Showing papers on "Artificial immune system published in 2021"

Improved Artificial Immune System Algorithm for Type-2 Fuzzy Flexible Job Shop Scheduling Problem

Abstract: In practical applications, particularly in flexible manufacturing systems, there is a high level of uncertainty. A type-2 fuzzy logic system (T2FS) has several parameters and an enhanced ability to handle high levels of uncertainty. This article proposes an improved artificial immune system (IAIS) algorithm to solve a special case of the flexible job shop scheduling problem (FJSP), where the processing time of each job is a nonsymmetric triangular interval T2FS (IT2FS) value. First, a novel affinity calculation method considering the IT2FS values is developed. Then, four problem-specific initialization heuristics are designed to enhance both quality and diversity. To enhance the exploitation abilities, six local search approaches are conducted for the routing and scheduling vectors, respectively. Next, a simulated annealing method is embedded to accept antibodies with low affinity, which can enhance the exploration abilities of the algorithm. Moreover, a novel population diversity heuristic is presented to eliminate antibodies with high crowding values. Five efficient algorithms are selected for a detailed comparison, and the simulation results demonstrate that the proposed IAIS algorithm is effective for IT2FS FJSPs.

Anomaly process detection using negative selection algorithm and classification techniques

Abstract: Artificial immune system is derived from the biological immune system. This system is an important method for generating detectors that include self-adaption, self- regulation and self-learning which have self/non-self-detection features. This method is used in anomaly process detection where the anomaly is non-self in the system. We present a new combining technique for anomaly process detection. This combined technique is a unification of both negative selection and classification algorithm. The main aim of the proposed techniques is to increase the accuracy in this system while decreasing its training time. In this research, CICIDS 2017 and NSL-KDD dataset with different sets of features and the same number of detectors are used. This paper presents a framework for detecting anomaly processes on a host base computer system which is established on the artificial immune system. We evaluate our technique using machine learning algorithms such as: logistic regression, random forest, decision tree and K-neighbors. Moreover, we use WEKA tool classification to perform a correlation based feature selection on the dataset.

Data-driven network layer security detection model and simulation for the Internet of Things based on an artificial immune system

Abstract: Traditional network security detection models are trained offline using attack samples of known types. Although such models have high detection rates for known attack types, they cannot identify new attack types in the network layer. At present, these detection systems have the disadvantages of slow system construction and a high cost of model updating. Facing the increasing expansion of networks and endless attacks, these detection systems lack self-adaptability and expansibility, so it is difficult to detect complex and changeable attack events in networks. In this paper, the integrated use of immunology theory, complex adaptive system theory, and computational experiment technology is proposed to develop an Internet network layer security detection model based on an artificial immune system as an improvement over existing models of Internet network layer security. On the basis of testing knowledge, when a new type of attack is encountered, the online detection and learning process enables the dynamic extension of the network security detection model. Experimental calculations and an example analysis are presented to verify the scientific validity and feasibility of the model.

A review of artificial intelligence applications in apparel industry

Abstract: Nowadays apparel industries face ever-increasing global competition and unpredictable variations in demand. These pressures force manufacturers to consistently improve the efficiency of their manuf...

Artificial Immune System for Fault Detection and Classification of Semiconductor Equipment

Abstract: Semiconductor manufacturing comprises hundreds of consecutive unit processes. A single misprocess could jeopardize the whole manufacturing process. In current manufacturing environments, data monitoring of equipment condition, wafer metrology, and inspection, etc., are used to probe any anomaly during the manufacturing process that could affect the final chip performance and quality. The purpose of investigation is fault detection and classification (FDC). Various methods, such as statistical or data mining methods with machine learning algorithms, have been employed for FDC. In this paper, we propose an artificial immune system (AIS), which is a biologically inspired computing algorithm, for FDC regarding semiconductor equipment. Process shifts caused by parts and modules aging over time are main processes of failure cause. We employ state variable identification (SVID) data, which contain current equipment operating condition, and optical emission spectroscopy (OES) data, which represent plasma process information obtained from faulty process scenario with intentional modification of the gas flow rate in a semiconductor fabrication process. We achieved a modeling prediction accuracy of modeling of 94.69% with selected SVID and OES and an accuracy of 93.68% with OES data alone. To conclude, the possibility of using an AIS in the field of semiconductor process decision making is proposed.

