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Paper | Networking problem | Approach | Method/classifier technique | Application type | Dataset used | Experimental result | SDN | ML/DL | Elephant | Data center |
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Aceto et al. [30] | Encrypted TC | Deep learning | SAE, CNN, and LSTM | HTTP traffic | 300 k Mobile datasets activity | SAE outperforms in TC | ⁰ | • | ⁰ | ⁰ |
Nascita et al. [35] | | Deep learning | 1D-CNN | WeChat | MIRAGE 2019 dataset | Global model interpretation r | ⁰ | • | ⁰ | ⁰ |
Ali[37] | Intelligence TC | Deep learning | Deep neural network | HTTP, mail, and multimedia | Moore dataset | Better accuracy, precision, recall, and FScore results | ⁰ | • | ⁰ | ⁰ |
Dong and Xia [39] | Deep learning application | Deep learning | SC-CNN | Image segmentation | MNIST dataset | Deep learning create more powerful optimization methods | ⁰ | • | ⁰ | ⁰ |
Dong et al. [38] | Abnormal traffic detection | Deep learning | Kmean, AE, and reinforcement | Abnormal traffic | NSL-KDD and AWID datasets | Achieved good result in time complexity | ⁰ | • | ⁰ | ⁰ |
Dong et al. [36] | Tc | Deep learning | CNN and GAN | FTP, Gmail, and Skype | USTC-TFC2016 dataset | Yields better application traffic classification and detection result | ⁰ | • | ⁰ | • |
Bovenzi et al. [29] | Model parallelism TC | Machine learning | RF, DT, and Bayes | IoT and fog platform | Anon17 NIMS dataset | Reducing training time | ⁰ | • | ⁰ | • |
Chang [40] | Encrypted TC | Machine learning | RNN-AE | Github, Gmail, and Icloud | Real-world dataset 18 applications | Achieves 99.14% performance | ⁰ | • | ⁰ | ⁰ |
Ibrahim [49] | Online game | Machine learning-mixed | Fixed Java code | http, FTP, and Skype | LOL game dataset | Produces 91% accuracy | ⁰ | • | ⁰ | ⁰ |
Dong [20] | Multiclass TC | Machine learning | SVM | http, imap, and dns | MOORE and NOC datasets | Improve classification accuracy | ⁰ | • | ⁰ | ⁰ |
Shi et al. [50] | Machine learning Application | Machine learning | Netflow extended machine learning | FCBF algorithm | Machine learning Application optimization | Algorithm called FCBF yields better performance | ⁰ | • | ⁰ | ⁰ |
Dong and Li [42] | Traffic identification | Machine learning | Neural network and Naïve Bayes | TCP and UDP flows | MOORE and NOCSET | Achieves 95% identification accuracy | ⁰ | • | ⁰ | ⁰ |
Shi [13] | Online encrypted | Machine learning | Naïve Bayes | Online Skype | Skype-SET | Reduces false positives and false negatives | ⁰ | • | ⁰ | ⁰ |
Dong et al. [16] | Traffic identification | Algorithm based on architecture | High identification accuracy | Routing application | NOC_SET, CAIDA, and LBNL_SET | High accuracy | ⁰ | • | ⁰ | ⁰ |
Oliveira [2] | QoS | Architecture | Fixed Python program | Distributed applications | Small text and video dataset | Low overhead | • | ⁰ | • | • |
Hamdan et al. [11] | Load balancing | Architecture | Fixed Python program | d/t applications | Sketch-based filter elephants | Good running time and performance | • | ⁰ | • | • |
This paper | Elephant flow detector | Deep learning | DNN, CNN, LSTM, and AE | Real-time apps | NIMS and SDN datasets | 98.78% accuracy | • | • | • | • |
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