In artificial intelligence, artificial immune systems (AIS) are a class of computationally intelligent, rule-based machine learning systems inspired by the principles and processes of the vertebrate immune system. The algorithms are typically modeled after the immune system's characteristics of learning and memory for use in problem-solving.
Definition[edit]
The field of Artificial Immune Systems (AIS) is concerned with abstracting the structure and function of the immune system to computational systems, and investigating the application of these systems towards solving computational problems from mathematics, engineering, and information technology. AIS is a sub-field of Biologically-inspired computing, and Natural computation, with interests in Machine Learning and belonging to the broader field of Artificial Intelligence.
Immunity ĭ-mu´nĭ-te the condition of being immune; the protection against infectious disease conferred either by the immune response generated by immunization or previous infection or by other nonimmunologic factors. It encompasses the capacity to distinguish foreign material from self, and to neutralize, eliminate, or metabolize that which is. Artificial immunity occurs when antibodies develop in response to the presence of a specific antigen, as from vaccination or exposure to an infectious disease. According to Vaccines.gov, infections are the most common cause of sickness in humans. Every human being has some degree of natural immunity to infectious agents.
Artificial Immune Systems (AIS) are adaptive systems, inspired by theoretical immunology and observed immune functions, principles and models, which are applied to problem solving.[1]
AIS is distinct from computational immunology and theoretical biology that are concerned with simulating immunology using computational and mathematical models towards better understanding the immune system, although such models initiated the field of AIS and continue to provide a fertile ground for inspiration. Finally, the field of AIS is not concerned with the investigation of the immune system as a substrate for computation, unlike other fields such as DNA computing.
History[edit]
AIS emerged in the mid-1980s with articles authored by Farmer, Packard and Perelson (1986) and Bersini and Varela (1990) on immune networks. However, it was only in the mid-1990s that AIS became a field in its own right. Forrest et al. (on negative selection) and Kephart et al.[2] published their first papers on AIS in 1994, and Dasgupta conducted extensive studies on Negative Selection Algorithms. Hunt and Cooke started the works on Immune Network models in 1995; Timmis and Neal continued this work and made some improvements. De Castro & Von Zuben's and Nicosia & Cutello's work (on clonal selection) became notable in 2002. The first book on Artificial Immune Systems was edited by Dasgupta in 1999.
Currently, new ideas along AIS lines, such as danger theory and algorithms inspired by the innate immune system, are also being explored. Although some believe that these new ideas do not yet offer any truly 'new' abstract, over and above existing AIS algorithms. This, however, is hotly debated, and the debate provides one of the main driving forces for AIS development at the moment. Other recent developments involve the exploration of degeneracy in AIS models,[3][4] which is motivated by its hypothesized role in open ended learning and evolution.[5][6]
Originally AIS set out to find efficient abstractions of processes found in the immune system but, more recently, it is becoming interested in modelling the biological processes and in applying immune algorithms to bioinformatics problems.
In 2008, Dasgupta and Nino [7] published a textbook on Immunological Computation which presents a compendium of up-to-date work related to immunity-based techniques and describes a wide variety of applications.
Techniques[edit]
The common techniques are inspired by specific immunological theories that explain the function and behavior of the mammalianadaptive immune system.
- Clonal Selection Algorithm: A class of algorithms inspired by the clonal selection theory of acquired immunity that explains how B and T lymphocytes improve their response to antigens over time called affinity maturation. These algorithms focus on the Darwinian attributes of the theory where selection is inspired by the affinity of antigen–antibody interactions, reproduction is inspired by cell division, and variation is inspired by somatic hypermutation. Clonal selection algorithms are most commonly applied to optimization and pattern recognition domains, some of which resemble parallel hill climbing and the genetic algorithm without the recombination operator.[8]
- Negative Selection Algorithm: Inspired by the positive and negative selection processes that occur during the maturation of T cells in the thymus called T cell tolerance. Negative selection refers to the identification and deletion (apoptosis) of self-reacting cells, that is T cells that may select for and attack self tissues. This class of algorithms are typically used for classification and pattern recognition problem domains where the problem space is modeled in the complement of available knowledge. For example, in the case of an anomaly detection domain the algorithm prepares a set of exemplar pattern detectors trained on normal (non-anomalous) patterns that model and detect unseen or anomalous patterns.[9]
- Immune Network Algorithms: Algorithms inspired by the idiotypic network theory proposed by Niels Kaj Jerne that describes the regulation of the immune system by anti-idiotypic antibodies (antibodies that select for other antibodies). This class of algorithms focus on the network graph structures involved where antibodies (or antibody producing cells) represent the nodes and the training algorithm involves growing or pruning edges between the nodes based on affinity (similarity in the problems representation space). Immune network algorithms have been used in clustering, data visualization, control, and optimization domains, and share properties with artificial neural networks.[10]
- Dendritic Cell Algorithms: The Dendritic Cell Algorithm (DCA) is an example of an immune inspired algorithm developed using a multi-scale approach. This algorithm is based on an abstract model of dendritic cells (DCs). The DCA is abstracted and implemented through a process of examining and modeling various aspects of DC function, from the molecular networks present within the cell to the behaviour exhibited by a population of cells as a whole. Within the DCA information is granulated at different layers, achieved through multi-scale processing.[11]
See also[edit]
Notes[edit]
Artificial Immunity Mac Os Download
- ^de Castro, Leandro N.; Timmis, Jonathan (2002). Artificial Immune Systems: A New Computational Intelligence Approach. Springer. pp. 57–58. ISBN978-1-85233-594-6.
