Augmented Negative Selection Algorithm with Complete Random Subspace Technique for Anomaly Detection

A spam detection model based on negative selection algorithm is proposed in this paper. The artificial immune system creates techniques to solve complex computations, aiming to developing immune based models. This is done by distinguishing self from non-self. Preliminary mathematical analysis will expose the computation and experimental description of the method and how it is applied to spam detection. A new detector model and matching rule model are also generated for effective matching of both self and non-self in other to burst the detector performance of the model. Our unique matching technique use in the negative selection algorithm help the model to overcome the limitation of a normal negative selection algorithm in defining harmfulness of self and non-self. This improves the requirement of the model and satisfactory requirement in terms of true positive and false positive rates. The experimental result confirms that the proposed model is able to establish a better true positive on an unknown spam Models and application in artificial immune system is coming up as an active and attractive field of great diversity. Great source of inspiration to computational model are drawn from the already knowledge of the immune system. Over the past years, rapid expansion of computer network system as change the world. It is essential for an effective computer security system because attacks and criminal intend are increasingly popular in computer network(1) . There are several measure put in place by many companies in the area of creating anti spam software based on signatures and have a very efficient performance in detecting spam fast. Though, new variation of spam and unknown spam are very difficult to detect by this software. The traditional way of detecting spam based on signature is no more efficient for today systems. Recent years, researchers are interested in the field of immune system in achieving computer security. Immune theory was first applied to computer abnormality detection by Forrest et al. After then, several proposition in the area of immune system as made tremendous success. Negative selection algorithm, while not reacting to the self cells uses the immune system capability to detect unknown antigens. Its mechanism protects body against self reactive lymphocytes. Receptors are made through a pseudo-random genetic re-arrangement process during the generation of T-cells (2); they then undergo a censoring process in the thymus called the negative selection. In this process T-cells that do not bind to self-proteins are destroyed. Therefore, immunological function and protection of the body against foreign antigens is possible through circulation of matured T-cells. Artificial negative selection algorithm was proposed to follow that pattern; generating random detectors and then discard those that match self samples. It is a spam detection in which training data from only one of the classes are available. With these illustrations, the traditional negative selection algorithm believes that all self are not dangerous and all non- self

The Negative Selection (NS) algorithm is the first algorithm to come from the study of natural immune systems, and is the most widely known and applied algorithm in the field. It has been used to build intrusion detection systems along with many other security-related tasks. However, it has not been possible to use the Negative Selection algorithm on many real-world scenarios. The present research shows an optimization of the negative selection algorithm to make its execution faster. The optimized algorithm remains functionally the same, providing the same results as the unoptimized algorithm. Details are given about the optimization scheme used and the optimized negative selection algorithm is tested on the UCI Breast Cancer data set. The performance of the unoptimized negative selection algorithm is compared to the performance of the algorithm with the proposed optimization. Three claims about the function of the optimized negative selection algorithm are made and tested with four experiments. The results of the experiments are used to demonstrate that the algorithm is faster and does not change the negative selection algorithm or lower its accuracy. Although there has been research into the optimization of the Negative Selection algorithm, this work will only apply to hyper-sphere detectors, which has not been done before.

The negative selection algorithm (NSA) is one of the important methods in the field of immunological computation (or artificial immune systems). Over the years, some progress was made that turns this algorithm (NSA) into an efficient approach to solve problems in different domain. This review takes into account these signs of progress during the last decade and categorizes those based on different characteristics and performances. Our study shows that NSA’s evolution can be labeled in four ways highlighting the most notable NSA variations and their limitations in different application domains. We also present alternative approaches to NSA for comparison and analysis. It is evident that NSA performs better for nonlinear representation than most of the other methods, and it can outperform neural-based models in computation time. We summarize NSA’s development and highlight challenges in NSA research in comparison with other similar models.

Damage analysis in composite materials using a hybrid artificial immune system

Application of the feature-detection rule to the Negative Selection Algorithm

The negative selection algorithm (NSA) is one of models in artificial immune systems. In this paper, two issues existed in traditional NSAs are described. Inspired by immune suppression mechanism, a novel framework of NSA that combining boundary selves and detectors to perform detection is proposed. By introducing the framework into V-detector algorithm, the improved algorithm is implemented and applied with synthetic data and real data. The experiment results show that the new algorithm based on immune suppression ensures better detection performance with fewer detectors.

