Experimenting Dynamic Clonal Selection (DCS) for Parallel Multiple Interest Topics of User Profile Adaptation in Content Based Filtering

A visual modelling approach and its computational technique were proposed to represent and simulate a kind of immune system, which is comprised of immune cells and immune molecules etc. To study natural immune system and artificial immune system according to information theories and computational methodologies, the hierarchical model of the immune system was proposed, more faithful and suitable for visual simulation than traditional models. The hierarchical immune system basically consisted of innate immune tier, adaptive immune tier and immune cell tier. Thus, the tri-tier model of the immune system was seamless and coherent with the architecture of the artificial immune system, so that the research on the natural immune system and the research on the artificial one could improve and synchronize each other. Though the structure and features of the natural immune system were difficult to measure and test, the tri-tier architecture and qualitative features of the artificial immune system were built, changed and verified. To validate the new approach to visualize and explore the natural immune system, many experiments were tested on the tri-tier artificial immune system. At last, the visual results of the simulations show that the visual modelling approach can provide an effective and better way of understanding the natural immune system.

This paper investigates the approaches of using an analogy of the Human Immune System (HIS) to create an Artificial Immune System (AIS) based Intrusion Detection System (IDS). The two most popular AIS generating algorithms, Negative and Clonal Selection were explored and tested on the NSL-KDD dataset with different sets of features and different numbers of detectors. The experiments show that the Negative Selection Algorithm (NSA) and the Clonal Selection Algorithm (CSA) show a severe scaling issue when handling real network traffic.

In this paper we investigate the use of Artificial Immune Systems' principles to cope with the satisfiability problem. We describe ClonSAT, a new iterative approach for solving the well known Maximum Satisfiability (Max-SAT) problem. This latter has been shown to be NP-hard if the number of variables per clause is greater than 3. The underlying idea is to harness the optimization capabilities of artificial clonal selection algorithm to achieve good quality solutions for MaxSAT problem. To foster the process, a local search has been used. The obtained results are very encouraging and show the feasibility and effectiveness of the proposed hybrid approach.

In mobile ad-hoc networks, nodes act both as terminals and information relays, and participate in a common routing protocol, such as Dynamic Source Routing (DSR). The network is vulnerable to routing misbehavior, due to faulty or malicious nodes. Misbehavior detection systems aim at removing this vulnerability. In this paper we investigate the use of an Artificial Immune System (AIS) to detect node misbehavior in a mobile ad-hoc network using DSR. The system is inspired by the natural immune system of vertebrates. Our goal is to build a system that, like its natural counterpart, automatically learns and detects new misbehavior. We describe the first step of our design; it employs negative selection, an algorithm used by the natural immune system. We define how we map the natural immune system concepts such as self, antigen and antibody to a mobile ad-hoc network, and give the resulting algorithm for misbehavior detection. We implemented the system in the network simulator Glomosim; we present detection results and discuss how the system parameters impact the results. Further steps will extend the design by using an analogy to the innate system, danger signals, costimulation and memory cells.

The rapid integration of cyberphysical systems (CPSs) and the Industrial Internet of Things (IIoT) has transformed industrial environments, driving advancements in autonomy, flexibility, and interconnectedness. However, this evolution introduces significant cybersecurity challenges, including vulnerability to sophisticated cyberattacks targeting critical infrastructure. Artificial immune systems (AISs), inspired by the human immune system′s adaptive and self‐regulating mechanisms, provide a promising approach for intrusion detection in dynamic industrial networks. This study conducted a systematic literature review (SLR) following the PRISMA methodology to investigate the state‐of‐the‐art applications, challenges, and research trends of AIS in industrial intrusion detection systems (IDSs). From an initial pool of 1320 records retrieved from databases like IEEE Xplore, Scopus, and Web of Science, 50 peer‐reviewed studies were selected for in‐depth analysis. The results highlight a growing adoption of AIS algorithms, including the negative selection algorithm (NSA), clonal selection algorithm (CSA), and dendritic cell algorithm (DCA), for anomaly detection, fault tolerance, and distributed control in industrial settings. Despite promising simulation‐based outcomes, challenges such as real‐time scalability, model interpretability, and infrastructure compatibility remain significant barriers. To address these gaps, this study proposes a novel conceptual framework that integrates AIS with multiagent systems (MASs) and blockchain technology, enabling autonomous detection, context‐aware decision‐making, and secure, decentralized data exchange. Future research should prioritize enhancing AIS scalability, reducing algorithmic complexity, and exploring integration with emerging technologies like explainable AI and blockchain to ensure robust, adaptive, and secure industrial systems. These findings offer critical insights for researchers and practitioners aiming to develop intelligent and resilient industrial cybersecurity solutions.