- ^Kephart, J. O. (1994). 'A biologically inspired immune system for computers'. Proceedings of Artificial Life IV: The Fourth International Workshop on the Synthesis and Simulation of Living Systems. MIT Press. pp. 130–139.
- ^Andrews and Timmis (2006). A Computational Model of Degeneracy in a Lymph Node. Lecture Notes in Computer Science. 4163. pp. 164–177. doi:10.1007/11823940_13. ISBN978-3-540-37749-8. S2CID2539900.
- ^Mendao; et al. (2007). 'The Immune System in Pieces: Computational Lessons from Degeneracy in the Immune System'. Foundations of Computational Intelligence (FOCI): 394–400. doi:10.1109/FOCI.2007.371502. ISBN978-1-4244-0703-3. S2CID5370645.
- ^Edelman and Gally (2001). 'Degeneracy and complexity in biological systems'. Proceedings of the National Academy of Sciences of the United States of America. 98 (24): 13763–13768. Bibcode:2001PNAS..9813763E. doi:10.1073/pnas.231499798. PMC61115. PMID11698650.
- ^Whitacre (2010). 'Degeneracy: a link between evolvability, robustness and complexity in biological systems'. Theoretical Biology and Medical Modelling. 7 (6): 6. doi:10.1186/1742-4682-7-6. PMC2830971. PMID20167097.
- ^Dasgupta, Dipankar; Nino, Fernando (2008). Immunological Computation: Theory and Applications. CRC Press. p. 296. ISBN978-1-4200-6545-9.
- ^de Castro, L. N.; Von Zuben, F. J. (2002). 'Learning and Optimization Using the Clonal Selection Principle'(PDF). IEEE Transactions on Evolutionary Computation. 6 (3): 239–251. doi:10.1109/tevc.2002.1011539.
- ^Forrest, S.; Perelson, A.S.; Allen, L.; Cherukuri, R. (1994). 'Self-nonself discrimination in a computer'(PDF). Proceedings of the 1994 IEEE Symposium on Research in Security and Privacy. Los Alamitos, CA. pp. 202–212.
- ^Timmis, J.; Neal, M.; Hunt, J. (2000). 'An artificial immune system for data analysis'(PDF). BioSystems. 55 (1): 143–150. doi:10.1016/S0303-2647(99)00092-1. PMID10745118.
- ^Greensmith, J.; Aickelin, U. (2009). Artificial Dendritic Cells: Multi-faceted Perspectives(PDF). Human-Centric Information Processing Through Granular Modelling. Studies in Computational Intelligence. 182. pp. 375–395. CiteSeerX10.1.1.193.1544. doi:10.1007/978-3-540-92916-1_16. ISBN978-3-540-92915-4. Archived from the original(PDF) on 2011-08-09. Retrieved 2009-06-19.
References[edit]
- J.D. Farmer, N. Packard and A. Perelson, (1986) 'The immune system, adaptation and machine learning', Physica D, vol. 2, pp. 187–204
- H. Bersini, F.J. Varela, Hints for adaptive problem solving gleaned from immune networks. Parallel Problem Solving from Nature, First Workshop PPSW 1, Dortmund, FRG, October, 1990.
- D. Dasgupta (Editor), Artificial Immune Systems and Their Applications, Springer-Verlag, Inc. Berlin, January 1999, ISBN3-540-64390-7
- V. Cutello and G. Nicosia (2002) 'An Immunological Approach to Combinatorial Optimization Problems' Lecture Notes in Computer Science, Springer vol. 2527, pp. 361–370.
- L. N. de Castro and F. J. Von Zuben, (1999) 'Artificial Immune Systems: Part I -Basic Theory and Applications', School of Computing and Electrical Engineering, State University of Campinas, Brazil, No. DCA-RT 01/99.
- S. Garrett (2005) 'How Do We Evaluate Artificial Immune Systems?' Evolutionary Computation, vol. 13, no. 2, pp. 145–178. http://mitpress.mit.edu/journals/pdf/EVCO_13_2_145_0.pdf
- V. Cutello, G. Nicosia, M. Pavone, J. Timmis (2007) An Immune Algorithm for Protein Structure Prediction on Lattice Models, IEEE Transactions on Evolutionary Computation, vol. 11, no. 1, pp. 101–117. https://web.archive.org/web/20120208130715/http://www.dmi.unict.it/nicosia/papers/journals/Nicosia-IEEE-TEVC07.pdf
- Villalobos-Arias M., Coello C.A.C., Hernández-Lerma O. (2004) Convergence Analysis of a Multiobjective Artificial Immune System Algorithm. In: Nicosia G., Cutello V., Bentley P.J., Timmis J. (eds) Artificial Immune Systems. ICARIS 2004. Lecture Notes in Computer Science, vol 3239. Springer, Berlin, Heidelberg. DOI https://doi.org/10.1007/978-3-540-30220-9_19
External links[edit]
- AISWeb: The Online Home of Artificial Immune Systems Information about AIS in general and links to a variety of resources including ICARIS conference series, code, teaching material and algorithm descriptions.
