| Named Data Networking(NDN)is a phenomenal architecture for future Internet developed to thrash the shortcoming of the current IP-based Internet.NDN aims to improve reliability,scalability,and security by the way that information is retrieved.The key feature of the NDN is in-network caching that is every router has its own cache to store data for future use and thus improve the usage of the network bandwidth and reduce the network latency.However,in-network caching increases the security risks,namely cache pollution attacks(CPA),including locality disruption(ruining the cache locality by sending random requests for unpopular contents to make them popular),and False Locality(introducing unpopular contents in the router's cache by sending requests for a set of unpopular contents).In this dissertation,we propose a machine learning method,named Triangle Area Based Multivariate Correlation Analysis(TAB-MCA),to detect the cache pollution attacks in NDN.This detection system has two parts,the triangle-area-based MCA technique and the threshold-based anomaly detection technique.The TAB-MCA technique is used to extract hidden geometrical correlations between two distinct features for all possible permutations and the threshold-based anomaly detection technique helps our model to be able to distinguish attacks from legitimate traffic records without requiring prior knowledge.Our technique detects both types of CPA,locality disruption and false locality and the simultaneous occurrence of both of them with more accuracy.Implementation on XC-topology,proposed method shows high efficiency in mitigating these attacks.In comparison to other ML-methods,our proposed method has low overhead cost in mitigating CPA as it doesn't require attackers' prior knowledge.Secondly,our method can also detect non-uniform attack distributions. |
