A Method for Joint Detection of Attacks in Named Data Networking

The interest flooding attack (IFA) and conspiracy interest flooding attack (CIFA) are typical security threats faced by the named data networking (NDN). Aiming at the problem that existing detection methods cannot effectively identify the attack types due to single detection features and the detection rate is not high enough, this paper proposes a method based on association rule algorithm and decision tree algorithm to detect attacks in NDN. First of all, by extracting the data information in the content cache (CS) of NDN routing node, the new detection feature “CS packet growth rate” in CS is mined. It is found in the experiment that “cache growth rate” is a favorable basis for distinguishing attack types. Secondly, association rule algorithm is used to combine the new detection feature with multiple detection features in pending interest table (PIT) to find the correlation between each feature. After preprocessing the output results of multiple association rules, they are used as input into the decision tree as a training set. Finally, the detection model generated by the decision tree algorithm is used to detect the attack. This method uses decision tree algorithm and association rule algorithm to jointly detect attacks in NDN, which not only avoids misjudgment caused by single detection features, but also enriches the classification attributes of decision trees. The simulation results show that this method can accurately distinguish and detect IFA and CIFA and improve the detection rate.