Hofmeyr, S.A. and Forrest, S.: Architecture for an Artificial Immune System. Research Notes. Department of Computer Science, University of New Mexico (2003). Home/Search Document Details and Download
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The Role of Crossover in an Immunity Based Genetic Algorithm for.. - Huang (Correct)
....specificity of an antibody. Note that the consequence of a partial matching rule is that there is a trade o# between the number of antibodies used and their specificity as the specificity of antibodies increases, so does the number of antibodies required to achieve a certain level of detection [6]. For the scoring rule discussed in this building blockbased recognition problem, we can also expand its definition by allowing partial match. That is, a prefect building block match indicates that an antibody scores if all of its bits at a building block are complementary to those of an antigen. ....
Hofmeyr, S. A., and Forrest, S.: Architecture for an Artificial Immune System. Evolutionary Computation, 8(4) (2000) 443-473.
Enabling Automatic Adaptation in Systems With.. - Raz, Koopman, Shaw (2002) (2 citations) (Correct)
....analysis [10, 9, 5] testing [7] and intrusion detection [17] However, these naturally have a different domain, and often concentrate on a specific technique. Our long term vision is to increase the dependability of soft5 ware systems through self healing [25] The artificial immune system [14] concentrates on the security domain. It compares sequences of events as the basic detection method for intrusion detection. IBM s eLiza [2] is an autonomic computing [1] project dealing mainly with connectivity problems in self managing servers. BEAM [20] is an end to end method for real time ....
S. Hofmeyr and S. Forrest. Architecture for an artificialimmune system. In Evolutionary Computation Journal, 2000.
The Danger Theory and Its Application to Artificial Immune.. - Aickelin, Cayzer (2002) (1 citation) (Correct)
....[22] In this section we will present indicative examples of such artificial systems, explain their current shortcomings and show how the Danger Theory might help overcome some of these. One of the first such approaches is presented by Forrest et al. [11] and extended by Hofmeyr and Forrest [13]. This work is concerned with building an Artificial Immune System that is able to detect non self in the area of network security where non self is defined as an undesired connection. All connections are modelled as binary strings and there is a set of known good and bad connections, which is ....
Hofmeyr S, Forrest S, Architecture for an Artificial Immune System, Evolutionary Computation 8(4), 443-473, 2000.
Eliciting User Expectations for Data Behavior via.. - Raz, Buchheit.. (2003) (Correct)
.... problems are scarce and either require domain knowledge [24, 18, 4, 19] or provide a specific technique [15] Our approach of inferring the characteristics of a data feed from its behavior is similar to work in the areas of program analysis [12, 11, 3] testing [9] and intrusion detection [20, 16]. However, these naturally have a different domain, and often concentrate on a single technique. Daikon [12] dynamically discovers likely program invariants from program executions. We incorporate Daikon in our invariant inference tool kit. Bugs as deviant behavior [11] infers beliefs from ....
....specific to code. Observation based testing [9] uses clustering and visualization techniques over program execution profiles to identify unusual executions. We have similar techniques in our tool kit. Intrusion detection mostly looks for patterns in sequences of events, such as network traffic [16]) or user shell commands [20] Many people have been analyzing WIM data. However, most are concerned with transportation issues, not data quality. Quality analysis was done in [6] However, it was domain specific, concentrating on the daily sum of ESAL. 6 Conclusions and future work We proposed ....
S. Hofmeyr et al. Architecture for an artificial immune system. In Evolutionary Computation, 2000.
Computation in the Wild - Forrest, Balthrop, Glickman, Ackley (2002) Self-citation (Forrest) (Correct)
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S. A. Hofmeyr and S. Forrest. Architecture for an artificial immune system. Evolutionary Computation Journal, 8(4):443--473, 2000.
Revisiting LISYS: Parameters and Normal Behavior - Balthrop, Forrest, Glickman (2002) Self-citation (Forrest) (Correct)
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S. Hofmeyr and S. Forrest. Architecture for an artificial immune system. Evolutionary Comp. Jrnl., 8(4):443--473, 2000.
Coverage and Generalization in an Artificial Immune.. - Balthrop, Esponda.. (2002) (2 citations) Self-citation (Forrest) (Correct)
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S. Hofmeyr and S. Forrest. Architecture for an artificial immune system. Evolutionary Computation Journal, 8(4):443--473, 2000.
Information Immune Systems - Chao, Forrest (2003) Self-citation (Forrest) (Correct)
....strings were used as negative detectors to detect novel sequences of bytes, such as those introduced when a virus corrupts or infects a file. The ARTIS framework is an extension of this work that applies negative selection to detect anomalies in streams of data rather than in static data sets [27, 28]. The ARTIS framework was demonstrated on the problem of network anomaly intrusion detection [27, 28, 4] in which any unusual cluster of TCP connections is flagged as anomalous. Many useful sources of information contain unexpected but interesting data, however, so it might be undesirable for an ....
