Improved Harris Combined With Clustering Algorithm for Data Traffic Classification

Liu, Qingli; Li, Mengqian; Cao, Na; Zhang, Zhenya; Yang, Guoqiang

doi:10.1109/access.2022.3188866

Cited by 6 publications

(6 citation statements)

References 22 publications

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“…It can control the behavior of the whole group through the interaction of individuals without supervision, and the algorithm is easy to implement. Liu et al [15] used the optimization ability of HHO to solve the shortcomings of clustering algorithms that tend to fall into local optimum. A data traffic classification method combining a clustering algorithm and improved HHO is proposed.…”

Section: Network Traffic Identification Methods Based On Machine Lear...mentioning

confidence: 99%

An Improved Jump Spider Optimization for Network Traffic Identification Feature Selection

Xu,

Hu,

Cao

et al. 2023

Computers, Materials &Amp; Continua

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The massive influx of traffic on the Internet has made the composition of web traffic increasingly complex. Traditional port-based or protocol-based network traffic identification methods are no longer suitable for today's complex and changing networks. Recently, machine learning has been widely applied to network traffic recognition. Still, high-dimensional features and redundant data in network traffic can lead to slow convergence problems and low identification accuracy of network traffic recognition algorithms. Taking advantage of the faster optimizationseeking capability of the jumping spider optimization algorithm (JSOA), this paper proposes a jumping spider optimization algorithm that incorporates the harris hawk optimization (HHO) and small hole imaging (HHJSOA). We use it in network traffic identification feature selection. First, the method incorporates the HHO escape energy factor and the hard siege strategy to form a new search strategy for HHJSOA. This location update strategy enhances the search range of the optimal solution of HHJSOA. We use small hole imaging to update the inferior individual. Next, the feature selection problem is coded to propose a jumping spiders individual coding scheme. Multiple iterations of the HHJSOA algorithm find the optimal individual used as the selected feature for KNN classification. Finally, we validate the classification accuracy and performance of the HHJSOA algorithm using the UNSW-NB15 dataset and KDD99 dataset. Experimental results show that compared with other algorithms for the UNSW-NB15 dataset, the improvement is at least 0.0705, 0.00147, and 1 on the accuracy, fitness value, and the number of features. In addition, compared with other feature selection methods for the same datasets, the proposed algorithm has faster convergence, better merit-seeking, and robustness. Therefore, HHJSOA can improve the classification accuracy and solve the problem that the network traffic recognition algorithm needs to be faster to converge and easily fall into local optimum due to high-dimensional features.

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Section: Network Traffic Identification Methods Based On Machine Lear...mentioning

confidence: 99%

An Improved Jump Spider Optimization for Network Traffic Identification Feature Selection

Xu,

Hu,

Cao

et al. 2023

Computers, Materials &Amp; Continua

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“…Authors of [47] combined the Harris eagle algorithm with fuzzy c-means. They used Moore dataset [48] with 10 flow features to identify various traffic classes.…”

Section: Fuzzy Logic Based Tcmentioning

confidence: 99%

Classification of Flow-based Network Applications using Fuzzy Logic

Khan,

Farooq

2023

Preprint

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Ever since the boom of social media, the internet has drastically changed our lives. From social mediaplatforms, Industry 4.0, Big Data, Cloud Computing to Augmented and Virtual reality, the amountof data on the internet has increased on an unprecedented scale, and it is not going to decrease in theyears to come. This increasing amount of data puts a huge strain on today’s network infrastructure. Itis the need of the hour to keep the internet infrastructure up-to-date to tackle the upcoming challenges.Internet Network Traffic Classification is thus a new paradigm in which lightweight sophisticatedalgorithms are designed and tested on live networks. This problem has been focused a lot with thehelp of Machine and Deep learning tools. These models do provide a high accuracy of result, but canbecome complex problems depending upon the specific application. In this paper, a Fuzzy Logic basedLightweight Classification Architecture is proposed and implemented to successfully identify Audioand Video from other Network Traffic (NT) applications. A detailed elaboration of features extractedfrom a variety of applications is presented. Fuzzy logic is used to distinguish among the classes basedon human intuition based rules. To verify the proposed algorithm, flow-based network captures isused to identify whether the correct class of network traffic is predicted or not. The results obtainedfrom the Fuzzy logic is also compared with traditional Logistic regression and kNN based MachineLearning algorithms to showcase the superiority of Fuzzy logic.

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“…Ref. [40] proposed a data flow classification method based on an improved Harris Eagle algorithm combined with fuzzy C-means clustering. The method maps data flow samples to Harris Eagle population individuals, finds the optimal position through several iterations of the improved Harris Eagle optimization (IHHO) algorithm, and uses it as the initial clustering center to guide data flow classification by clustering according to the maximum membership principle, which improves the accuracy and stability of classification to some extent.…”

Section: Unknown Internet Traffic Identificationmentioning

confidence: 99%

Unknown Traffic Recognition Based on Multi-Feature Fusion and Incremental Learning

et al. 2023

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Accurate classification and identification of Internet traffic are crucial for maintaining network security. However, unknown network traffic in the real world can affect the accuracy of current machine learning models, reducing the efficiency of traffic classification. Existing unknown traffic classification algorithms are unable to optimize traffic features and require the entire system to be retrained each time new traffic data are collected. This results in low recognition efficiency, making the algoritms unsuitable for real-time application detection. To solve the above issues, we suggest a multi-feature fusion-based incremental technique for detecting unknown traffic in this paper. The approach employs a multiple-channel parallel architecture to extract temporal and spatial traffic features. It then uses the mRMR algorithm to rank and fuse the features extracted from each channel to overcome the issue of redundant encrypted traffic features. In addition, we combine the density-ratio-based clustering algorithm to identify the unknown traffic features and update the model via incremental learning. The cassifier enables real-time classification of known and unknown traffic by learning newly acquired class knowledge. Our model can identify encrypted unknown Internet traffic with at least 86% accuracy in various scenarios, using the public ISCX-VPN-Tor datasets. Furthermore, it achieves 90% accuracy on the intrusion detection dataset NSL-KDD. In our self-collected dataset from a real-world environment, the accuracy of our model exceeds 96%. This work offers a novel method for identifying unknown network traffic, contributing to the security preservation of network environments.

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Improved Harris Combined With Clustering Algorithm for Data Traffic Classification

Cited by 6 publications

References 22 publications

An Improved Jump Spider Optimization for Network Traffic Identification Feature Selection

An Improved Jump Spider Optimization for Network Traffic Identification Feature Selection

Classification of Flow-based Network Applications using Fuzzy Logic

Unknown Traffic Recognition Based on Multi-Feature Fusion and Incremental Learning

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