@inproceedings{1b9923bbe17247269e66216d01c3471e,
title = "Early Detection of In-Memory Malicious Activity Based on Run-Time Environmental Features",
abstract = "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.",
keywords = "Early detection, In-memory attacks, Malware detection",
author = "Dorel Yaffe and Danny Hendler",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 5th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2021 ; Conference date: 08-07-2021 Through 09-07-2021",
year = "2021",
month = jan,
day = "1",
doi = "10.1007/978-3-030-78086-9_29",
language = "English",
isbn = "9783030780852",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "397--404",
editor = "Shlomi Dolev and Oded Margalit and Benny Pinkas and Alexander Schwarzmann",
booktitle = "Cyber Security Cryptography and Machine Learning - 5th International Symposium, CSCML 2021, Proceedings",
address = "Germany",
}