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Authors
Abstract(s)
Advanced Persistent Threat (APT) have become one of the primary challenges in cyber defense. Charac
terized by sophisticated and prolonged attacks, these threats infiltrate networks aiming to steal sensitive
data, often remaining undetected for extended periods. This evolution in attack tactics underscores the
urgent need for improvements in defense strategies and threat detection.
Within the scope of this thesis, a framework named Advanced Persistent Threat Stage Prediction
(APTSP) was developed. APTSP is capable of predicting, based on identified threats, the current stage
of the attack, as well as the most likely subsequent stage. It also provides insights into the most probable
perpetrating APT group, considering known APTs. To achieve this, APTSP takes network data classified
by an Intrusion Detection System (IDS) and applies a Markov model to determine the probabilities for
the APT stages. It also uses a machine learning model to identify the potential agent responsible for the
attack.
APTSP was experimentally evaluated on a public dataset, comparing its results with different solu
tions. APTSP outperformed previous approaches in all the metrics used.
Description
Keywords
Advanced Persistent Threat (APT) Markov model stage of the attack identify the potential agent cyber defense.