@inproceedings{46320690bdd94c008061692e8dc725a0,
title = "Anomaly Detection to Protect Networks from Advanced Persistent Threats Using Adaptive Resonance AI Concepts",
abstract = "In this paper, we will improve the Advanced Persistent Threats (APT) attack detection rate accuracy by using an artificial intelligence based anomalous intrusion detection that will be based on unsupervised learning techniques. This system will be mainly network-based with a thin layer running on the host device. We plan to mainly use an unsupervised artificial intelligence technique that utilizes Adaptive Resonance theory that will be paired with a signature-based system that will filter anomalous data and significantly improve detection rates and decrease false positive rates compared to typical anomalous intrusion detection system (IDS). If proven here, this system could be applied to future IDS and can significantly increase overall network security for an organization.",
author = "Syed Rizvi and Tanner Flock and Travis Flock and Iyonna Williams",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE; 6th International Conference on Software Security and Assurance, ICSSA 2020 ; Conference date: 28-10-2020 Through 30-10-2020",
year = "2020",
doi = "10.1109/ICSSA51305.2020.00018",
language = "English (US)",
series = "Proceedings - 2020 International Conference on Software Security and Assurance, ICSSA 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "60--65",
booktitle = "Proceedings - 2020 International Conference on Software Security and Assurance, ICSSA 2020",
address = "United States",
}