the same time developing the sailing robotic system (as there was a large amount of time consumed with the more engineering side of work on the sailing robotic system). We feel this project has been a success and we list the highlights from the project.

Most biological rates and times decrease systematically with organism body size. We use an ordinary differential equation (ODE) model of West Nile Virus in birds to show that pathogen replication rates decline with host body size, but natural immune system (NIS) response rates do not change systematically with body size. This is surprising since the NIS has to search for small quantities of pathogens through larger physical spaces in larger organisms, and also respond by producing larger absolute quantities of antibody in larger organisms. We call this scale-invariant detection and response. We hypothesize that the NIS has evolved an architecture to efficiently neutralize pathogens. We investigate a range of architectures using an Agent Based Model (ABM). We find that a sub-modular NIS architecture, in which lymph node number and size both increase sublinearly with body size, efficiently balances the tradeoff between local pathogen detection and global response using antibodies. This leads to nearly scale-invariant detection and response, consistent with experimental data. Similar to the NIS, physical space and resources are also important constraints on Artificial Immune Systems (AIS), especially distributed systems applications used to connect low-powered sensors using short-range wireless communication. We show that AIS problems, like distributed robot control, will also require a sub-modular architecture to efficiently balance the tradeoff between local search for a solution and global response or proliferation of the solution between different components. This research has wide applicability in other distributed systems AIS applications.

Most biological rates and times decrease systematically with organism body size. We use an ordinary differential equation (ODE) model of West Nile Virus in birds to show that pathogen replication rates decline with host body size, but natural immune system (NIS) response rates do not change systematically with body size. This is surprising since the NIS has to search for small quantities of pathogens through larger physical spaces in larger organisms, and also respond by producing larger absolute quantities of antibody in larger organisms. We call this scale-invariant detection and response. We hypothesize that the NIS has evolved an architecture to efficiently neutralize pathogens. We investigate a range of architectures using an Agent Based Model (ABM). We find that a sub-modular NIS architecture, in which lymph node number and size both increase sublinearly with body size, efficiently balances the tradeoff between local pathogen detection and global response using antibodies. This leads to nearly scale-invariant detection and response, consistent with experimental data. Similar to the NIS, physical space and resources are also important constraints on Artificial Immune Systems (AIS), especially distributed systems applications used to connect low-powered sensors using short-range wireless communication. We show that AIS problems, like distributed robot control, will also require a sub-modular architecture to efficiently balance the tradeoff between local search for a solution and global response or proliferation of the solution between different components. This research has wide applicability in other distributed systems AIS applications.

Borrowing from biology and implementing in binary, AIS closely follows immunology and uses it in many areas, including system security. Dr. Julie Greensmith of the University of Nottingham introduces the subject. Crashes, Cosmic Rays and Kernel Panic: http://youtu.be/nr9auoHtvzM Emoji & The Levitating Businessman: http://youtu.be/tITwM5GDIAI Danger Theory: Coming Soon VR Hyperspace Simulator: Coming Soon http://www.facebook.com/computerphile https://twitter.com/computer_phile This video was filmed and edited by Sean Riley. Computer Science at the University of Nottingham: http://bit.ly/nottscomputer Computerphile is a sister project to Brady Haran's Numberphile. See the full list of Brady's video projects at: http://bit.ly/bradychannels Source: https://www.youtube.com/watch?v=u2qRUtg2k3Y Uploader: Computerphile

Artificial immune systems (AIS) draw inspiration from the mechanisms of the natural immune system. They extract ideas from the functioning of the natural immune system in order to use them to build computer models to solve real-world problems. While traditional AIS models mimic the biological immune system’s capacity to distinguish between self and non-self entities, they often face challenges in environments where data may be incomplete or ambiguous. In this paper we introduce neutrosophic AIS that includes degrees of truthiness, falsehood and indeterminacy to better address ambiguity within pattern recognition tasks. Within this neutrosophic framework we discuss and redefine main AIS concepts, including self and non-self categorization, clonal selection, negative selection, and immunological memory. A numerical example, from the field of computer security, illustrates the application of the suggested approach in detecting non-self entities. With the addition of neutrosophic logic, the proposed model significantly enhances AIS’s adaptability and pattern recognition capabilities, addressing uncertainties inherent in real-world applications.

Artificial immune systems (AIS) draw inspiration from the mechanisms of the natural immune system. They extract ideas from the functioning of the natural immune system in order to use them to build computer models to solve real-world problems. While traditional AIS models mimic the biological immune system’s capacity to distinguish between self and non-self entities, they often face challenges in environments where data may be incomplete or ambiguous. In this paper we introduce neutrosophic AIS that includes degrees of truthiness, falsehood and indeterminacy to better address ambiguity within pattern recognition tasks. Within this neutrosophic framework we discuss and redefine main AIS concepts, including self and non-self categorization, clonal selection, negative selection, and immunological memory. A numerical example, from the field of computer security, illustrates the application of the suggested approach in detecting non-self entities. With the addition of neutrosophic logic, the proposed model significantly enhances AIS’s adaptability and pattern recognition capabilities, addressing uncertainties inherent in real-world applications.

The Attacks done by Viruses, Worms, Hackers, etc. are a Network Security-Problem in many Organisations. Current Intrusion Detection Systems have significant Disadvantages, e.g. the need of plenty of Computational Power or the Local Installation. Therefore, we introduce a novel Framework for Network Security which is called SANA. SANA contains an artificial Immune System with artificial Cells which perform certain Tasks in order to to support existing systems to better secure the Network against Intrusions. The Advantages of SANA are that it is efficient, adaptive, autonomous, and massively-distributed. In this Article, we describe the Architecture of the artificial Immune System and the Functionality of the Components. We explain briefly the Implementation and discuss Results.

The Attacks done by Viruses, Worms, Hackers, etc. are a Network Security-Problem in many Organisations. Current Intrusion Detection Systems have significant Disadvantages, e.g. the need of plenty of Computational Power or the Local Installation. Therefore, we introduce a novel Framework for Network Security which is called SANA. SANA contains an artificial Immune System with artificial Cells which perform certain Tasks in order to to support existing systems to better secure the Network against Intrusions. The Advantages of SANA are that it is efficient, adaptive, autonomous, and massively-distributed. In this Article, we describe the Architecture of the artificial Immune System and the Functionality of the Components. We explain briefly the Implementation and discuss Results.

Determining required joint angles to achieve a desired position in a manipulator’s arm is a complicated problem without simple analytical solutions. This paper researches several computational methods based on artificial intelligence (AI) for calculating the joint positions of the 6-DOF robotic arm. We can extrapolate relevance, for example, to the crucial role that robotic manipulator arms play in industrial and medical applications, where enhanced precision and movement efficiency may sharply boost performance and expand applicability. Here, we investigate the effectiveness of methods, such as the artificial immune system (AIS) and multi-layer perceptron-biogeography-based optimization (MLP-BBO). Those AI-driven methods have been applied to determine joint angles for reaching desired positions through simulations for the robotic arm. The results show that the AIS and MLP-BBO approach can handle the intrinsic complexities of the task, thus testifying to the practicability and dependability of these two methods in this application. From the findings in the study, it was indicated that AI-driven techniques can effectively answer the complex problem of the robotic manipulator arm in finding joint angles.