Artificial Immune Systems at Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland.

This is rather outdated version

Some (old) papers and reports

General Description
Clustering
Optimization in Dynamic Environments
Multimodal Optimization
Receptors Generation
Idiotypic Networks
Papers on AIS in Adversarial Machine Learning

Demos

DAAISY (Data Analysis with Artificial Immune SYstem)

General Description

In English:: A brief introduction to AIS, S. T. Wierzchoń
In Polish:: Rozdział 1 - Wprowadzenie, S. T. Wierzchoń, "Sztuczne systemy immunologiczne. Teoria i zastosowania", Akademicka Oficyna Wydawnicza EXIT, Warszawa 2001; Sztuczne systemy immunologiczne - zastosowanie w optymalizacji kombinatorycznej, Anna Świtalska (na podstawie pracy dyplomowej, Akademia Podlaska, Siedlce, 2005)

Clustering

In English:: Results of experiments from paper: S. T. Wierzchoń, U. Kużelewska, "Adaptive clusters formation in an Artificial Immune System", WAE'02 Warsztaty Naukowe: Algorytmy Ewolucyjne i Optymalizacja Globalna, Kraków, 23-24 wrzesnia 2002, pp. 131-138 (pdf file); S.T. Wierzchoń, U. Kużelewska, "Stable clusters formation in an artificial immune system", ICARIS'02 : 1st International Conference on Artificial Immune Systems, University of Kent at Canterbury, UK September 9th-11th, 2002, pp. 68-75 (pdf file)

Optimization in Dynamic Environments

In English:: Results of experiments from paper: K. Trojanowski, S. T. Wierzchoń, "Memory Management in Artificial Immune System", ICNNSC'02: 6th International Conference on Neural Networks and Soft Computing, Zakopane, 11-15 June, 2002.; Results of experiments from paper: K. Trojanowski, S. T. Wierzchoń, "Immune Memory Control in Artificial Immune System", WAE'02 Warsztaty Naukowe: Algorytmy Ewolucyjne i Optymalizacja Globalna, Kraków, 23-24 wrzesnia 2002, pp. 111-118 (pdf file)
In Polish:: S.T. Wierzchoń,"Immune algorithms in action: Optimization of nonstationary functions", SzI-16'2001: XII Ogólnopolskie Konwersatorium nt. Sztuczna Inteligencja - nowe wyzwania (badania - zastosowania - rozwój), Siedlce-Warszawa, 28 listopada 2001, str. 97-106 (pdf file)

Multimodal Optimization

In English:: S.T. Wierzchoń "Function optimization by the immune metaphor. Task Quarterly, vol. 6, no. 3 (2002), pp. 493-508 (pdf file)

Receptors generation

In English:: S.T. Wierzchoń; "Deriving concise description of non-self patterns in an artificial immune system", L. C. Jain, J. Kacprzyk, eds, New Learning Paradigm in Soft Computing, Physica-Verlag 2002, ISBN 3-7908-1436-9, pp. 438-458 (pdf file); S.T. Wierzchoń; "Discriminative power of the receptors activated by k-contiguous bits rule", (Invited paper) Journal of Computer Science and Technology. Special Issue on Research Computer Science, vol. 1, no. 3, 2000, pp. 1-13 (ps file); S.T. Wierzchoń; "Generating optimal repertoire of antibody strings in an artificial immune system", M.A.Kłopotek, M.Michalewicz, S.T.Wierzchoń, eds: Intelligent Information Systems. Advances in Soft Computing Series of Physica-Verlag/Springer Verlag, Heidelberg/ New York 2000, pp. 119-133 (ps file)

Idiotypic Networks

In English:: K. Trojanowski, M. Sasin; "The Idiotypic Network with Binary Patterns Matching", H. Bersini, J. Carneiro, eds, Artificial Immune Systems, ICARIS 2006, LNCS 4163, pp. 95-108, 2006, ISBN 3-540-37749-2 (conference presentation - pdf file)

AIS and Adversarial Machine Learning

AIS:: S.T. Wierzchon, Brief intro to AIS; X. Xie et al. Immunology for AI; Raczej stare, ale mozna spojrzec Immunology as a Metaphor for Adaptive and Distributed Information Processing (2007); Biologically Inspired Mechanisms for Adversarial Robustness (2020); Artificial Immune System LARK; Understanding immunity: an alternative framework beyond defense and strength BIOLOGIA czysta
Adversarial Learning:: Coursera: What Is Adversarial Machine Learning?; What Is an Adversarial AI Attack?; How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses (2024); Adversarial Examples in Modern Machine Learning: A Review (2019); Adversarial Attacks and Defences: A Survey (2018) 1030 cytowan; A survey on adversarial attacks and defences (2020); Adversarial Training Methods for Deep Learning: A Systematic Review (2022); A Systematic Review of Adversarial Machine Learning Attacks, Defensive Controls, and Technologies, 2024; Adversarial Attack and Defense: A Survey (2022); Classification of Adversarial Attacks Using Ensemble Clustering Approach, 2022; Is Data Clustering in Adversarial Settings Secure?, 2018
linki od p. Lidii Moryc:: On Adaptive Attacks to Adversarial Example Defenses, 2020; Towards Evaluating the Robustness of Neural Networks, 2016; Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples, 2016; Explaining and Harnessing Adversarial Examples, 2014
Immune-based apprtoaches to AL:: Artificial Immune System of Secure Face Recognition Against Adversarial Attacks, 2024, code; Defense Mechanisms Against Adversarial Attacks: Strengthening AI Security in Cybersecurity Applications, 2025; Advancements in Defense Mechanisms against Adversarial Attacks in Computer Vision, 2024; Defending Against Adversarial Attacks in Artificial Intelligence Technologies, 2025; Defending local poisoning attacks in multi-party learning via immune system, 2022; Adversarial attacks on medical machine learning, 2019; Modeling Biological Immunity to Adversarial Example (2020) - models of perception are discussed; Adopting Immunological Metaphors in Cybersecurity Applications (2022) A highly philosophical thesis
Books:: Digital Immune System: Principles and Practices, 2025; Generative Adversarial Learning: Architectures and Applications To chyba mozna sciagnac na uczelni; book on adversarial learnin.