Seq2Img: A sequence-to-image based approach towards IP traffic classification using convolutional neural networks

Chen, Zhitang; He, Ke; Li, Jian; Geng, Yanhui

doi:10.1109/bigdata.2017.8258054

Cited by 100 publications

(61 citation statements)

References 15 publications

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“…They evaluate their model on an encrypted application dataset of 12 classes and show a significant improvement over C4.5 approach that use time series and statistical features. In [8], authors also use CNN with 2 convolutional, 2 pooling, and 3 fully connected layers for protocol and application classification tasks. They use reproducing kernel Hilbert space (RKHS) embedding and convert the early time series data into 2-D images.…”

Section: B Convolutional Neural Networkmentioning

confidence: 99%

“…Time Series+Header: Since time series features are barely affected by encryption, it has been widely applied to various applications and datasets. The first few packets, from 10 to 30 packets, are reported to be enough for classification in many datasets [8], [12]. Sampled packets from the entire flow are also shown to achieve promising accuracy [3].…”

Section: F Model Selectionmentioning

confidence: 99%

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Deep Learning for Encrypted Traffic Classification: An Overview

2019

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Traffic classification has been studied for two decades and applied to a wide range of applications from QoS provisioning and billing in ISPs to security-related applications in firewalls and intrusion detection systems. Port-based, data packet inspection, and classical machine learning methods have been used extensively in the past, but their accuracy have been declined due to the dramatic changes in the Internet traffic, particularly the increase in encrypted traffic. With the proliferation of deep learning methods, researchers have recently investigated these methods for traffic classification task and reported high accuracy. In this article, we introduce a general framework for deep-learning-based traffic classification. We present commonly used deep learning methods and their application in traffic classification tasks. Then, we discuss open problems and their challenges, as well as opportunities for traffic classification.

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Section: B Convolutional Neural Networkmentioning

confidence: 99%

Section: F Model Selectionmentioning

confidence: 99%

Deep Learning for Encrypted Traffic Classification: An Overview

2019

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“…Trained by different learning algorithms, AppScanner can reach the highest accuracy of 99.8% on 110 apps. Chen et al [17] proposed an online traffic classification framework which utilizes kernel methods and deep neural networks. They evaluated their approach on 5 different protocols and 5 mobile applications, which achieved an accuracy of 99.84% and an accuracy of 88.43% correspondingly.…”

Section: Background 21 Related Workmentioning

confidence: 99%

“…Different from those doing fingerprinting at flow level [17,41], HomeMole works at packet level, which means a lable will be given to all packets after they are processed by our models. As such, HomeMole is able to work in online mode and give prompt results of current device status.…”

Section: Adversary Modelmentioning

confidence: 99%

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

Dong

Tang

et al. 2020

Proceedings of the 15th ACM Asia Conference on Computer and Communications Security

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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. To this aim, 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.

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“…Time-series data can confirm trends in data between the past and the present, and time-series data is also sensitive to time-based information. Time-series data is largely found in domains that utilize real-time sensor data [ 16 , 17 ] such as traffic conditions [ 18 , 19 ], speech recognition [ 20 , 21 ], and weather information [ 22 , 23 ] using prediction and classification models [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. In particular, a large amount of data flows from the sensor, and data warehouse technology [ 24 ] and techniques for analyzing this type of data are being developed.…”

Section: Introductionmentioning

confidence: 99%

Resource-Efficient Pet Dog Sound Events Classification Using LSTM-FCN Based on Time-Series Data

Kim

Chung

et al. 2018

Sensors

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The use of IoT (Internet of Things) technology for the management of pet dogs left alone at home is increasing. This includes tasks such as automatic feeding, operation of play equipment, and location detection. Classification of the vocalizations of pet dogs using information from a sound sensor is an important method to analyze the behavior or emotions of dogs that are left alone. These sounds should be acquired by attaching the IoT sound sensor to the dog, and then classifying the sound events (e.g., barking, growling, howling, and whining). However, sound sensors tend to transmit large amounts of data and consume considerable amounts of power, which presents issues in the case of resource-constrained IoT sensor devices. In this paper, we propose a way to classify pet dog sound events and improve resource efficiency without significant degradation of accuracy. To achieve this, we only acquire the intensity data of sounds by using a relatively resource-efficient noise sensor. This presents issues as well, since it is difficult to achieve sufficient classification accuracy using only intensity data due to the loss of information from the sound events. To address this problem and avoid significant degradation of classification accuracy, we apply long short-term memory-fully convolutional network (LSTM-FCN), which is a deep learning method, to analyze time-series data, and exploit bicubic interpolation. Based on experimental results, the proposed method based on noise sensors (i.e., Shapelet and LSTM-FCN for time-series) was found to improve energy efficiency by 10 times without significant degradation of accuracy compared to typical methods based on sound sensors (i.e., mel-frequency cepstrum coefficient (MFCC), spectrogram, and mel-spectrum for feature extraction, and support vector machine (SVM) and k-nearest neighbor (K-NN) for classification).

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Seq2Img: A sequence-to-image based approach towards IP traffic classification using convolutional neural networks

Cited by 100 publications

References 15 publications

Deep Learning for Encrypted Traffic Classification: An Overview

Deep Learning for Encrypted Traffic Classification: An Overview

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

Resource-Efficient Pet Dog Sound Events Classification Using LSTM-FCN Based on Time-Series Data

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