A negative selection algorithm with online adaptive learning under small samples for anomaly detection

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.

Two main techniques used to build Intrusion Detection System (IDS), abnormal detection and misuse detection, have their own strengths and limitations respectively. It implies that an effective IDS should employ an abnormal detector and a misuse detector in parallel. While the human immune system (HIS) distinguishes previously known and unknown pathogens from human body via its own passive layers which are called natural immune systems and adaptive immune systems. Natural immune system is akin to the misuse detector of IDS and adaptive immune system is similar to the abnormal detector. Inspired from adaptive immune system, Negative Selection Algorithm (NSA) is applied to abnormal detection. But NSA can not generate dynamic detectors changed with nonselves. Inspired from affinity maturation, Dynamic Negative Selection Algorithm Based on Affinity Maturation (DNSA-AM), where NSA is used to delete detectors which detect any selves, is proposed and can be adapted to the change of nonselves. Match rule with the match threshold (r) is one of the most important components in NSA and DNSA-AM. ‘r’ is related to selves and must be set at first. So DNSA-AM can not be adapted to the change of selves.

DENSA: An effective negative selection algorithm with flexible boundaries for self-space and dynamic number of detectors

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.

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.

The traditional negative selection algorithm with a randomly generated hypersphere detector is unable to satisfy the development needs of continuous learning due to the inherent defects of the detector. This paper proposes a novel negative selection algorithm for hyper-rectangle detectors that overcomes the shortcomings of randomly generated hyper-sphere detectors and lays the foundation for a negative selection algorithm with continuous learning capability. It uses self-sample clusters of equal-sized hypercubes instead of self-samples for training. The hyper-rectangle detectors are generated by cutting the nonself-space along the boundary of the self-sample clusters. The state space is covered without overlapping each other by self-sample clusters and detectors. The anomaly detection performance of the proposed method was demonstrated using Iris data, vowel recognition data (Vowel), and Wisconsin Breast Cancer (BCW) data. The experimental results show that the proposed method outperforms other artificial immune algorithms and clustering algorithms under the same parameter conditions.

In engineering application, the characteristics of the control system are entirely determined by the system controller once the controlled object has been chosen. Improving the traditional controller or constructing the new controller is an unfading study field of control theory and application. The control system is greatly enriched and developed by this way. As a complicated self-adaptable system, the biological immune system can effectively and smoothly stand against antigens and viruses intruded into organism. It is possible to improve the self-learning, adaptive and robustness capability of the control system through embedded an artificial immune controller in control system. Based on the biological immune mechanism and artificial immune model, this chapter attempts to study the immune controller design and application in traditional control system..First, a kind of artificial immune controller is proposed based on the T-B cells immunity. The boundedness and the stability of SISO control systems, which constructed by the artificial immune controller, are proved by the little gain theorem. A general controller structure frame based on the T-B cells immunity is proposed, which includes the same kind of controller proposed previously. The validity of this artificial immune controller is verified by simulation. Second, a new type of artificial immune controllers is constructed according to a simple double-cell immune dynamics model. The non-error characteristic of SISO control systems, which constructed by the artificial immune controller, is proved by the nonlinear theory in this chapter. The I/O stability and no-error characteristic of the system are verified by simulations, which show that the kind of artificial immune system have good anti-lag capability. Third, the Varela immune network model has been improved based on which an artificial immune system is proposed. The odd linearization method of the non-linear system is used to prove the stability and non-error characteristic of the SISO system constructed by the artificial immune control system. Its I/O stability, non-error characteristic and strong anti-lag capability are also verified by simulation. Finally, based on the comparison of the three kinds of immune controllers, a general structure of the artificial immune controller is proposed. The further study on this field is indicated in this chapter lastly.

In order to solve the problem that the traditional signature-based detection technology cannot effectively detect unknown malware, we propose in this study a smartphone malware detection model (SP-MDM) based on artificial immune system, in which static malware analysis and dynamic malware analysis techniques are combined, and antigens are generated by encoding the characteristics extracted from the malware. Based on negative selection algorithm, the mature detectors are generated. By introducing clonal selection algorithm, the detectors with higher affinity are selected to undergo a proliferation and somatic hyper-mutation process, so that more excellent detector offspring can be generated. Experimental result shows that the detection model has a higher detection rate for unknown smartphone malware, and better detection performance can be achieved by increasing the clone generation.