Continual learning classification method with constant-sized memory cells based on the artificial immune system

Abstract: Most classification methods cannot further improve their classification performance by learning the testing data during the testing stage, for lacking continual learning ability. A new classification method, continual learning classification method with constant-sized memory cells based on the artificial immune system (C-CLCM), is proposed. It is inspired by the continual learning mechanism of the biological immune system. C-CLCM gradually enhances its classification performance by continually learning the testing data especially the new types of labeled data and new types of unlabeled data during the testing stage. At the same moment, it updates the existing memory cells and culture new types of memory cells. C-CLCM degenerates into a common supervised learning classification method under certain conditions. To assess its performance and possible advantages, the experiments on well-known datasets from the UCI repository were performed. Results show that C-CLCM has better classification performance when it degenerates into a common supervised learning classification method. It outperforms the other methods when the training data do not cover all types. The less type of training, the more advantages it has.

A cognitive mechanism for mitigating DDoS attacks using the artificial immune system in a cloud environment

Abstract: Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks can largely damage the availability of the cloud services and can be effectively initiated by utilizing different tools, prompting financial harm or influencing the reputation. Consequently, there is a requirement for a more grounded and general approach to block these attacks. This paper proposes the use of artificial immune systems to alleviate DDoS attacks in cloud computing by identifying the most potential features of the attack. This methodology is capable of detecting threats and responding according to the behavior of the biological resistance mechanism in human beings. It is carried out by emulating the various immune reactions and the construction of the intrusion detection system. For the assessment, experiments with public domain datasets (KDD cup 99) were implemented. Based on broad theoretical and performance analysis, the proposed system is capable to identify the anomalous entries with high detection accuracy and low false alarm rate.

A novel sophisticated hybrid method for intrusion detection using the artificial immune system

Abstract: Artificial immune system (AIS) as a good prototype for developing Machine Learning (ML) is a promising candidate to design Intrusion Detection System (IDS). Two of its prevalent paradigms, the negative selection, and the danger theory, inspired by immunity responses of the powerful human immune system (HIS) are being widely used in this case. In this paper, we proposed a novel sophisticated hybrid method including two defensive lines by using two aforesaid mechanisms. In the proposed method, decentralized cooperation of dendritic cells together with mature detectors acts as a stimulator to generate efficient and accurate detectors and retain memory in the long-term that meant security control via ensuring immunity. Simulating such a system was applicable only by establishing artificial life cycles in the MATLAB environment. Experimental consequences demonstrate that the present schema gradually ameliorates its overall performance during the incipient stages of life cycles and also it is more capable than the previous ones found in the literature.

Automatic adaptation of hypermutation rates for multimodal optimisation

Abstract: Previous work has shown that in Artificial Immune Systems (AIS) the best static mutation rates to escape local optima with the ageing operator are far from the optimal ones to do so via large hyper-mutations and vice-versa. In this paper we propose an AIS that automatically adapts the mutation rate during the run to make good use of both operators. We perform rigorous time complexity analyses for standard multimodal benchmark functions with significant characteristics and prove that our proposed algorithm can learn to adapt the mutation rate appropriately such that both ageing and hypermutation are effective when they are most useful for escaping local optima. In particular, the algorithm provably adapts the mutation rate such that it is efficient for the problems where either operator has been proven to be effective in the literature.

Fast Immune System-Inspired Hypermutation Operators for Combinatorial Optimization

