Survey on network protocol reverse engineering approaches, methods and tools

Sija, Baraka D.; Goo, Young-Hoon; Kyu-Seok-Shim,; Kim, Sungyun; Choi, Mi-Jung; Kim, Myung-Sup

doi:10.1109/apnoms.2017.8094126

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…Analyzing the role of network protocol specifications in the field of network regulation can help us to obtain information about the network traffic in the target network [21,22]. By classifying the traffic generated by these protocols, network usage can be identified, network expansion plans can be developed, and bandwidth for specific protocols can be controlled.…”

Section: Introductionmentioning

confidence: 99%

From Unknown to Similar: Unknown Protocol Syntax Analysis for Network Flows in IoT

Wang

Hei

et al. 2021

Security and Communication Networks

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Internet of Things (IoT) is the development and extension of computer, Internet, and mobile communication network and other related technologies, and in the new era of development, it increasingly shows its important role. To play the role of the Internet of Things, it is especially important to strengthen the network communication information security system construction, which is an important foundation for the Internet of Things business relying on Internet technology. Therefore, the communication protocol between IoT devices is a point that cannot be ignored, especially in recent years; the emergence of a large number of botnet and malicious communication has seriously threatened the communication security between connected devices. Therefore, it is necessary to identify these unknown protocols by reverse analysis. Although the development of protocol analysis technology has been quite mature, it is impossible to identify and analyze the unknown protocols of pure bitstreams with zero a priori knowledge using existing protocol analysis tools. In this paper, we make improvements to the existing protocol analysis algorithm, summarize and learn from the experience and knowledge of our predecessors, improve the algorithm ideas based on the Apriori algorithm idea, and perform feature string finding under the idea of composite features of CFI (Combined Frequent Items) algorithm. The advantages of existing algorithm ideas are combined together to finally propose a more efficient OFS (Optimal Feature Strings) algorithm with better performance in the face of bitstream protocol feature extraction problems.

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Section: Introductionmentioning

confidence: 99%

From Unknown to Similar: Unknown Protocol Syntax Analysis for Network Flows in IoT

Wang

Hei

et al. 2021

Security and Communication Networks

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“…As a bridge of these methods, protocol can capture the lifeblood of these illegal means.Because the means of unknown network protocol is almost 0 in the existing technology, it is urgent to analyze the unknown network protocol. According to the ITRC, so far in 2018, there have been more than 1100 data leakage incidents, 6 with a total of 561 700 000 exposure records. The global average cost of data leakage research in 2018, sponsored by the Ponemon.…”

Section: Introductionmentioning

confidence: 99%

An unknown protocol syntax analysis method based on convolutional neural network

Wang

Bai

Hei

et al. 2020

Trans Emerging Tel Tech

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In recent years, a large number of botnets and dark networks rely on command and control channels of unknown protocol formats for communication, and with the development of Internet of Things technology, this problem becomes more prominent. The syntax analysis of the unknown protocol is helpful to measure the boundary of Botnet in the environment of Internet of things, so as to protect the network security. Based on the analysis of the characteristics of the current bitstream protocol data format, this article proposes an unknown protocol syntax analysis method based on convolutional neural network (CNN). First, the protocol data are preprocessed, and then the image is transformed. Next, the converted image is input to the convolution layer for convolution. After convolution, the data are flattened. Then the flattened data are put into the fully connected neural network. Finally, the unknown protocol is analyzed and predicted. The experimental results show that compared with the traditional feature extraction combine frequent item algorithm (CFI) and other neural network deep neural networks, CNN is 15% more accurate than CFI in the analysis of unknown protocol syntax, and it can accurately analyze and identify the unknown protocol.

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