  In recent years malware has become increasingly sophisticated and difficult
to detect prior to exploitation. While there are plenty of approaches to
malware detection, they all have shortcomings when it comes to identifying
malware correctly prior to exploitation. The trade-off is usually between false
positives, causing overhead, preventing normal usage and the risk of letting
the malware execute and cause damage to the target. We present a novel
end-to-end solution for in-memory malicious activity detection done prior to
exploitation by leveraging machine learning capabilities based on data from
unique run-time logs, which are carefully curated in order to detect malicious
activity in the memory of protected processes. This solution achieves reduced
overhead and false positives as well as deployment simplicity. We implemented
our solution for Windows-based systems, employing multi disciplinary knowledge
from malware research, machine learning, and operating system internals. Our
experimental evaluation yielded promising results. As we expect future
sophisticated malware may try to bypass it, we also discuss how our solution
can be extended to thwart such bypassing attempts.