Abstract: Various studies have shown that immune system-inspired hypermutation operators can allow artificial immune systems (AIS) to be very efficient at escaping local optima of multimodal optimization problems. However, this efficiency comes at the expense of considerably slower runtimes during the exploitation phase compared to the standard evolutionary algorithms. We propose modifications to the traditional hypermutations with mutation potential (HMP) that allow them to be efficient at exploitation, as well as maintaining their effective explorative characteristics. Rather than deterministically evaluating fitness after each bit-flip of a hypermutation, we sample the fitness function stochastically with a “parabolic” distribution. This allows the stop at the first constructive mutation (FCM) variant of HMP to reduce the linear amount of wasted function evaluations when no improvement is found to a constant. The stochastic distribution also allows the removal of the FCM mechanism altogether as originally desired in the design of the HMP operators. We rigorously prove the effectiveness of the proposed operators for all the benchmark functions, where the performance of HMP is rigorously understood in the literature. We validate the gained insights to show linear speed-ups for the identification of high-quality approximate solutions to classical NP-Hard problems from combinatorial optimization. We then show the superiority of the HMP operators to the traditional ones in an analysis of the complete standard Opt-IA AIS, where the stochastic evaluation scheme allows HMP and aging operators to work in harmony. Through a comparative performance study of other “fast mutation” operators from the literature, we conclude that a power-law distribution for the parabolic evaluation scheme is the best compromise in black-box scenarios, where little problem knowledge is available.

A fast detector generation algorithm for negative selection

Abstract: Inspired by biological immune systems, the field of artificial immune system (AIS), particularly the negative selection algorithm (NSA), has been proved effective in solving computational problems. However in practical applications, NSA still encounter challenges, such as noise in training data leading to imprecise classifications, the lack of sufficient samples for detector maturation, and potential overlap among detectors. Address to these problems, we propose a novel hybrid detector generation algorithm based on fast clustering for artificial immune systems, namely FCAIS-HD. It primarily consists of two stages: first it utilizes a fast clustering algorithm to generate self-samples to decrease the effect of noise in the data. FCAIS-HD replaces the self-samples with a small number of self-detectors to reduce the time of generating non-self detectors; in the second stage, it utilizes a novel variable-radius non-self detector generation algorithm to generate a small number of non-self detectors with small overlap rates. Finally, both self-detectors as well as non-self detectors are used to implement hybrid detection (HD). Comprehensive experiments are conducted on both simulation and real world data sets to compare classification performance with baselines. The results demonstrate that FCAIS-HD outperforms other algorithms with well excluded low-level noise interference, higher rate of detection and less parameter sensitivity. Additionally, experiments are carried out on some real word data sets demonstrate that FCAIS-HD also performs well in high-dimensional data sets.

Artificial immune system training algorithm for a dendritic neuron model

Abstract: Dendritic neuron model (DNM), which is a single neuron model with a plastic structure, has been applied to resolve various complicated problems. However, its main learning algorithm, namely the back-propagation (BP) algorithm, suffers from several shortages, such as slow convergence rate, being easy to fall into local minimum and over-fitting problems. That largely limits the performances of the DNM. To address this issue, another bio-inspired learning paradigm, namely the artificial immune system (AIS) is employed to train the weights and thresholds of the DNM, which is termed AISDNM. These two methods have advantages on different issues. Due to the powerful global search capability of the AIS, it is considered to be efficient in improving the performance of the DNM. To evaluate the performance of AISDNM, eight classification datasets and eight prediction problems are adopted in our experiments. The experimental results and statistical analysis confirm that the AISDNM can exhibit superior performance in terms of accuracy and convergence speed when compared with the multilayer perceptron (MLP), decision tree (DT), the support vector machine with the linear kernel (SVM-l), the support vector machine with the radial basis function kernel (SVM-r), the support vector machine with the polynomial kernel (SVM-p) and the conventional DNM. It can be concluded that the reasonable combination of two different bio-inspired learning paradigms is efficient. Furthermore, for the classification problems, empirical evidence also validates the AISDNM can delete superfluous synapses and dendrites to simplify its neural structure, then transform the simplified structure into a logic circuit classifier (LCC) which is suitable for hardware implementation. The process does not sacrifice accuracy but significantly improves the classification speed. Based on these results, both the AISDNM and the LCC can be regarded as effective machine learning techniques to solve practical problems.

