A Review on Machine Learning Intrusion Detection Systems (MLIDS) in Encrypted Traffic - Harinath, K.R; Kishore Kumar, G (International Journal of Computer Sciences and Engineering, 2023)
Global connection depends on the internet and protecting it is a top priority for organizations and governments. As technology advances, so does the number of different types of network attacks. These attacks can be considered as intrusions. Due to deficiency of protection the information protection becomes onerous. To detect intrusions, a well defined intrusion detection system was utilized. It is one among the tools towards building secure system. To combat with advanced attacks and to protect the data and network, MLIDS (Machine Learning Based Intrusion Detection systems) is an advanced technology among best solutions. When accesses are encrypted, however, IDS is ineffective. Although encryption increases sender and receiver privacy, it causes an issue with inaccurate traffic categorization. There are Several ID approaches to analyse encrypted traffic interchange using data range, data similarity and data time without decryption. In this survey, paper presents a different techniques, datasets and challenges of detection over cipher text and comparative survey on machine learning algorithms from recent work.