International Conference on Computer and Knowledge Engineering

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Enhanced Autoencoder-based Clustering for Message Analysis in Binary Protocols

Authors :

Mohaddese Nemati¹ Shiva Mahmoudzadeh² Mehdi Teimouri³

1- University of Tehran 2- University of Tehran 3- University of Tehran

Keywords :

Protocol reverse engineering،Clustering،Binary protocols،Autoencoder،Affinity propagation،Mini-Batch K-Means

Abstract :

Reverse engineering is a critical process in software engineering and communication networks, playing a vital role in optimizing protocols and complex systems. By enabling the analysis and understanding of existing structures, reverse engineering enhances system performance and supports the development of innovative solutions to improve communication efficiency and security. A key aspect of protocol reverse engineering is identifying message types, which is essential for understanding both individual message functions and the overall protocol. Accurate message classification, particularly through clustering algorithms that group messages based on similarities, is crucial for achieving reliable identification results. In this study, after evaluating and comparing various methods, two superior approaches—AEMK and EAEAP—have been identified. These methods integrate two types of autoencoders with Affinity Propagation algorithms and Mini-Batch K-Means. The findings reveal that these proposed approaches significantly enhance cluster homogeneity compared to other methods, such as the PREUNN tool. Specifically, while the PREUNN tool achieves an average homogeneity of 0.193, the AEMK method reaches 0.554, and the EAEAP method achieves 0.919. These results demonstrate that the proposed methods effectively cluster binary protocols, thereby improving the accuracy of message type identification and advancing the field of protocol reverse engineering.

Papers List

List of archived papers

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Systematic review on AI techniques in detection and navigation of agricultural machines and robots

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Bridging Knowledge and Language Models in Healthcare: A RAG Survey

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GAP: Fault tolerance Improvement of Convolutional Neural Networks through GAN-aided Pruning

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Real-Time Forecasting Using Mixed Frequency Time-Series Data

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Lossless Watermarking in Encrypted Triangular Mesh Models Based on Optimized Vertex Estimation and Error Histogram Shifting

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Object Detection on Detecting Skin Lesion using Dab-DETR

Sheida Shadman - Amirreza Rouhbakhshmeghrazi - Shayan Nalbandian - Bo Li - Shaghayegh Shadman - Malik Muhammad Owais Siddique