Partitioned artificial immune system for detection and identification of autonomous flight vehicle abnormal conditions

Abstract: An artificial immune system (AIS) for the detection and identification of abnormal operational conditions affecting an unmanned air vehicle (UAV) is developed using the partition of the universe approach. The performance of the proposed methodology is assessed through simulation within the West Virginia University (WVU) unmanned aerial system (UAS) simulation environment.,An AIS is designed and generated for a fixed wing UAV using data from the WVU UAS simulator. A novel partition of the universe approach augmented with the hierarchical multiself strategy is used to define the self, within the AIS paradigm. Several 2-dimensional and 3-dimensional commanded trajectories are simulated under normal and abnormal conditions affecting actuators and sensors. Data recorded are used to build the AIS and develop an abnormal condition detection and identification scheme for the two categories of subsystems. The performance of the methodology is evaluated in terms of detection and identification rates, false alarms and decision times.,The proposed methodology for UAV abnormal condition detection and identification has the potential to support a comprehensive and integrated solution to the problem of aircraft subsystem health management. The novel partition of the universe approach has been proven to be a promising alternative to the previously investigated clustering methods by providing similar or better performance for the cases investigated.,The promising results obtained within this research effort motivate further investigation and extension of the proposed methodology toward a complete system health management process, including abnormal condition evaluation and accommodation.,The use of the partition of the universe approach for AIS generation may potentially represent a valuable alternative to current clustering methods within the AIS paradigm. It can facilitate a simpler and faster implementation of abnormal condition detection and identification schemes.,In this paper, a novel method for AIS generation, the partition of the universe approach, is formulated and applied for the first time for the development of abnormal condition detection and identification schemes for UAVs. This approach is computationally less expensive and mitigates some of the issues related to the typical clustering approaches. The implementation of the proposed approach can potentially enhance the robustness of UAS for safety purposes.

FDIA System for Sensors of the Aero-Engine Control System Based on the Immune Fusion Kalman Filter

Abstract: The Kalman filter plays an important role in the field of aero-engine control system fault diagnosis. However, the design of the Kalman filter bank is complex, the structure is fixed, and the parameter estimation accuracy in the non-Gaussian environment is low. In this study, a new filtering method, immune fusion Kalman filter, was proposed based on the artificial immune system (AIS) theory and the Kalman filter algorithm. The proposed method was used to establish the fault diagnosis, isolation, and accommodation (FDIA) system for sensors of the aero-engine control system. Through a filtering calculation, the FDIA system reconstructs the measured parameters of the faulty sensor to ensure the reliable operation of the aero engine. The AIS antibody library based on single sensor fault was constructed, and with feature combination and library update, the FDIA system can reconstruct the measured values of multiple sensors. The evaluation of the FDIA system performance is based on the Monte Carlo method. Both steady and transient simulation experiments show that, under the non-Gaussian environment, the diagnosis and isolation accuracy of the immune fusion Kalman filter is above 95%, much higher than that of the Kalman filter bank, and compared with the Kalman particle filter, the reconstruction value is smoother, more accurate, and less affected by noise.

Developing a new integrated artificial immune system and fuzzy non-discretionary DEA approach

Abstract: As a useful performance evaluation and decision-making tool, data envelopment analysis (DEA) has been proven to be an excellent data-oriented efficiency analysis method when there are multiple inputs and outputs. However, when working with large datasets, DEA requires more time to solve and calculate the optimal values for each decision-making unit (DMU). To close this methodological gap, this study proposes a new integrated fuzzy nondiscretionary DEA (FNDEA) model and artificial immune system (AIS) to predict and find the optimal values of DMUs. In so doing, we first modify an FNDEA model to classify the set of all DMUs into efficient and inefficient, and immune system (AIS) is used to predict and find the optimal values of DMUs. Then, a modified fuzzy nondiscretionary additive DEA model, which is designed as a middle chain, is used for sensitivity analysis of inefficient DMUs to determine their target outputs, which are called antigens. Finally, we try to reduce the distance between these two steps in order to predict the optimal values (with nearest distance to antigens) of inefficient DMUs and improve their efficiency by using a combined AIS and FNDEA model called the FNDEA–AIS approach. To illustrate the advantages of the proposed FNDEA–AIS approach, a dataset from 24 Iranian forest management units is collected; the results indicated that our new FNDEA–AIS approach (in comparison with other well-established performance prediction techniques) exhibits better convergent validity and high correlation with low error rate to predict optimal values of inefficient DMUs. The main conclusion is that our new applied method, developed for the first time herein, provides a suitable improvement over previously developed methods, such as artificial neural networks (ANNs), to detect inefficiency and to improve the overall performance when analyzing different types of real-life problems.

Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches

Abstract: The fast growth of the Internet of Things (IoT) and its diverse applications increase the risk of cyberattacks, one type of which is malware attacks. Due to the IoT devices’ different capabilities and the dynamic and ever-evolving environment, applying complex security measures is challenging, and applying only basic security standards is risky. Artificial Immune Systems (AIS) are intrusion-detecting algorithms inspired by the human body’s adaptive immune system techniques. Most of these algorithms imitate the human’s body B-cell and T-cell defensive mechanisms. They are lightweight, adaptive, and able to detect malware attacks without prior knowledge. In this work, we review the recent advances in employing AIS for the improved detection of malware in IoT networks. We present a critical analysis that highlights the limitations of the state-of-the-art in AIS research and offer insights into promising new research directions.

ISM-AC: an immune security model based on alert correlation and software-defined networking

Abstract: Anomaly-based detection techniques have a high number of false positives, which degrades the detection performance. To address this issue, we propose a distributed intrusion detection system, named ISM-AC, based on anomaly detection using artificial immune system and attack graph correlation. To analyze network traffic, we use negative selection, clonal selection, and immune network algorithms to implement an agent-based detection system. ISM-AC leverages the programmability of software-defined networking to reduce the false positive rate. Our findings show that ISM-AC achieves better detection performance for denial of service, user to root, remote to local, and probe attack classes. Alert correlation plays a key role in this achievement.

An Artificial Immune System for Black Box Test Case Selection.

Abstract: Testing is a crucial part of the development of a new product. For software validation a transformation from manual to automated tests can be observed which enables companies to implement large numbers of test cases. However, during testing situations may occur where it is not feasible to run all tests due to time constraints. Hence a set of critical test cases must be compiled which usually fulfills several criteria. Within this work we focus on criteria that are feasible for black box testing such as system tests. We adapt an existing artificial immune system for our use case and evaluate our method in a series of experiments using industrial datasets. We compare our approach with several other test selection methods where our algorithm shows superior performance.

Decision tree and artificial immune systems for stroke prediction in imbalanced data

Abstract: Although cerebral stroke is a important public worldwide health problem with more than 43 million global cases reported recently, more than 90% of metabolic risk factors are controllable. Therefore, early treatment can take advantage of a fast and low-cost diagnosis to minimize the disease’s sequels. The use Machine Learning (ML) techniques can provide an early and low-cost diagnosis. However, the performance of these techniques is reduced in problems of prediction of rare events and with class imbalance. We proposed Machine learning approach to cerebral stroke prediction based on Artificial Immune Systems (AIS) and Decision Trees (DT) induced via Genetic Programming (GP). In general, the approaches for stroke prediction presented in the literature do not allow the development of models considered interpretable; our approach, on the other hand, uses a simplification operator that reduces the complexity of the induced trees to increase their interpretability. We evaluated our approach on a highly imbalanced data set with only 1.89% stroke cases and used AIS combined with One Sided Selection (OSS) to create a new balanced data set. This new data set is used by the GP to evolve a population of DTs, and, at the end of this process, the best tree is used to classify new instances. Two experiments are used to test the proposed approach. In the first experiment, our approach achieved, in terms of sensitivity and specificity, are 70% and 78%, respectively, indicating its competitiveness with the state-of-the-art technique. The second experiment evaluates the proposed simplification mechanism in creating rules that can be interpreted by humans. The proposed approach can effectively increase sensitivity and specificity while maintaining accurate prediction using interpretable models, indicating its potential to be clinically used in stroke diagnosis.

Continual learning classification method for time-varying data space based on artificial immune system

Abstract: Classification methods play an important role in many fields. However, they cannot effectively classify the samples from sample spaces that are varying with time, for they lack continual learning ability. A continual learning classification method for time-varying data space based on artificial immune system, CLCMTVD, is proposed. It is inspired by the intelligent mechanism that memory cells of the biological immune system can recognize and eliminate previous invaders when they attack again very fast and more efficiently, and these memory cells can evolve with the evolution of previous invaders. Memory cells were continuously updated by learning testing data during the testing stage, thus realize the self-improvement of classification performance. CLCMTVD changes a linearly inseparable spatial problem into many classification problems of several different times, and it degenerates into a common supervised learning classification method when all data independent of time. To assess the performance and possible advantages of CLCMTVD, the experiments on well-known datasets from UCI repository, synthetic data and XJTU-SY rolling element bearing accelerated life test datasets were performed. Results show that CLCMTVD has better classification performance for time-invariant data, and outperforms the other methods for time-varying data space.

