pdf/convolutional-neural-network-based-intrusion-detection-txd6jnpenh.pdf

Convolutional neural network-based intrusion detection system for AVTP streams in automotive Ethernet-based networks

Network Traffic Classification (NTC) has become an important feature in various network management operations, e.g., Quality of Service (QoS) provisioning and security services. Machine Learning (ML) algorithms as a popular approach for NTC can promise reasonable accuracy in classification and deal with encrypted traffic. However, ML-based NTC techniques suffer from the shortage of labeled traffic data which is the case in many real-world applications. This study investigates the applicability of an active form of ML, called Active Learning (AL), in NTC. AL reduces the need for a large number of labeled examples by actively choosing the instances that should be labeled. The study first provides an overview of NTC and its fundamental challenges along with surveying the literature on ML-based NTC methods. Then, it introduces the concepts of AL, discusses it in the context of NTC, and review the literature in this field. Further, challenges and open issues in AL-based classification of network traffic are discussed. Moreover, as a technical survey, some experiments are conducted to show the broad applicability of AL in NTC. The simulation results show that AL can achieve high accuracy with a small amount of data. 

/pdf/active-learning-for-network-traffic-classification-a-1x82jteq.pdf

Active Learning for Network Traffic Classification: A Technical Study

Network management is facing a great challenge to analyze and identify encrypted network traffic with specific applications and protocols. A significant number of network users applying different encryption techniques to network applications and services to hide the true nature of the network communication. These challenges attract the network community to improve network security and enhance network service quality. Network managers need novel techniques to cope with the failure and shortcomings of the port-based and payload-based classification methods of encrypted network traffic due to emergent security technologies. Mainly, the famous network hopping mechanisms used to make network traffic unknown and anonymous are VPN (virtual private network) and TOR (Onion Router). This paper presents a novel scheme to unveil encrypted network traffic and easily identify the tunneled and anonymous network traffic. The proposed identification scheme uses the highly desirable deep learning techniques to easily and efficiently identify the anonymous network traffic and extract the Voice over IP (VoIP) and Non VoIP ones within encrypted traffic flows. Finally, the captured traffic has been classified into four different categories, i-e., VPN VoIP, VPN Non-VoIP, TOR VoIP, and TOR Non-VoIP. The experimental results show that our identification engine is extremely robust to VPN and TOR network traffic.

/pdf/voip-traffic-detection-in-tunneled-and-anonymous-networks-43io0qno42.pdf

VoIP Traffic Detection in Tunneled and Anonymous Networks Using Deep Learning

The sharp increase in encrypted traffic brings a huge challenge to traditional traffic classification methods. Combining deep learning with time series analysis techniques is a recent trend in solving this problem. Most of these approaches only capture the temporal correlation within a flow. The accuracy and robustness are unsatisfactory, especially in an unstable network environment with high packet loss and reordering. How to learn a representation with a strong generalization ability for each encrypted traffic flow remains a key challenge. Our detailed analysis indicates that there is a graph with particular local structures corresponding to each type of encrypted traffic flow. Inspired by this observation, we propose a novel deep learning framework called EC-GCN to classify encrypted traffic flows based on multi-scale graph convolutional neural networks. We first provide a novel lightweight layer that only relies on the metadata and encodes each encrypted traffic flow into graph representations. So that our framework can be independent of different encryption protocols. Then we design a novel graph pooling and structure learning layer to dynamically extract the multi-graph representations and improve the capabilities to adapt to complex network environments. EC-GCN is an end-to-end classification model that learns representative spatial–temporal traffic features hidden in a traffic time series and then classifies them in a unified framework. Our comprehensive experiments on three real-world datasets indicate that EC-GCN can achieve up to 5%–20% accuracy improvement and outperforms state-of-the-art methods. 

EC-GCN: A encrypted traffic classification framework based on multi-scale graph convolution networks

Tor is increasingly used on the Internet as a means of accessing illicit or illegal services. If enacted by employees, such use may lead to negative impact on the organization. By its nature, Tor traffic is encrypted multiple times before being sent across networks to reach a destination. Therefore it may be impossible to detect the nature of a Tor user’s online activities. Nevertheless, such users cannot hide the fact that they are using Tor. This paper proposes a novel data payload analysis as a means of classifying Tor traffic using machine learning. To this end, we consider the characteristics of the encrypted data payload for Tor and encrypted nonTor packets from 8 different applications and extract features to train our machine learning model. Our results indicate that, contrary to the commonsense assumption that Tor packets resemble other encrypted packets, such payload content can be used to distinguish between Tor and nonTor packets.

Tor Traffic Classification Based on Encrypted Payload Characteristics

As the amount of network traffic is growing exponentially, traffic analysis and classification are playing a significant role for efficient resource allocation and network management. However, with emerging security technologies, this work is becoming more difficult by encrypted communication such as Tor, which is one of the most popular encryption techniques. This paper proposes an approach to classify Tor traffic using hexadecimal raw packet header and convolutional neural network model. Comparing with competitive machine learning algorithms, our approach shows a remarkable accuracy. To validate this method publicly, we use UNB-CIC Tor network traffic dataset. Based on the experiments, our approach shows 99.3% accuracy for the fractionized Tor/non-Tor traffic classification.

Tor Traffic Classification from Raw Packet Header using Convolutional Neural Network

Internet of Things (IoT) finds its applications in home, city and industrial settings. Current network is in transition to adopt fog/edge architecture for providing the capacity for IoT. However, in order to deal with the enormous amount of traffic generated by IoT devices and to reduce queuing delay, novel self-learning network management algorithms are required at fog/edge nodes. Active Queue Management (AQM) is a known intelligent packet dropping techique for differential QoS. In this paper, we propose a new AQM scheme based on Deep Reinforcement Learning (DRL) technique and introduce scaling factor in our reward function to achieve the trade-off between queuing delay and throughput. We choose Deep Q-Network (DQN) as a baseline for our scheme, and compare our approach with various AQM schemes by deploying them at the interface of fog/edge node. We simulated them by configuring different bandwidth and round trip time (RTT) values. The simulation results show that our scheme outperforms other AQM schemes in terms of delay and jitter while maintaining above-average throughput, and also verifies that AQM based on DRL is efficient in managing congestion.

Deep Reinforcement Learning Based Active Queue Management for IoT Networks

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Tor Traffic Classification from Raw Packet Header using Convolutional Neural Network

Deep Reinforcement Learning Based Active Queue Management for IoT Networks