A new fuzzy clustering algorithm based on clonal selection theory from artificial immune systems (AIS), namely, FCSA, is proposed to obtain the optimal clustering result of land cover classification without a priori assumptions on the number of clusters. FCSA can adaptively find the optimal number of clusters and is designed as a two‐layer system: the classification layer and the optimization layer. The classification layer of FCSA, inspired by clonal selection theory, generates the optimal classification result with a fixed cluster number by utilizing the clone, mutation, and selection of immune operators. The optimization layer of FCSA evaluates the optimal solutions according to performance measures for cluster validity and then adjusts the cluster number to output the final optimal cluster number. Two experiments with different types of image evince that FCSA not only finds the optimal number of clusters, but also consistently outperforms the traditional clustering algorithms, such as K‐means and Fuzzy C‐means. Hence, FCSA provides an effective option for performing the task of land cover classification.

Clonal Selection Algorithm (CSA), inspired by the clonal selection theory, has gained much attention and wide applications. In most common forms, the CSAs use a binary representation of variables, and the emulated immune operators, mutation, proliferation, selection, for example, are made to act on it. However, the binary representation often suffers from the so-called Hamming Cliff problem. In order to overcome this problem, a Gray-coded CSA is presented and used to solve optimization problems. The algorithm is applied to numerous bench-mark problems of numerical optimization problems and the computational results show effectiveness of the proposed algorithm.

This paper presents a novel approach to an artificial immune system, which uses the rough set theory to improve its classification ability under uncertainty in data. The proposed approach is mainly based on a negative selection algorithm and is suitable to solve problems where the knowledge of non-self is scarce and noisy. The rough set theory is used to deal with uncertainty in data and to obtain rule sets necessary to specify both self and non-self classes. The proposed artificial immune system is implemented with rough valued lymphocytes, which emulate the approximate binding performed by natural immune systems.

In this report the immune system and its adaptive properties are described and a simple artificial immune system (AIS) model based on the clonal selection theory is presented. This immune system model is demonstrated to be capable of learning the structure of novel antigens, of memory for previously encountered antigens, and of being able to use its memory to respond more efficiently to antigens related to ones it has previously seen (cross-reactivity). The learning, memory and cross-reactivity of the AIS are fruitfully applied to the problem of fuzzy resource identification. Interesting antigen/antibody relationships are also identified.

Danger Theory is presented with particular predominance on analogies in the Artificial Immune Systems world. Artificial Immune System (AIS) is relatively naive paradigm for intelligent computations. The inspiration for AIS is derived from natural Immune System (IS). The idea is that the artificial cells release signals describing their status, e.g., safe signals and danger signals. The various artificial cells use the signals in order to adapt their behavior. 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. In this paper, we look at Danger Theory from the perspective of AIS practitioners and an overview of the Danger Theory is presented with particular emphasis on analogies in the Artificial Immune Systems world.

