Signature Identification and User Activity Analysis on Whatsapp Web through Network Data

Mapping Intimacies ◽

10.21203/rs.3.rs-498274/v1 ◽

2021 ◽

Author(s):

Ramraj S ◽

Usha G

Keyword(s):

Network Traffic ◽

Data Transfer ◽

Instant Messaging ◽

Traffic Analysis ◽

Traffic Classification ◽

Location Sharing ◽

Other Information ◽

The Status ◽

Voice Message ◽

User Activities

Abstract WhatsApp messenger is a popular instant messaging application that employs end-to-end encryption for communication. WhatsApp Web is the browser-based implementation of WhatsApp messenger. Users of WhatsApp communicate securely using SSL protocol. Encryption and use of common port for communication by multiple applications poses challenge in traffic classification for application identification. It is highly needed to analyze the network traffic for the purpose of QoS, Intrusion Detection and application specific traffic classification. In this paper, we have done traffic analysis on the network packets captured through data transfer in whatsapp web. In the result, we have explored the user activities such as message texting, contact sharing, voice message, location sharing, media transfer and status viewing. Packet level traffic analysis of user activities reveal patterns in the encrypted SSL communication. This pattern is identified across SSL packet lengths for WhatsApp media transfer and voice message communication. Other important features WhatsApp is the ability to view the status of the message being sent. We have identified the read and unread message status in these data packets by exposing signatures in the network layer. These signatures are identified with the help of the SSL lengths in the TLS header information of WhatsApp Web network traffic traces. Various other information on WhatsApp traffic presented in our study is relevant to the version of WhatsApp Web v0.3.2386.

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Deep Reinforcement Learning-Based Network Routing Technology for Data Recovery in Exa-Scale Cloud Distributed Clustering Systems

Applied Sciences ◽

10.3390/app11188727 ◽

2021 ◽

Vol 11 (18) ◽

pp. 8727

Author(s):

Dong-Jin Shin ◽

Jeong-Joon Kim

Keyword(s):

Reinforcement Learning ◽

Data Transfer ◽

Block Size ◽

Network Routing ◽

Erasure Coding ◽

Block Transmission ◽

Other Information ◽

The Status ◽

Link Cost ◽

Network Costs

Research has been conducted to efficiently transfer blocks and reduce network costs when decoding and recovering data from an erasure coding-based distributed file system. Technologies using software-defined network (SDN) controllers can collect and more efficiently manage network data. However, the bandwidth depends dynamically on the number of data transmitted on the network, and the data transfer time is inefficient owing to the longer latency of existing routing paths when nodes and switches fail. We propose deep Q-network erasure coding (DQN-EC) to solve routing problems by converging erasure coding with DQN to learn dynamically changing network elements. Using the SDN controller, DQN-EC collects the status, number, and block size of nodes possessing stored blocks during erasure coding. The fat-tree network topology used for experimental evaluation collects elements of typical network packets, the bandwidth of the nodes and switches, and other information. The data collected undergo deep reinforcement learning to avoid node and switch failures and provide optimized routing paths by selecting switches that efficiently conduct block transfers. DQN-EC achieves a 2.5-times-faster block transmission time and 0.4-times-higher network throughput than open shortest path first (OSPF) routing algorithms. The bottleneck bandwidth and transmission link cost can be reduced, improving the recovery time approximately twofold.

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Data Transformation Schemes for CNN-Based Network Traffic Analysis: A Survey

Electronics ◽

10.3390/electronics10162042 ◽

2021 ◽

Vol 10 (16) ◽

pp. 2042

Author(s):

Jacek Krupski ◽

Waldemar Graniszewski ◽

Marcin Iwanowski

Keyword(s):

Machine Learning ◽

Network Traffic ◽

Three Dimensional ◽

Data Transformation ◽

Traffic Analysis ◽

Traffic Classification ◽

Cyber Attack ◽

Network Traffic Classification ◽

Scientific Papers ◽

Analytical Approaches

The enormous growth of services and data transmitted over the internet, the bloodstream of modern civilization, has caused a remarkable increase in cyber attack threats. This fact has forced the development of methods of preventing attacks. Among them, an important and constantly growing role is that of machine learning (ML) approaches. Convolutional neural networks (CNN) belong to the hottest ML techniques that have gained popularity, thanks to the rapid growth of computing power available. Thus, it is no wonder that these techniques have started to also be applied in the network traffic classification domain. This has resulted in a constant increase in the number of scientific papers describing various approaches to CNN-based traffic analysis. This paper is a survey of them, prepared with particular emphasis on a crucial but often disregarded aspect of this topic—the data transformation schemes. Their importance is a consequence of the fact that network traffic data and machine learning data have totally different structures. The former is a time series of values—consecutive bytes of the datastream. The latter, in turn, are one-, two- or even three-dimensional data samples of fixed lengths/sizes. In this paper, we introduce a taxonomy of data transformation schemes. Next, we use this categorization to describe various CNN-based analytical approaches found in the literature.

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