Mining Network Traffic with the k‐Means Clustering Algorithm for Stepping‐Stone Intrusion Detection
Lixin Wang, Jianhua Yang, Xiaohua Xu, Peng‐Jun Wan · Wireless Communications and Mobile Computing · 2021
Intruders on the Internet usually launch network attacks through compromised hosts, called stepping stones, in order to reduce the chance of being detected. With stepping‐stone intrusions, an attacker uses tools such as SSH to log in several compromised hosts remotely and create an interactive connection chain and then sends attacking packets to a target system. An effective method to detect such an intrusion is to estimate the length of a connection chain. In this paper, we develop an efficient algorithm to detect stepping‐stone intrusion by mining network traffic using the k‐means clustering. Existing approaches for connection‐chain‐based stepping‐stone intrusion detection either are not effective or require a large number of TCP packets to be captured and processed and, thus, are not efficient. Our proposed detection algorithm can accurately determine the length of a connection chain without requiring a large number of TCP packets being captured and processed, so it is more efficient. Our proposed detection algorithm is also easier to implement than all existing approaches for stepping‐stone intrusion detection. The effectiveness, correctness, and efficiency of our proposed detection algorithm are verified through well‐designed network experiments.