Diagnostics of industrial equipment and faults prediction based on modified algorithms of artificial immune systems

Abstract: Nowadays, industrial enterprises are equipped with sophisticated equipment, diagnostics and prediction of the state of which is an urgent task. The article presents the developed system for diagnostics of industrial equipment based on the methodology for analyzing failure modes, their influence and the degree of AMDEC criticality (l'Analyse des Modes de Defaillances, de leurs Effets et de leur Criticite), as well as modified algorithms of artificial immune systems (AIS) on the example of real production data of TengizChevroil enterprise. The classical AMDEC model is improved by assessing the degree of criticality of equipment failures using the developed modified GWO-AIS and FPA-AIS algorithms based on gray wolf optimization and flower pollination methods. The proposed diagnostic system allows to reduce the financial risks of an enterprise associated with equipment faults by predicting possible failures, the possibility of planning maintenance, reducing the time for equipment repair and increasing the reliability of production.

Multisource Neighborhood Immune Detector Adaptive Model for Anomaly Detection

Abstract: The artificial immune system (AIS) is one of the important branches of artificial intelligence technology, and it is widely used in many fields. The detector set is the core knowledge set, and the AIS application effects are mainly determined by the generation, evolution, and detection of the detectors. Presently, the problem space (shape-space) of AIS mainly applied real-valued representation. But the real-valued detectors have some problems that have not been solved well, such as slow convergence speed of generation, holes in the nonself region, detector overlapping redundancy, dimension curse, etc., which lead to the unsatisfactory detection effects. Moreover, artificial immune anomaly detection is a dynamic adaptive model, needs to be evolved adaptively with the detection environments. Without better adaptive modeling, these problems mentioned before will get worse. In view of this, this article proposes a multisource immune detector adaptive model in neighborhood shape-space and applies it to anomaly detection: based on random, chaotic map and DNA genetic algorithm (DNA-GA), multisource neighborhood negative selection algorithm (MSNNSA), multisource neighborhood immune detector generation algorithm (MS-NIDGA), and neighborhood immune anomaly detection algorithm (NIADA) are proposed, so that the generation and detection of immune detectors can be improved efficiently; introducing immune adaptive and feedback mechanism, multisource neighborhood immune detector adaptive model (MS-NIDAM) is built, so that the detectors can be adaptively evolved in a more targeted search domain, and keep better distribution to the nonself region in real time, so as to solve various problems existing in the real-valued shape-space under dynamic environment mentioned before and improve the overall detection performances. The experimental results show that MS-NIDAM can improve the detector generation/evolution efficiency, keep the up-to-date understanding of the changing environment, so as to obtain better overall detection performances and stability than other comparative methods.

Lesions Detection of Multiple Sclerosis in 3D Brian MR Images by Using Artificial Immune Systems and Support Vector Machines