- ARTIST: Network for Artificial Immune Systems Provides information about the UK AIS network, ARTIST. It provides technical and financial support for AIS in the UK and beyond, and aims to promote AIS projects.
- Computer Immune Systems Group at the University of New Mexico led by Stephanie Forrest.
- AIS: Artificial Immune Systems Group at the University of Memphis led by Dipankar Dasgupta.
- IBM Antivirus Research Early work in AIS for computer security.
Artificial Immunity Mac Os X
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Macintosh operating systems, specifically Mac OS X, have a reputation of being very secure, much more so than Windows XP.
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Mac OS X is built on what is considered to be one of the more secure Unix-based operating systems, BSD. However, that's not the only reason Macs have had a reputation of being more secure.
Windows has the dominant market share, which gives attackers the largest number of targets to saturate when attacking networks -- and let's face it, Microsoft has done a poor job in the past of building a secure operating system, browser and applications. This has changed significantly with the well accepted 'patch Tuesday' process and a concentrated focus by Microsoft to improve Windows XP and the upcoming Windows Vista release.
This has created a false sense of security for Mac OS X users, though. While the Mac operating system is more secure than PC operating systems at this point in time, that doesn't mean Macs are immune. Overconfident Mac users may find themselves unprepared when a worm or exploit does hit.
Apple Becomes Vulnerable
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Two patches for code execution vulnerabilities were released almost immediately following the introduction of the Intel-based Mac Pro running Mac OS X 10.4.7. In the fall of 2006, a Symantec study reported that the number of vulnerabilities in the Mac Safari Internet browser doubled during the first half of 2006 compared to the previous six months.
Commotion was stirred up at the 2006 Black Hat Conference in Las Vegas after speakers demonstrated a Macintosh vulnerability in third-party 802.11 WiFi drivers. While Apple attempted to defuse the criticism as a third-party problem, the company ended up delivering patches for two separate stack buffer overflow problems in the Apple AirPort wireless drivers.
The fact of the matter is that despite Apple's work to maintain the image of Macs as secure devices, researchers are concentrating much more heavily on finding underlying security vulnerabilities in Mac software. As a result, we are seeing security patches for Apple software now on a regular basis.
Dont dead mac os. Intel-based Mac Pro introduces a new wrinkle in the Mac security fabric: virtualization. Windows XP can be run as a virtual machine on the Mac Pro, creating a situation where is it just as vulnerable as the any other unsecured or unpatched Windows device.
Mac Security Answers
What should Mac OS X users do to secure their computers? Here are some starting recommendations:
- Don't be complacent. Take the security of any computing platform seriously, whether it's a Mac, PC, PDA or phone. The easiest device to compromise is the one that everyone assumes won't be attacked. Overconfident Mac users are ripe for the picking, so don't become the next security victim by believing your Mac cannot be compromised.
- Apply security updates. Windows users have learned this lesson the hard way and so has the OS manufacturer, Microsoft. Beginning with Windows XP SP2, automatic application of security patches is enabled by default removing one less opportunity for the device to be left unprotected against the latest vulnerability. Whenever possible, apply a Mac OS X security patch automatically so your Mac is up to date with the latest security fixes.
- Use a bi-directional personal firewall. The personal firewall provided with Mac OS X only offers protection for network connections that are inbound to the Mac. Consider upgrading to a third-party firewall, such as free Brickhouse software, that offers inbound and outbound firewall protection. Also, remember that the least intrusive and easiest-to-use personal firewall is one that will likely stay in use and not be disabled due to annoying pop-ups or configuration screens.
- Practice good WiFi security connections. Use a good security and encryption technique, such as WEP, to secure the network. Be cautious when connecting to open networks -- such as at the airport or local coffee shop -- and never initiate a WiFi connection to an ad hoc network, unless you know what the device is on the other end and that it has been properly secured.
- Use AV software. Don't take a chance of being the first Mac user to get the next e-mail-borne virus. Yes, it is common for Mac users to go without antivirus software, but this is slowly changing.
- Use good security practices with Windows virtualization. Secure that Windows virtual session just like any other Windows computer on the network. Automatic updates, personal firewalls and antivirus software are musts for any Windows computer and virtual Windows XP session. A Mac Pro computer is no different.
Replacing complacency with good security practices can protect any Mac OS X user. Believing Macs are secure just because television advertisements say they are builds a false sense of security. The increase of Mac OS X vulnerabilities and the number of patches released clearly show that Mac security may soon be a thing of the past.
Mitchell Ashley is CTO and VP of Customer Experience at StillSecure, where he is responsible for the product strategy and development of the StillSecure suite of network security products. Ashley has more than 20 years of industry experience holding leading positions in data networking, network security, and software product and services development.