RETRACTED ARTICLE: Research on network intrusion detection technology based on improved FCMA algorithm

Li, Hanguang

doi:10.1007/s12652-021-03145-5

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Similarly, the study by Sadiq, Abbas, and Amin [27] proposes an intelligent approach for network intrusion detection based on the decision tree and Naive Bayes algorithm. The authors aimed to design an approach to enhance the accuracy of intrusion detection while reducing the false alarm rate.…”

Section: Literature Surveymentioning

confidence: 99%

Comparative Evaluation of Machine Learning Algorithms for Intrusion Detection

Oluwakemi,

Abdullahi,

Anyachebelu

2023

AJRCoS

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This study undertakes a comparative examination of machine learning algorithms used for intrusion detection, addressing the escalating challenge of safeguarding networks from malicious attacks in an era marked by a proliferation of network-related applications. Given the limitations of conventional security tools in combatting intrusions effectively, the adoption of machine learning emerges as a promising avenue for bolstering detection capabilities. The research evaluates the efficacy of three distinct machine learning algorithms—Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Naive Bayes—in identifying diverse attack categories, including Denial of Service, Probe, Remote to Local, and User to Root. Conducted on the NSL-KDD dataset, the analysis unveils CNN and RNN as superior performers compared to Naive Bayes, particularly in terms of detection accuracy. These findings extend value to both researchers and practitioners in the realm of intrusion detection systems, offering insights into optimal algorithmic choices. Furthermore, the study's implications resonate within broader contexts, such as the advancement of secure automation in industrial environments and the realm of automobile automation. Overall, this research contributes to the ongoing efforts to fortify network security and promote the development of safer technological landscapes.

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Section: Literature Surveymentioning

confidence: 99%

Comparative Evaluation of Machine Learning Algorithms for Intrusion Detection

Oluwakemi,

Abdullahi,

Anyachebelu

2023

AJRCoS

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“…(3) The controller cooperation algorithm achieves load balance and stability among various controllers through data synchronization and cooperation [25].…”

Section: Controller Deployment Techniquesmentioning

confidence: 99%

Deployment Strategy for Multiple Controllers Based on the Aviation On-Board Software-Defined Data Link Network

Zhu,

Fu,

et al. 2023

CMC

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In light of the escalating demand and intricacy of services in contemporary terrestrial, maritime, and aerial combat operations, there is a compelling need for enhanced service quality and efficiency in airborne cluster communication networks. Software-Defined Networking (SDN) proffers a viable solution for the multifaceted task of cooperative communication transmission and management across different operational domains within complex combat contexts, due to its intrinsic ability to flexibly allocate and centrally administer network resources. This study pivots around the optimization of SDN controller deployment within airborne data link clusters. A collaborative multi-controller architecture predicated on airborne data link clusters is thus proposed. Within this architectural framework, the controller deployment issue is reframed as a two-fold problem: subdomain partitioning and central interaction node selection. We advocate a subdomain segmentation approach grounded in node value ranking (NDVR) and a central interaction node selection methodology predicated on an enhanced Artificial Fish Swarm Algorithm (AFSA). The advanced NDVR-AFSA (Node value ranking-Improved artificial fish swarm algorithm) algorithm makes use of a chaos algorithm for population initialization, boosting population diversity and circumventing premature algorithm convergence. By the integration of adaptive strategies and incorporation of the genetic algorithm's crossover and mutation operations, the algorithm's search range adaptability is enhanced, thereby increasing the possibility of obtaining globally optimal solutions, while concurrently augmenting cluster reliability. The simulation results verify the advantages of the NDVR-IAFSA algorithm, achieve a better load balancing effect, improve the reliability of aviation data link cluster, and significantly reduce the average propagation delay and disconnection rate, respectively, by 12.8% and 11.7%. This shows that the optimization scheme has important significance in practical application, and can meet the high requirements of modern sea, land, and air operations to aviation airborne communication networks.

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“…PCA obtains principal components (feature vectors) and their values through covariance matrix decomposition (Salem and Hussein, 2019). Except for research in intrusion detection (Li, 2021), PCA was widely applied in face recognition, stock model (Zhi and Liu, 2019).…”

Section: Feature Extractionmentioning

confidence: 99%

A novel dimension reduction based method to detect network intrusions

Chang

Nalluri

Chen

et al. 2022

Preprint

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There is a steep rise in the trend of the utility of internet technology nowadays. This tremendous increase ushered in a massive amount of data generated and personal information has been stored in the cloud. For apparent reasons, undivided attention could cause huge economic losses and cause serious security issues. Therefore, effective security solutions for detecting and combating cyber-attacks have become increasingly important. In addition, intrusion detection systems can be divided into several detection methods based on data type and analysis methods. However, no one method is absolutely effective in a variety of environments. For different types of cyber-attacks, various intrusion detection systems have been developed using artificial intelligence or machine learning techniques, but with the change in attack patterns, the intrusion detection performance of these systems needs to be greatly improved. In this study, the anomaly detection concept is used to propose a new local kernel-principal component analysis (LKPCA) method to improve classification performance. In addition, we are developing two multi-class learning strategies for binary classifiers to build multi-class classifiers. The analysis was duly conducted on the benchmark NSL-KDD and KDD datasets. The proposed method is validated using cross-validation and compared with existing feature extraction methods. This method outperformed over the existing intrusion detection methods used for the dataset.

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RETRACTED ARTICLE: Research on network intrusion detection technology based on improved FCMA algorithm

Cited by 4 publications

References 17 publications

Comparative Evaluation of Machine Learning Algorithms for Intrusion Detection

Comparative Evaluation of Machine Learning Algorithms for Intrusion Detection

Deployment Strategy for Multiple Controllers Based on the Aviation On-Board Software-Defined Data Link Network

A novel dimension reduction based method to detect network intrusions

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