  Living-off-the-Land is an evasion technique used by attackers where native
binaries are abused to achieve malicious intent. Since these binaries are often
legitimate system files, detecting such abuse is difficult and often missed by
modern anti-virus software. This paper proposes a novel abuse detection
algorithm using raw command strings. First, natural language processing
techniques such as regular expressions and one-hot encoding are utilized for
encoding the command strings as numerical token vectors. Next, supervised
learning techniques are employed to learn the malicious patterns in the token
vectors and ultimately predict the command's label. Finally, the model is
evaluated using statistics from the training phase and in a virtual environment
to compare its effectiveness at detecting new commands to existing anti-virus
products such as Windows Defender.
