Communication Protocol Classification Based on LSTM and DBN

Li, Wenwen; Dou, Zheng; Qi, Lin

doi:10.1109/access.2020.2979768

Cited by 10 publications

(5 citation statements)

References 43 publications

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“…In this analysis, the effective performed rate is attained with the help of maximum iteration 25 and population rate 10. Multiple contrasting approaches like Sunflower Optimization (SFO) [ 30 ], Jaya (JA) [ 31 ], Elephant Herding Optimization (EHO) [ 32 ], and AOA [ 26 ] along with classifiers like Random Forest with Support Vector Machine (RF+SVM) [ 33 ], DBN [ 28 ], LSTM [ 29 ], AL-DBN [ 34 ] were used for the calculation.…”

Section: Resultsmentioning

confidence: 99%

An automated system of intrusion detection by IoT-aided MQTT using improved heuristic-aided autoencoder and LSTM-based Deep Belief Network

Vijayan,

Sundar

2023

PLoS ONE

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The IoT offered an enormous number of services with the help of multiple applications so it faces various security-related problems and also heavy malicious attacks. Initially, the IoT data are gathered from the standard dataset as Message Queuing Telemetry Transport (MQTT) set. Further, the collected data are undergone the pre-processing stage, which is accomplished by using data cleaning and data transformation. The resultant processed data is given into two models named (i) Autoencoder with Deep Belief Network (DBN), in which the optimal features are selected from Autoencoder with the aid of Modified Archimedes Optimization Algorithm (MAOA). Further, the optimal features are subjected to the AL-DBN model, where the first classified outcomes are obtained with the parameter optimization of MAOA. Similarly, (ii) Long Short-Term Memory (LSTM) with DBN, in this model, the optimal features are chosen from LSTM with the aid of MAOA. Consequently, the optimal features are subjected into the AL-DBN model, where the second classified outcomes are acquired. Finally, the average score is estimated by two outcomes to provide the final classified result. Thus, the findings reveal that the suggested system achieves outstanding results to detect the attack significantly.

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Section: Resultsmentioning

confidence: 99%

An automated system of intrusion detection by IoT-aided MQTT using improved heuristic-aided autoencoder and LSTM-based Deep Belief Network

Vijayan,

Sundar

2023

PLoS ONE

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“…is the bias of feature map c, and K (1) is the number of convolution kernels in the first convolutional layer. f conv_2D represents the twodimensional convolution operation.…”

Section: B Algorithmmentioning

confidence: 99%

“…This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ kernels are used for down-sampling, while the output sizes are [1,8], [1,4], and [1, 1] respectively. Thus, the variable length input with size [1, (L/ 3 l=1 s l p )] can be converted to the fixed length output with size [1,13].…”

Section: B Algorithmmentioning

confidence: 99%

“…E LECTROMAGNETIC spectrum surveillance is important for electronic warfare, which is used to discover electromagnetic signals and identify target parameters. Protocol is the foundation of wireless communication system, and the identification of protocol type is an important research in electromagnetic spectrum surveillance [1]. Protocol defines the transmission data format, data content and information timing of the wireless system, that is closely related to its data structure, transmission system, data type and application type [2].…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Based Identification Method for Signal-Level Wireless Protocol

Ren

Song

et al. 2022

IEEE Access

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Electromagnetic spectrum surveillance is the basis of space environment monitoring and management, while protocol identification is one of the most important methods. Protocol identification has been applied in many fields, such as target recognition, anomaly detection, security management and information countermeasure. In the past decade, a lot of deep learning based protocol identification methods have been proposed, based on the bit-level protocol. However, the data needs to be demodulated and decoded, depending on the prior information of the target system. In this paper, we focus on signal-level protocol, and introduce the deep learning based identification method. We first develop a multi-dimensional convolutional neural network based method for feature extraction of signal-level protocol. Then, a spatial pooling convolutional neural network is introduced for variable length protocol identification. At last, an unsupervised training network is proposed for feature extraction of unlabelled signal-level protocol. The proposed methods are evaluated by using the practical wireless protocols, and the effectiveness is verified by simulation results.

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“…It is worth noting that the message distance is the basis of protocol clustering. Besides, the specifications vary greatly according to the protocol type [26]. Therefore, the distance between two different types of protocol messages is greater than the distance of the protocol messages in the same type, so it can be used as a basis for protocol clustering.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Binary Protocol Clustering Based on Maximum Sequential Patterns

Shi¹,

Ye²,

Li³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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With the rapid development of the Internet, a large number of private protocols emerge on the network. However, some of them are constructed by attackers to avoid being analyzed, posing a threat to computer network security. The blockchain uses the P2P protocol to implement various functions across the network. Furthermore, the P2P protocol format of blockchain may differ from the standard format specification, which leads to sniffing tools such as Wireshark and Fiddler not being able to recognize them. Therefore, the ability to distinguish different types of unknown network protocols is vital for network security. In this paper, we propose an unsupervised clustering algorithm based on maximum frequent sequences for binary protocols, which can distinguish various unknown protocols to provide support for analyzing unknown protocol formats. We mine the maximum frequent sequences of protocol message sets in bytes. And we calculate the fuzzy membership of the protocol message to each maximum frequent sequence, which is based on fuzzy set theory. Then we construct the fuzzy membership vector for each protocol message. Finally, we adopt K-means++ to split different types of protocol messages into several clusters and evaluate the performance by calculating homogeneity, integrity, and Fowlkes and Mallows Index (FMI). Besides, the clustering algorithms based on Needleman-Wunsch and the fixed-length prefix are compared with the algorithm presented in this paper. Compared with these traditional clustering methods, we demonstrate a certain improvement in the clustering performance of our work.

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Communication Protocol Classification Based on LSTM and DBN

Cited by 10 publications

References 43 publications

An automated system of intrusion detection by IoT-aided MQTT using improved heuristic-aided autoencoder and LSTM-based Deep Belief Network

An automated system of intrusion detection by IoT-aided MQTT using improved heuristic-aided autoencoder and LSTM-based Deep Belief Network

Deep Learning Based Identification Method for Signal-Level Wireless Protocol

Unsupervised Binary Protocol Clustering Based on Maximum Sequential Patterns

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