Abstract: This paper presents a segmentation method to detect multiple sclerosis (MS) lesions in brain MRI based on the artificial immune systems (AIS) and a support vector machines (SVM). In the first step, AIS is used to segment the three main brain tissues white matter, gray matter, and cerebrospinal fluid. Then the features were extracted and SVM is applied to detect the multiple sclerosis lesions based on SMO training algorithm. The experiments conducted on 3D brain MR images produce satisfying results. KeywoRDS 3D Brain MRI, AIS, Detection, Lesions, Multiple Sclerosis, Segmentation, SMO, SVM INTRoDUCTIoN Multiple sclerosis is an autoimmune chronic disease of the central nervous system especially the brain, the optic nerves and the spinal cord. The symptoms are very variable, numbness of a limb, blurred vision, loss of equilibrium...etc (Xavier et al, 2012). Magnetic resonance (MR) imaging can accurately visualize and locate plaques in both the brain and spinal cord. Depending on the sequences used, they appear white (in technical terms, we speak of “hypersignals”) or black (“hyposignals”). In 2019, more than 2.4 million people suffer from multiple sclerosis .The research is focused on finding innovative treatments to relieve people with MS. The goal of this study is to detect abnormalities of gray matter and white matter in MS from 3D RM Image Many methods have been proposed to automatically segment lesions since manual segmentation requires expert knowledge, is time consuming and is subject to intraand interexpert variability (Vera-Olmos et al, 2016). Veronese et al (Veronese et al, 2013) proposed a fuzzy classification algorithm that uses spatial information for MS lesion segmentation. In addition to spatial information, standard deviation dependent filtering is incorporated into the algorithm to provide better noise immunity. Also, fuzzy logic is adjusted to be more selective on vertical elliptical objects instead of circular objects since most plates are in this form. Saba et al (Saba et al, 2018) presented a method of segmentation of MS lesions beginning with contour detection using the canny algorithm, and then a modified blurred mean c algorithm is applied International Journal of Cognitive Informatics and Natural Intelligence Volume 15 • Issue 2 • April-June 2021 98 to increase the accuracy of the diagnosis. Pre-treatment techniques are applied to get the best result were used, such as the brain extraction tool and binarisation Bassem (Bassem, 2012) proposed a technique for segmentation of Sclerosis lesions by using texture textural features and support vector machines. They used two generic configurable components: a central processing module that locates areas of the brain that may form MS lesions, and a postprocessing module that adds or removes these areas for more accurate data. Based on these configurable modules, single-view segmentation and multiple-section view pipelines are provided to address the limitations found in segmentation results. Khotanlou et al (Khotanlou et al, 2011) proposed a SCPFCM algorithm named based on t membership, typicity and spatial information. Firstly, initial segmentation is applied to T1-w and T2-w images to detect MS lesions. then the non-cerebral tissues are removed by using morphological functions and finally for extraction of MS lesions, the result of the image T1 is used as a mask and compared to the image T2. Ayelet et al (Ayelet et al, 2009) presented a multiscale method to detect lesions in multiple sclerosis based on two phases: segmentation and classification. The first one obtains a hierarchical decomposition of a multichannel anisotropic MRI scans and produces a set of features. These features are used in the second phase via a decision tree to detect lesions at all scales. The authors find that the problem of MS lesions segmentation is still widely open especially for supervised automatic approaches. This motivates to propose an automatic approach for MS lesion detection that uses a supervised learning without an explicit expert intervention. The approach is based on AIS for brain tissue segmentation and SVM with SMO for lesions detection. A number of features to define vector types of specific lesions were calculated and these vector types were used as inputs for SVM. This paper is organized as follows. In section 2, the researchers resent their proposed approach. Section 3 shows the obtained experimental results. Section 4 describes the comparison with a previous work and another proposed method. The final section provides the conclusion of this work. THe PRoPoSeD APPRoACH Automatic segmentation of MS lesions is difficult, as indicated in the previous section due to the large variability of multiple sclerosis lesions. Lesions have deformable shapes, their texture and intensity can vary and their location can also vary from one patient to another. The researchers propose to apply a new segmentation workflow based on a voxel analysis. The method consists of three steps (see Figure 1). For each 3D MR image, the AIS are applied for segmentation of the three main brain tissues white matter, gray matter and cerebrospinal fluid. The authors compute a number of features then the SVM is used for MS lesions segmentation only on the white matter since MS lesions are located in. Brain Tissue Segmentation The researchers started by segmenting the image of the brain MR in the three classes mentioned (grey matter, white matter and cerebrospinal fluid) using the AIS algorithm. Segmentation by Artificial Immune Systems (AIS) Artificial immune systems is a model that encompasses both, mathematics and biological principles, as the natural immune system offers interesting features like memory and learning that will be useful for solving problems (Tavana et al, 2016). International Journal of Cognitive Informatics and Natural Intelligence Volume 15 • Issue 2 • April-June 2021 99 Learning This phase is to find the memory cells (voxels) representing the regions in this study area and then make the classification using the algorithm of CLONCLAS that uses the principles of artificial clonal selection. The elements which are used in this algorithm are (Komaki et al, 2016): • Antibody: or samples represents the basis of training that will recognize the image of antigens. • Antigen: represents the basis of examples for which we want to determine the class. • Affinity: affinity in immune systems is the measure of similarity between the antibody and the • Antigen: the latter two are represented by a point. Affinity is the distance between these two points, in this work, the Euclidean distance is used. • Memory cell: represents the best antibodies found for the class. Learning occurs class by class by the CLONAG algorithm (Komaki et al, 2016): 1. Training samples are considered a priori to antibody (Ab). One of these samples randomly drawn is likened to an antigen (Ag). Through the Euclidean distance, they calculated affinity of this Ag with all Abs of the class. 2. Abs voxels are ranked in descending order according to their affinity compared to the Ag considered. The first N voxels will be selected to undergo a cloning while preserving the first voxel to form the memory cell Mc class match. Figure 1. Flowchart of the proposed approach for MS lesions segmentation International Journal of Cognitive Informatics and Natural Intelligence Volume 15 • Issue 2 • April-June 2021 100 3. Clone the n selected voxels i in proportion to their affinity. The clones number of a voxel is even higher than the affinity of the voxel is high. This number is calculated as follows: The number of clones for each member: Nc = round (β* (n/I)) (1) With Nc is the number of clones of an element, β is the cloning coefficient, I is the position of the element to be cloned, and round is a function that rounds a real number to an integer. The total number of clones (Erik et al, 2012):

