An association analysis and identification for unknown protocol of bitstream oriented

Zheng, Jie

doi:10.1002/cpe.3799

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…There are no significant achievements in the analysis of such protocols in current studies. Zhang et al [29] only recognized the address field in the message format, and Zheng et al [30] could not identify the message types of the protocol.…”

Section: Related Workmentioning

confidence: 99%

Association Analysis and Identification of Unknown Bitstream Protocols Based on Composite Feature Sets

et al. 2021

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Concomitant with the rapid development of network communications technology, the analysis of communication protocols has become indispensable in the maintenance of daily network security.Common protocol analysis methods predominantly analyze protocols using known information, such as fixed port numbers; however, these methods have significant limitations. In the current network environment, the proportion of undisclosed protocols is increasing daily, and the information related to such protocols is difficult to obtain and sometimes fails because of the particularity of the unknown protocol format. Therefore, it is crucial to analyze unknown protocols in the context of less prior knowledge. To solve this problem, this paper proposes a novel protocol identification method in which association analysis and identification of unknown bitstream protocols are first carried out based on composite feature sets. Furthermore, data mining and statistics-related knowledge are applied to realize protocol message-type identification and protocol message-format analysis. The results of experiments conducted on the bitstream protocol dataset verify that the proposed method can accurately identify different message types. Specifically, taking the ICMP and ARP protocols as examples, the proposed method could effectively infer the main features, which is helpful for further protocol information extraction and analysis.INDEX TERMS Composite feature set, network communication technology, protocol message-format analysis, protocol message-type identification.

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Section: Related Workmentioning

confidence: 99%

Association Analysis and Identification of Unknown Bitstream Protocols Based on Composite Feature Sets

et al. 2021

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“…introduced one such comprehensive schemes: it takes consideration of the DG's own characteristics and its ability to support the local loading, it adopts different protection strategy, and realizes the fault isolation and island division through the coordination between the protection and the automatic devices. J. Zheng extracted the feature information and also mined the association rules to identify and mark the unknown protocol using the learning mechanism of machine. The unknown protocol in a specific environment was found and analyzed by marking the fingerprint information of protocol.…”

mentioning

confidence: 99%

Big data‐related technologies and applications

Luo

Liu

2016

Concurrency and Computation

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With the advances of information communication technologies, it is critical to improve the efficiency and accuracy of modern data processing techniques. The past decade has witnessed the tremendous technical advances in sensor networks, Internet/Web of Things, cloud computing, mobile/embedded computing, spatial/temporal data processing, and big data, and these technologies have provided new opportunities and solutions to data processing techniques. Big data is an emerging paradigm applied to datasets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. Such datasets are often from various sources (Variety) yet unstructured such as social media, sensors, scientific applications, surveillance, video and image archives, Internet texts and documents, Internet search indexing, medical records, business transactions, and web logs and are of large size (Volume) with fast data in/out (Velocity). More importantly, big data has to be of high value (Value) and establish trust in it for business decision-making (Veracity). Various technologies are being discussed to support the handling of big data such as massively parallel processing databases, scalable storage systems, cloud computing platforms, and MapReduce. Big data is more than simply a matter of size; it is an opportunity to find insights in new and emerging types of data and content, to make business more agile, and to answer questions that were previously considered beyond our reach. This special issue wants to demonstrate the emerging issues in the research of big data and approaches towards it. Original and research articles are solicited in all aspects including theoretical studies, practical applications, and experimental prototypes.The submitted manuscripts were reviewed by experts from both academia and industry. After two rounds of reviewing, the highest quality manuscripts were accepted for this special issue. Totally, we have received 20 manuscripts and 11 papers are accepted. Five papers are selected from SKG2015 conference with about 50% new content. This special issue will be published by Concurrency and Computation: Practice and Experience as special issues.In order to detect and describe the real-time urban emergency event, the knowledge base model is proposed by the paper by Z. Xu et al [1]. The crowdsourcing-based knowledge base model is introduced, which uses the information from social media. X. Lin et al [2]. introduced one such comprehensive schemes: it takes consideration of the DG's own characteristics and its ability to support the local loading, it adopts different protection strategy, and realizes the fault isolation and island division through the coordination between the protection and the automatic devices. J. Zheng [3] extracted the feature information and also mined the association rules to identify and mark the unknown protocol using the learning mechanism of machine. The unknown protocol in a specific environment was found and analyzed by marking t...

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Unknown Protocol Identification Based on Improved K-Means++ Algorithm

Feng

Man

2020

J. Phys.: Conf. Ser.

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In recent years, the gradual popularization of mobile terminals and the vigorous development of the network have spawned the birth of a new Internet structure and promoted the growth of network traffic. Behind such a large network, effective supervision of network traffic is the cornerstone of network security protection. At present, many studies on the direction of network supervision focus on the analysis of unknown network protocol types. The protocol identification method combined with machine learning is a hot topic in this kind of research. This method extracts data stream features and builds data sets, using machine learning algorithms. The model analyzes unknown network traffic and can obtain better recognition results than traditional network protocol analysis methods. Aiming at the problem of unknown traffic identification, this paper proposes a reasonable unknown traffic identification algorithm. The feature normalization preprocessing, feature selection, LOF outlier analysis, etc. are introduced. The clustering process uses the K-Means++ algorithm, and the maximum local reachable density point in the outlier analysis is used to realize the initial cluster center point. Accurate positioning.

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An association analysis and identification for unknown protocol of bitstream oriented

Cited by 3 publications

References 19 publications

Association Analysis and Identification of Unknown Bitstream Protocols Based on Composite Feature Sets

Association Analysis and Identification of Unknown Bitstream Protocols Based on Composite Feature Sets

Big data‐related technologies and applications

Unknown Protocol Identification Based on Improved K-Means++ Algorithm

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