Detection of Web Attacks via PART Classifier - Ahmed, Omar Iskndar; Varol, Cihan (Ieee, 2021)
With the vast and continuous growth in the both computers and communications fields, despite its facilitation of work at all levels, there a number of new challenges the society is facing. The most important of which is the security of sensitive data. With so many hackers wanting to steal sensitive information and exploit it for their own unethical purposes, new protection techniques have to be found. In recent years, Intrusion Detection System (IDS) technology has emerged as an effective option for protecting information within the network. This technology can distinguish between normal traffic and intrusion within the network. In this study, the PART-machine learning classifier algorithm was used to detect web attack attempts based on one of the most recent dataset CICIDS2017. The classifier achieved more than 99% accuracy. RandomForest, NaiveBayes and BayesNet algorithms are also tested for comparison purpose.
9th International Symposium on Digital Forensics and Security (ISDFS) -- JUN 28-29, 2021 -- Firat Univ, Elazig, TURKEY
Keywords: CICIDS2017 dataset; IDS; PART; Web attack; WEKA