Abstract
Ideally, in a real cyberattack, the early detection of probable hacker intent can lead to improved mitigation or prevention of exploitation. With the knowledge of basic principles of communication protocols, the reconnaissance/scanning phase intentions of a hacker can be inferred by detecting specific patterns of behavior associated with hacker tools and commands. Analyzing the reconnaissance behavior of the TCP Syn Scan between Nmap and the host, we built machine learning models incorporating the use of a filtering method we developed for labeling a dataset for detection of this behavior. We conclude that feature selection and detailed targeted labeling, based on behavior patterns, yield a high accuracy and F1 Score using Random Forest and Logistics Regression classifiers.
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