A Comprehensive Investigation on the Identification of Real and Encrypted Synthetic Network Attacks using Machine Learning Algorithms - Shukla, Pratyush; Chaudhari, Swati; Thakur, Archana (INFOCOMP Journal of Computer Science, 2025)
Network Intrusion Detection System (NIDS) are enhanced and updated consistently, but at the same the network intruders and hackers are also modernizing and renovating their methodologies. Hence, it is very important to develop novel Intrusion Detection Systems which is constructive to deal with heterogeneous network attacks. Recent research indicates that the Intrusion Detection Systems powered by Machine Learning techniques are capable to curb these issues upto great extent but still there is a long way to go. There are several distinguished models and algorithms exist which are capable to detect networks attacks. Most of the existing research is focused upon building a robust system against common and prevalent network attack categories and these approaches do not extend to some peculiar and menacing network attacks, which are often encrypted to spoof the Intrusion Detection Systems. Hence, we have proposed an effective Decision Tree Model which is capable to detect such attacks with nearly 100% accuracy. We have also investigated and presented comparative study more than 10 machine learning models, using one of the latest dataset, HIKARI-2021 [1] dataset. Moreover, the existing research work, particularly dealing with encrypted attacks do not explicitly indicate the detection accuracy of encrypted network attack category and hence we have also worked upon individual network attack categories for various machine learning approaches.