Multilayer Feedforward Neural Network for Internet Traffic Classification

Recently, the efficient internet traffic classification has gained attention in order to improve service quality in IP networks. But the problem with the existing solutions is to handle the imbalanced dataset which has high uneven distribution of flows between the classes. In this paper, we propose a multilayer feedforward neural network architecture to handle the high imbalanced dataset. In the proposed model, we used a variation of multilayer perceptron with 4 hidden layers (called as mountain mirror networks) which does the feature transformation effectively. To check the efficacy of the proposed model, we used Cambridge dataset which consists of 248 features spread across 10 classes. Experimentation is carried out for two variants of the same dataset which is a standard one and a derived subset. The proposed model achieved an accuracy of 99.08% for highly imbalanced dataset (standard)

An Effective Cost-Sensitive Convolutional Neural Network for Network Traffic Classification

The volume, and density of computer network traffic are increasing dramatically with the technology advancements, which has led to the emergence of various new protocols. Analyzing the huge data in large business networks has become important for the owners of those networks. As the majority of the developed applications need to guarantee the network services, while some traditional applications may work well enough without a specific service level. Therefore, the performance requirements of future internet traffic will increase to a higher level. Increasing pressure on the performance of computer networks requires addressing several issues, such as maintaining the scalability of new service architectures, establishing control protocols for routing, and distributing information to identified traffic streams. The main concern is flow detection and traffic detection mechanisms to help establish traffic control policies. A cost-sensitive deep learning approach for encrypted traffic classification has been proposed in this research, to confront the effect of the class imbalance problem on the low-frequency traffic data detection. The developed model can attain a high level of performance, particularly for low-frequency traffic data. It outperformed the other traffic classification methods

Using deep learning to classify community network traffic

Traffic classification is an important aspect of network management. This aspect improves the quality of service, traffic engineering, bandwidth management and internet security. Traffic classification methods continue to evolve due to the ever-changing dynamics of modern computer networks and the traffic they generate. Numerous studies on traffic classification make use of the Machine Learning (ML) and single Deep Learning (DL) models. ML classification models are effective to a certain degree. However, studies have shown they record low prediction and accuracy scores. In contrast, the proliferation of various deep learning techniques has recorded higher accuracy in traffic classification. The Deep Learning models have been successful in identifying encrypted network traffic. Furthermore, DL learns new features without the need to do much feature engineering compared to ML or Traditional methods. Traditional methods are inefficient in meeting the demands of ever-changing requirements of networks and network applications. Traditional methods are unfeasible and costly to maintain as they need constant updates to maintain their accuracy. In this study, we carry out a comparative analysis by adopting an ML model (Support Vector Machine) against the DL Models (Convolutional Neural Networks (CNN), Gated Recurrent Unit (GRU) and a hybrid model: CNNGRU to classify encrypted internet traffic collected from a community network. In this study, we performed a comparative analysis by adopting an ML model (Support vector machine). Machine against DL models (Convolutional Neural networks (CNN), Gated Recurrent Unit (GRU) and a hybrid model: CNNGRU) and to classify encrypted internet traffic that was collected from a community network. The results show that DL models tend to generalise better with the dataset in comparison to ML. Among the deep Learning models, the hybrid model outperformed all the other models in terms of accuracy score. However, the model that had the best accuracy rate was not necessarily the one that took the shortest time when it came to prediction speed considering that it was more complex. Support vector machines outperformed the deep learning models in terms of prediction speed

Your Smart Home Can't Keep a Secret: Towards Automated Fingerprinting of IoT Traffic with Neural Networks

The IoT (Internet of Things) technology has been widely adopted in recent years and has profoundly changed the people's daily lives. However, in the meantime, such a fast-growing technology has also introduced new privacy issues, which need to be better understood and measured. In this work, we look into how private information can be leaked from network traffic generated in the smart home network. Although researchers have proposed techniques to infer IoT device types or user behaviors under clean experiment setup, the effectiveness of such approaches become questionable in the complex but realistic network environment, where common techniques like Network Address and Port Translation (NAPT) and Virtual Private Network (VPN) are enabled. Traffic analysis using traditional methods (e.g., through classical machine-learning models) is much less effective under those settings, as the features picked manually are not distinctive any more. In this work, we propose a traffic analysis framework based on sequence-learning techniques like LSTM and leveraged the temporal relations between packets for the attack of device identification. We evaluated it under different environment settings (e.g., pure-IoT and noisy environment with multiple non-IoT devices). The results showed our framework was able to differentiate device types with a high accuracy. This result suggests IoT network communications pose prominent challenges to users' privacy, even when they are protected by encryption and morphed by the network gateway. As such, new privacy protection methods on IoT traffic need to be developed towards mitigating this new issue