Analysis of the Use of Artificial Immune Systems

Abstract: Artificial immune systems are systems of artificial intelligence at the basis of work, which are based on the principles of the functioning of the biological immune system. These systems are of great interest from researchers developing models and algorithms in the field of machine learning for solving complex computational and engineering problems. In the presented work, a review of modern methods for the implementation of artificial immune systems is carried out, taking into account the results obtained recently.

DCW-RNN: Improving Class Level Metrics for Software Vulnerability Detection Using Artificial Immune System with Clock-Work Recurrent Neural Network

Abstract: As the defenses evolve, so do the solutions to a software vulnerability. The primary reason for security incidents, e.g., cyber-attacks, originates from software vulnerabilities. It is challenging to enhance the performance of software processes and determine and eliminate software vulnerabilities. Thus, the development of algorithms with higher security to be applied to possible security issues in software represents a significant research subject for researchers in the domain of software security. The basis of the Dendritic Cell Algorithm (DCA), which is an emerging evolutionary algorithm, constitutes the behavior of specific immune agents, called dendritic cells (DCs). Till now, no strategy or idea has already been adopted on the Clock-Work Recurrent Neural Network (RNN) based Dendritic cell algorithm on vulnerability detection problems. In the present research, the first Clock-Work RNN based Dendritic Cell Algorithm (DCA) was suggested to identify complex dependencies between vulnerable object-oriented software metrics. The suggested method establishes immunity in software vulnerability prediction models to analyze the comparison of the Artificial Immune System Algorithms. The current paper involves the enhanced Clock-Work RNN based Dendritic Cell Algorithm, Genetic Algorithm (GA), and Clonal Selection Algorithm (CLONALG). Furthermore, comparison some studies was made on the basis Artificial Immune System (AIS) algorithms, such as Negative Selection Algorithm (NSA), Cellular Automata (CA), Membrane Computing (P-Systems). The experimental findings of our study demonstrate that our approach was computationally efficient on three different Java projects: Apache Tomcat (releases 6 and 7), Apache CXF, and the Stanford SecuriBench datasets.

Detection of anomalies in compiled computer program files inspired by immune mechanisms using a template method

Abstract: An intrusion detection system inspired by the human immune system is described: a custom artificial immune system that monitors a local area containing critical files in the operating system. The proposed mechanism scans the files and checks for possible malware-induced alterations in them, based on a negative selection algorithm. The system consists of two modules: a receptor generation unit, which generates receptors using an original method based on templates, and an anomaly detection unit. Anomalies detected in the files using previously generated receptors are reported to the user. The system has been implemented and experiments have been conducted to compare the effectiveness of the algorithms with that of a different receptor generation method, called the random receptor generation method. In a controlled testing environment, anomalies in the form of altered program code bytes were injected into the monitored programs. Real-world tests of this system have been performed regarding its performance and scalability. Experimental results are presented, evaluated in a comparative analysis, and some conclusions are drawn.