....when a virus corrupts or infects a file. The ARTIS framework is an extension of this work that applies negative selection to detect anomalies in streams of data rather than in static data sets [27, 28] The ARTIS framework was demonstrated on the problem of network anomaly intrusion detection [27, 28, 4] in which any unusual cluster of TCP connections is flagged as anomalous. Many useful sources of information contain unexpected but interesting data, however, so it might be undesirable for an IIS to reject all novel data. Consequently, the IIS design is inspired by the immune system s ability to ....
S. A. Hofmeyr and S. Forrest, "Architecture for an artificial immune system," Evolutionary Computation, vol. 8, no. 4, pp. 443--473, 2000.
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Hofmeyr, S.A. and Forrest, S.: Architecture for an Artificial Immune System. Research Notes. Department of Computer Science, University of New Mexico (2003).
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Hofmeyr, S. A., and Forrest, S. Architecture for an Artificial Immune System. Evolutionary Computation, pp. 443--473, vol. 8, num. 4, 2000.
Benefits of Bio-inspired Technologies for Networked Embedded.. - Dressler (2006) (Correct)
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S. Hofmeyer and S. Forrest, "Architecture for an Artificial Immune System," Evolutionary Computation, vol. 8 (2000) 443-473
An Artificial Immune System as a Recommender for Web Sites - Tom Morrison University (Correct)
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Hofmeyr, SA and Forrest, S. (2000). Architecture for an Artificial Immune System. Evolutionary Computation 7, pp 45-68.
Anomaly Detection Using Self/Nonself Discrimination for the Linux .. - Olsson (Correct)
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Stephen Hofmeyr and Stephanie Forrest, Architecture for an Artificial Immune System, in Journal of Evolut ionary Computation, 2000
USAID: Unifying Signature-Based and Anomaly-Based Intrusion.. - li, das, zhou (2005) (Correct)
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S. Hofmeyr and S. Forrest. Architecture for an artificial immune system. Evolutionary Computation, 8(4):443--473, 2000.
Anomaly Detection Using Self/Nonself Discrimination - Olsson (2003) (Correct)
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Stephen Hofmeyr and Stephanie Forrest, Architecture for an Artificial Immune System, in Journal of Evolurionary Computation, 2000
Detecting Danger: Applying A Novel Immunological.. - Greensmith.. (2004) (Correct)
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Hofmeyr S. and Forrest S., 2000, `Architecture for an Artificial Immune System', Evolutionary Computation, 8,(4), 443-473.
New Frontiers for an Artificial Immune System - Greensmith (2003) (Correct)
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Steven A. Hofmeyr and Stephanie Forrest. Architecture for an artificial immune system. Evolutionary Computation, 8(4):443--473, 2000.
An Artificial Immune System Approach to Misbehavior.. - Le Boudec, Sarafijanovic (2004) (1 citation) (Correct)
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S. A Hofmeyr and S. Forrest "Architecture for an Artificial Immune System". Evolutionary Computation 7(1):45-68. 2000.
An Artificial Immune System Approach to Misbehavior.. - Le Boudec, Sarafijanovic (2003) (1 citation) (Correct)
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S. A Hofmeyr and S. Forrest "Architecture for an Artificial Immune System". Evolutionary Computation 7(1):45-68. 2000.
Anomaly Detection Using Real-Valued Negative Selection - Gonzalez, Dasgupta (2004) (2 citations) (Correct)
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Hofmeyr, S. and S. Forrest: 2000, `Architecture for an Artificial Immune System'. Evolutionary Computation 8(4), 443--473.
An Artificial Immune System as a Recommender for Web Sites - Morrison, Aickelin (2002) (Correct)
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Hofmeyr, SA and Forrest, S. (2000). Architecture for an Artificial Immune System. Evolutionary Computation 7, pp 45-68.
Automated Assistance for Eliciting User Expectations - Raz, Buchheit, Shaw.. (Correct)
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S. Hofmeyr and S. Forrest. Architecture for an artificial immune system. In Evolutionary Computation Journal, 2000.
Extending the Computer Defense Immune System.. - Anchor, Zydallis.. (2002) (3 citations) (Correct)
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Steven Hofmeyr and Stephanie Forrest, "Architecture for an Artificial Immune System," Evolutionary Computation, vol. 7(1), pp. 1289--1296, 1999.
An Artificial Immune System as a Recommender for Web Sites - Morrison, Aickelin (2002) (Correct)
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Hofmeyr, SA and Forrest, S. (2000). Architecture for an Artificial Immune System. Evolutionary Computation 7, pp 45-68.
An Artificial Immune System Approach with Secondary.. - Sarafijanovic, Le Boudec (2003) (1 citation) (Correct)
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S. A Hofmeyr and S. Forrest "Architecture for an Artificial Immune System". Evolutionary Computation 7(1):45-68. 2000.
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