An internet traffic classification method based on echo state network and improved salp swarm algorithm

Zhang, Meijia; Sun, Wenjuan; Tian, Jie; Zheng, Xiyuan; Guan, Shaopeng

doi:10.7717/peerj-cs.860

Cited by 4 publications

(3 citation statements)

References 46 publications

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“…W in , W x , and W back were created arbitrarily and endured unchanged in the trained stage of ESN. The network only requires training the resultant linking weighted matrix W out that decreases the computational complexity [25].…”

Section: Ransomware Classification Using Esn Modelmentioning

confidence: 99%

Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection

Alalayah¹,

Alrayes²,

Nour³

et al. 2023

Computer Systems Science and Engineering

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Malware is a 'malicious software program that performs multiple cyberattacks on the Internet, involving fraud, scams, nation-state cyberwar, and cybercrime. Such malicious software programs come under different classifications, namely Trojans, viruses, spyware, worms, ransomware, Rootkit, botnet malware, etc. Ransomware is a kind of malware that holds the victim's data hostage by encrypting the information on the user's computer to make it inaccessible to users and only decrypting it; then, the user pays a ransom procedure of a sum of money. To prevent detection, various forms of ransomware utilize more than one mechanism in their attack flow in conjunction with Machine Learning (ML) algorithm. This study focuses on designing a Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection (LBAAA-OMLMD) approach in Computer Networks. The presented LBAAA-OMLMD model mainly aims to detect and classify the existence of ransomware and goodware in the network. To accomplish this, the LBAAA-OMLMD model initially derives a Learning-Based Artificial Algae Algorithm based Feature Selection (LBAAA-FS) model to reduce the curse of dimensionality problems. Besides, the Flower Pollination Algorithm (FPA) with Echo State Network (ESN) Classification model is applied. The FPA model helps to appropriately adjust the parameters related to the ESN model to accomplish enhanced classifier results. The experimental validation of the LBAAA-OMLMD model is tested using a benchmark dataset, and the outcomes are inspected in distinct measures. The comprehensive comparative examination demonstrated the betterment of the LBAAA-OMLMD model over recent algorithms.

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Section: Ransomware Classification Using Esn Modelmentioning

confidence: 99%

Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection

Alalayah¹,

Alrayes²,

Nour³

et al. 2023

Computer Systems Science and Engineering

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“…Using the information in each packet that was transmitted or through a collection of packets and their metadata, called a flow, ISPs can classify traffic based on the application that produced it and optimize their infrastructure to scale to the evolving needs of their customers [6]. Due to the emergence of a suite of encryption and anonymization technologies such as Secure Shell Protocol (SSH), Hypertext Transfer Protocol (HTTPS), The Onion Router (TOR), and Virtual Private Networks (VPNs), it can be difficult to rely on conventional techniques to discover the origin of the anonymous and encrypted traffic [7].…”

Section: Introductionmentioning

confidence: 99%

Tabular-to-Image Transformations for the Classification of Anonymous Network Traffic Using Deep Residual Networks

Briner,

Cullen,

Halladay

et al. 2023

IEEE Access

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With the meteoric rise in anonymous network traffic data, there is a considerable need for effective automation in traffic identification tasks. Though many shallow and deep machine learning network traffic classification solutions have been proposed, they often rely on tabular data, making them unable to detect complex spatial relationships. However, recent advancements in computer processing power have increased the viability of transforming tabular data into images for training deep convolutional neural networks, transforming structured data problems into spatial ones. To identify the most effective methods for representing tabular anonymous network traffic data as images, we compared five deep learning classifiers trained on data from different tabular-to-image algorithms-Image Generator for Tabular Data (IGTD), DeepInsight, vector-of-feature wrapping (normalized and non-normalized), and our newly introduced Binary Image Encoding (BIE) technique in the classification of eight network application types. Furthermore, we examine whether deep residual models trained on tabular-to-image data can outperform the top-performing shallow learner, XGBoost, at classifying anonymous network traffic. We found that ResNet-50, a pre-trained instance of deep residual network, trained on image datasets using IGTD and the novel Binary Image Encoding outperformed XGBoost trained on tabular data. Our ResNet-50 models trained using IGTD and BIE achieved F1-scores of 96.0% and 98.49% respectively, improving on the baseline of 95.1% achieved by XGBoost.

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“…Their experimental results show that their model is more effective than other benchmarks, and the lowest generalization error is obtained. They used the improved Salp Swarm algorithm (SSA) to optimize ESN, and Zhang et al (2022b) proposed an Internet traffic classification method based on ESN. Simulation results show that the proposed method performs better than other traditional machine learning algorithms in terms of each type of measurement and overall accuracy.…”

Section: Introductionmentioning

confidence: 99%

Sequence prediction with different dimensions based on two novel deep echo state network models

Sun,

Li,

Peng

et al. 2023

Transactions of the Institute of Measurement and Control

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The echo state network (ESN) is a typical reservoir computation model, which was first proposed by Jaeger et al. It was widely used in various fields and achieved excellent results for a long time, especially in time series prediction. In recent years, there are few improvements to the ESN structure, and the more famous is the deep echo state network (DESN) model. However, a DESN will cause the loss of input data. How to effectively optimize the structure of ESN and how to scientifically add input data to deep echo are urgent problems to be solved. In this paper, we propose multi-reservoir ESN models based on how the input data participate in the system. Then, we use complex nonlinear chaotic systems with different dimensions to test our model. Finally, we compare it with the traditional model and the recently proposed model, and then find that our models have better predictive performance.

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An internet traffic classification method based on echo state network and improved salp swarm algorithm

Cited by 4 publications

References 46 publications

Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection

Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection

Tabular-to-Image Transformations for the Classification of Anonymous Network Traffic Using Deep Residual Networks

Sequence prediction with different dimensions based on two novel deep echo state network models

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