all InfoSec news
Machine Learning for Encrypted Malicious Traffic Detection: Approaches, Datasets and Comparative Study. (arXiv:2203.09332v1 [cs.CR])
March 18, 2022, 1:20 a.m. | Zihao Wang, Kar-Wai Fok, Vrizlynn L. L. Thing
cs.CR updates on arXiv.org arxiv.org
As people's demand for personal privacy and data security becomes a priority,
encrypted traffic has become mainstream in the cyber world. However, traffic
encryption is also shielding malicious and illegal traffic introduced by
adversaries, from being detected. This is especially so in the post-COVID-19
environment where malicious traffic encryption is growing rapidly. Common
security solutions that rely on plain payload content analysis such as deep
packet inspection are rendered useless. Thus, machine learning based approaches
have become an important direction …
detection encrypted machine machine learning malicious traffic
More from arxiv.org / cs.CR updates on arXiv.org
Jobs in InfoSec / Cybersecurity
SOC 2 Manager, Audit and Certification
@ Deloitte | US and CA Multiple Locations
Security Engineer II, Offensive Security Penetration Testing
@ Amazon.com | US, TX, Virtual Location - Texas
Cybersecurity Specialist (Security Engineering)
@ Triton AI Pte Ltd | Singapore, Singapore, Singapore
Information Systems Security Officer (ISSO)
@ ARA | Arlington, Virginia, United States
Lead - IT Risk compliance & Info Security
@ First Advantage | Bengaluru-560042, Karnataka
Embedded VSOC Analyst
@ Sibylline Ltd | Australia, Australia