The acquired immune system is capable of specialising a defence of an organism in response to the its antigenic environment. This complex biological system possess interesting information processing features such as learning, memory, and the ability to generalize. The clonal selection theory is a cornerstone of modern biology in understanding the acquired immune system from the perspective of B-lymphocyte cells and antibody diversity. This work presents a series of computational adaptive systems inspired by features of the biological immune system and the clonal selection theory in particular. Starting with basic clonal operators as principle components, models are presented in increasing complexity from canonical clonal selection models, to discretised architecture models, to finally advanced vaccination, evolution, and ontogenetic models. In addition to providing the basis to an interesting line investigation in the field of artificial immune system, this series of adaptive models presents a hierarchal framework from which existing and future adaptive models inspired by the acquired immune system can be interpreted and related.

The artificial immune system (AIS) is an emerging research field of computational intelligence that is inspired by the principle of biological immune systems. With the adaptive learning ability and a self-organization and robustness nature, the immunology based AIS algorithms have successfully been applied to solve many engineering problems in recent years, such as computer network security analysis, fault detection, and data mining. The real-valued negative selection algorithm (RNSA) is a computational model of the self/non-self discrimination process performed by the T-cells in natural immune systems. In this research, three different real-valued negative selection algorithms (i.e., the detectors with fixed radius, the V-detector with variable radius, and the proliferating detectors) are studied and their applications in data classification and bioinformatics are investigated. A comprehensive study on various parameters that are related with the performance of RNSA, such as the dimensionality of input vectors, the estimation of detector coverage, and most importantly the selection of an appropriate distance metric, is conducted and the figure of merit (FOM) of each algorithm is evaluated using real-world datasets. As a comparison, a model based on artificial neural network is also included to further demonstrate the effectiveness and advantages of RNSA for specific applications.

With the great advancement in wireless technology, number of wireless sensor network applications have increased in which different sensor nodes communicates with each other via sending data among themselves. Query for communication among sensor nodes can be framed in different forms leading into different computation cost. So, the generation and selection of query plan of minimum cost becomes combinatorial in nature which cannot be solved in polynomial time to achieve global optimal cost of data communication. One of the solution to address this problem is nature inspired algorithms. These algorithms have served to number of real life intricate problems. Amidst of all algorithms, bio-inspired algorithms have largely accepted to assist such problems. Artificial immune system (AIS), one of bio-inspired algorithm is inspired from natural human immune system has been explored here. Clonal selection process, one of AIS approach has been discussed in this chapter to generate optimal distributed query plans in distributed wireless sensor network.

Effective distribution outage cause identification can help expedite the restoration procedure and improve the system availability. The fuzzy classification E-algorithm and the immune system inspired classification algorithm, artificial immune recognition system (AIRS), have demonstrated good capabilities in outage cause identification, especially with the existence of imbalanced data. E-algorithm extracts inference rules but is computational demanding; AIRS has the quick searching capability but is lack of rule extraction capability. In this paper, fuzzy artificial immune recognition system (FAIRS) has been proposed to take advantage of the strengths of E-algorithm and AIRS. FAIRS is applied to Duke Energy outage data for cause identification using three major customer interruption causes (tree, animal, and lightning) as prototypes; and FAIRS achieves comparable fault diagnosis performance with two base algorithms while being able to extract linguistic rules to explain the inference within significantly reduced computing time than E-algorithm.

This chapter deals with the problem of fitting curves to data points, a classical optimization problem in Computer-Aided Geometric Design (CAGD). This issue plays an important role in real problems such as construction of car bodies, ship hulls, airplane fuselage, and other free-form objects. A typical example comes from reverse engineering where free-form shapes are extracted from clouds of scanned data points. In this chapter we address this issue by applying a powerful bio-inspired method called Artificial Immune Systems (AIS). The AIS can be understood as a computational methodology based upon metaphors of the biological immune system. As such, there is not one but several AIS algorithms. In this chapter we focus on the clonal selection algorithm (CSA), which explicitly takes into account the affinity maturation of the immune response. This algorithm is applied to fit Bezier curves to given sets of data points. Some illustrative examples show the good performance of our approach.