Design of intrusion detection system based on improved ABC_elite and BP neural networks

Duan, Letian; Han, Dong‐Guk; Tian, Qiuting

doi:10.2298/csis181001026d

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…This subsection shows of our framework's evaluation. We applied KDDTest+ and KDDTest-21 datasets and following different machine learning classifiers: Naive Bayes (NB) [34,35], Logistic Regression (LR) [36,37], Jrip (JR) [38], J48 Decision Tree (J48) [39], LMT Decision Tree (LMT), Random Forest (RF), Support Vector Machine (SMO) [40][41][42], K-Nearest Neighbors (IBK) [43,44]. All classifier machine learning methods are notified in Table 5.…”

Section: Proposed Framework Evaluationmentioning

confidence: 99%

Machine learning based distributed big data analysis framework for next generation web in IoT

Singh

Cha

Kim

et al. 2021

ComSIS

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For the advancement of the Internet of Things (IoT) and Next Generation Web, various applications have emerged to process structured or unstructured data. Latency, accuracy, load balancing, centralization, and others are issues on the cloud layer of transferring the IoT data. Machine learning is an emerging technology for big data analytics in IoT applications. Traditional data analyzing and processing techniques have several limitations, such as centralization and load managing in a massive amount of data. This paper introduces a Machine Learning Based Distributed Big Data Analysis Framework for Next Generation Web in IoT. We are utilizing feature extraction and data scaling at the edge layer paradigm for processing the data. Extreme Learning Machine (ELM) is adopting in the cloud layer for classification and big data analysis in IoT. The experimental evaluation demonstrates that the proposed distributed framework has a more reliable performance than the traditional framework.

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Section: Proposed Framework Evaluationmentioning

confidence: 99%

Machine learning based distributed big data analysis framework for next generation web in IoT

Singh

Cha

Kim

et al. 2021

ComSIS

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“…Zhao and Li [8] used a real coding genetic algorithm to optimize the weights and threshold of BP neural network. Duan et al [9] got an IDS in which BP neural network is combined with improved artificial bee colony algorithm with elite-guided search equations. Yang et al [10] presented an LM-BP neural network model which optimizes the weight threshold of BP neural network.…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection System Using PSO and DE Algorithms with BP Neural Network

Wang

Guan

Luo

et al. 2022

Frontiers in Artificial Intelligence and Applications

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Intrusion detection system (IDS) combines software and hardware to detect network attacks. In this paper, we propose a new intrusion detection method based on an improved BP neural network algorithm. We improve the BP neural network algorithm by combining it with the particle swarm optimization (PSO) algorithm and the differential evolution (DE) algorithm. We also propose a new framework based on common intrusion detection framework to accommodate our improved BP neural network. The experiments based on the CICIDS2017 dataset show our approach achieves better detection efficiency.

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“…In order to efficiently identify various types of attacks on networks, researchers have applied machine learning to intrusion detection. Common machine learning methods include Naïve Bayesian (NB) [2], back propagation (BP) [3], support vector machines (SVM) [4] and extreme learning machines (ELM) [5]. However, the intrusion detection of network data featuring high dimensions and more redundancies using a single machine learning method will result in reduced detection speed and accuracy.…”

Section: Introductionmentioning

confidence: 99%

IKPCA-ELM-based Intrusion Detection Method

2020

KSII TIIS

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An IKPCA-ELM-based intrusion detection method is developed to address the problem of the low accuracy and slow speed of intrusion detection caused by redundancies and high dimensions of data in the network. First, in order to reduce the effects of uneven sample distribution and sample attribute differences on the extraction of KPCA features, the sample attribute mean and mean square error are introduced into the Gaussian radial basis function and polynomial kernel function respectively, and the two improved kernel functions are combined to construct a hybrid kernel function. Second, an improved particle swarm optimization (IPSO) algorithm is proposed to determine the optimal hybrid kernel function for improved kernel principal component analysis (IKPCA). Finally, IKPCA is conducted to complete feature extraction, and an extreme learning machine (ELM) is applied to classify common attack type detection. The experimental results demonstrate the effectiveness of the constructed hybrid kernel function. Compared with other intrusion detection methods, IKPCA-ELM not only ensures high accuracy rates, but also reduces the detection time and false alarm rate, especially reducing the false alarm rate of small sample attacks.

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Design of intrusion detection system based on improved ABC_elite and BP neural networks

Cited by 9 publications

References 15 publications

Machine learning based distributed big data analysis framework for next generation web in IoT

Machine learning based distributed big data analysis framework for next generation web in IoT

Intrusion Detection System Using PSO and DE Algorithms with BP Neural Network

IKPCA-ELM-based Intrusion Detection Method

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