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The rapidly increasing number of smart devices on the Internet necessitatesan efficient inspection system for safeguarding our networks from suspiciousactivities such as Address Resolution Protocol (ARP) probes. In this research,we analyze sequence data of ARP traffic on LAN based on the numerical count anddegree of its packets. Moreover, a dynamic threshold is employed to detectunderlying suspicious activities, which are further converted into featurevectors to train an unsupervised autoencoder neural network. Then, we leverageK-means clustering to separate the extracted latent features of suspiciousactivities from the autoencoder into various patterns. Besides, to evaluate theperformance, we collect and adopt a real-world network traffic dataset fromfive different LANs. At last, we successfully detect suspicious ARP patternsvarying in scale, lifespan, and regularity on the LANs.

Suspicious ARP Activity Detection and Clustering Based on Autoencoder Neural Networks