A new version of the deterministic dendritic cell algorithm based on numerical differential and immune response

Wen, Zhou; Liang, Yiwen

doi:10.1016/j.asoc.2020.107055

Cited by 12 publications

(9 citation statements)

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“…Compared with C4.5, LibSVM, Hoeffding Tree, and NaiveBayes, the KNN-DCA and SVM-DCA achieved better classification results. Zhou et al [1] proposed a NIDDCA that used numerical differential to calculate the change of the selected feature as the input signals. Through experimental results for signal extraction, they achieved a satisfactory result better than other recent DCA-derived classification algorithms on most datasets.…”

Section: Related Workmentioning

confidence: 99%

“…In this work, two experiments are performed to study the feasibility and superiority of the proposed approach. In the first experiment, GGA-DCA and state-of-the-art DCA expansion algorithms (NIDDCA [1], FLA-DCA [17], GA-PSM [29], and the SVM-DCA [20]) perform classification tasks on the 24 data sets. For the purpose of baseline comparison, the well-known classifiers, the KNN, the DT, the XGboost, the RF, and the ERT, also perform classification tasks on the 24 data sets.…”

Section: Experiments Setupmentioning

confidence: 99%

“…The natural immune system assists in recognizing pathogens that can cause destructive infections in individuals and has certain key characteristics: diversity, distributive, dynamic, adaptivity, and robustness [1]. Inspired by its function, several researchers mixed the mechanisms of immunity and computers to propose a collection of bio-inspired algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, machine learning algorithms, such as K-Nearest Neighbors (KNN) [19] and Support Vector Machine (SVM) [20], are employed for input signal generation. Moreover, Zhou et al [1] utilized numerical differential to extract features based on the data changes of the selected features. Among these methods, some approaches may not generate the optimal feature subset due to ignoring the effects of those filtered features; moreover, some approaches are unable to describe the relationship between the attributes and signal categorization.…”

Section: Introductionmentioning

confidence: 99%

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Dendritic Cell Algorithm with Grouping Genetic Algorithm for Input Signal Generation

Zhang¹,

Liang²,

Dong³

2023

Computer Modeling in Engineering &Amp; Sciences

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The artificial immune system, an excellent prototype for developing Machine Learning, is inspired by the function of the powerful natural immune system. As one of the prevalent classifiers, the Dendritic Cell Algorithm (DCA) has been widely used to solve binary problems in the real world. The classification of DCA depends on a data preprocessing procedure to generate input signals, where feature selection and signal categorization are the main work. However, the results of these studies also show that the signal generation of DCA is relatively weak, and all of them utilized a filter strategy to remove unimportant attributes. Ignoring filtered features and applying expertise may not produce an optimal classification result. To overcome these limitations, this study models feature selection and signal categorization into feature grouping problems. This study hybridizes Grouping Genetic Algorithm (GGA) with DCA to propose a novel DCA version, GGA-DCA, for accomplishing feature selection and signal categorization in a search process. The GGA-DCA aims to search for the optimal feature grouping scheme without expertise automatically. In this study, the data coding and operators of GGA are redefined for grouping tasks. The experimental results show that the proposed algorithm has significant advantages over the compared DCA expansion algorithms in terms of signal generation.

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Section: Related Workmentioning

confidence: 99%

Section: Experiments Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dendritic Cell Algorithm with Grouping Genetic Algorithm for Input Signal Generation

Zhang¹,

Liang²,

Dong³

2023

Computer Modeling in Engineering &Amp; Sciences

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“…The DCA classifier was successfully applied to a wide range of real-world applications, including cyber-attack detection, classification and anomaly detetion [11][12][13], but DCA still suffers from low accuracy and detection rate due to the fact that the lack of regulating, learning and memory mechanisms of innate immune system which results in DCA™s large number of random parameters are set according to expert knowledge. Over the past few years, many researchers have developed different works to extend the standard DCA version.…”

Section: Introductionmentioning

confidence: 99%

Innate immune memory and its application to artificial immune systems

et al. 2022

Self Cite

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The study of innate immune-based algorithms is an important research domain in Artificial Immune System (AIS), such as Dendritic Cell Algorithm (DCA), Toll-Like Receptor algorithm (TLRA). The parameters in these algorithms usually require either manually pre-defined usually provided by the immunologists, or empirically derived from the training dataset, and result in poor self-adaptation and self-learning. The fundamental reason is that the original innate immune mechanisms lack adaptive biological theory. To solve this problem, a theory called â€˜Trained Immunity™ or Innate Immune Memory (IIM)™ that thinks innate immunity can also build immunological memory to enhance the immune system™s learning and adaptive reactions to the second stimulus is introduced into AIS to improve the innate immune algorithms™ adaptability. In this study, we present an overview of IIM with particular emphasis on analogies in the AIS world, and a modified DCA with an effective automated tuning mechanism based on IIM (IIM-DCA) to optimize migration threshold of DCA. The migration threshold of Dendritic Cells (DCs) determines the lifespan of the antigen collected by DCs, and directly affect the detection speed and accuracy of DCA. Experiments on real datasets show that our proposed IIM-DCA which integrates Innate Immune Memory mechanism delivers more accurate results.

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Deep Artificial Immune System With Malicious Node Detection and Secure Routing Protocol in MANET

Jamaesha,

Gowtham,

Ramkumar

2024

Trans Emerging Tel Tech

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In the context of Mobile Ad Hoc Networks (MANETs), the dynamic and decentralized topology poses significant challenges like unreliable connectivity, limited bandwidth, node mobility, and vulnerability to security threats from malicious nodes. Ensuring secure and energy‐efficient data transmission in such environments is crucial for mission‐critical applications. This research addresses these pressing challenges by introducing a robust routing protocol capable of detecting and mitigating malicious nodes, thereby enhancing MANET's Quality of Service (QoS). The proposed approach, the Dendritic Cell with Adaptive Trust Q‐learning Protocol (dDC‐ATQP), integrates several innovative techniques to tackle these issues. Firstly, the trust evaluation mechanism assesses the behavior of nodes to identify potential malicious actors, mitigating the effect of malicious nodes on system execution. Secondly, the adaptive routing strategy optimizes data transmission paths based on real‐time network conditions, reducing latency and packet loss. To evaluate the effectiveness of this approach, extensive simulations are conducted using a range of performance metrics. The results demonstrate significant improvements over existing methods, including a throughput increase of (79.2% in 50 s), lower end‐to‐end‐delay (0.075 s for 20 nodes), energy consumption of (38.55/J), higher packet delivery ratio (98% for 20 nodes), reduced packet loss ratio (5% for 100 nodes), enhanced security (80% for 70 nodes).

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A new version of the deterministic dendritic cell algorithm based on numerical differential and immune response

Cited by 12 publications

References 12 publications

Dendritic Cell Algorithm with Grouping Genetic Algorithm for Input Signal Generation

Dendritic Cell Algorithm with Grouping Genetic Algorithm for Input Signal Generation

Innate immune memory and its application to artificial immune systems

Deep Artificial Immune System With Malicious Node Detection and Secure Routing Protocol in MANET

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