Foundational Models for Malware Embeddings Using Spatio-Temporal Parallel Convolutional Networks. (arXiv:2305.15488v1 [cs.CR])

In today's interconnected digital landscape, the proliferation of malware
poses a significant threat to the security and stability of computer networks
and systems worldwide. As the complexity of malicious tactics, techniques, and
procedures (TTPs) continuously grows to evade detection, so does the need for
advanced methods capable of capturing and characterizing malware behavior. The
current state of the art in malware classification and detection uses task
specific objectives; however, this method fails to generalize to other
downstream tasks involving the …

advanced complexity computer detection digital evade malicious malware networks procedures proliferation security systems tactics techniques temporal threat ttps