@inproceedings{086c19c43a9d441993a45190e1ef4b96,
title = "Deep Learning for Threat Actor Attribution from Threat Reports",
abstract = "Threat Actor Attribution is the task of identifying an attacker responsible for an attack. This often requires expert analysis and involves a lot of time. There had been attempts to detect a threat actor using machine learning techniques that use information obtained from the analysis of malware samples. These techniques will only be able to identify the attack, and it is trivial to guess the attacker because various attackers may adopt an attack method. A state-of-the-art method performs attribution of threat actors from text reports using Machine Learning and NLP techniques using Threat Intelligence reports. We use the same set of Threat Reports of Advanced Persistent Threats (APT). In this paper, we propose a Deep Learning architecture to attribute Threat actors based on threat reports obtained from various Threat Intelligence sources. Our work uses Neural Networks to perform the task of attribution and show that our method makes the attribution more accurate than other techniques and state-of-the-art methods.",
keywords = "attribution, classification, deep learning, threat actor, threat intelligence",
author = "S. Naveen and Rami Puzis and Kumaresan Angappan",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 4th International Conference on Computer, Communication and Signal Processing, ICCCSP 2020 ; Conference date: 28-09-2020 Through 29-09-2020",
year = "2020",
month = sep,
day = "28",
doi = "10.1109/ICCCSP49186.2020.9315219",
language = "English",
series = "4th International Conference on Computer, Communication and Signal Processing, ICCCSP 2020",
publisher = "Institute of Electrical and Electronics Engineers",
booktitle = "4th International Conference on Computer, Communication and Signal Processing, ICCCSP 2020",
address = "United States",
}