Web: http://arxiv.org/abs/2204.09502

April 29, 2022, 1:20 a.m. | Rahim Taheri

cs.CR updates on arXiv.org arxiv.org

A rising number of botnet families have been successfully detected using deep
learning architectures. While the variety of attacks increases, these
architectures should become more robust against attacks. They have been proven
to be very sensitive to small but well constructed perturbations in the input.
Botnet detection requires extremely low false-positive rates (FPR), which are
not commonly attainable in contemporary deep learning. Attackers try to
increase the FPRs by making poisoned samples. The majority of recent research
has focused on …

botnet detection system

More from arxiv.org / cs.CR updates on arXiv.org

Information Systems Security Officer (ISSO)

@ Spry Methods | Denver, CO

Client Manager - Cybersecurity - Nashville Enterprise

@ Optiv | Nashville, TN

Threat Analyst | Remote, USA

@ Optiv | Minneapolis, MN

Senior Cyber Security SME

@ Node.Digital | Dulles, Virginia, United States

Junior Security Engineer, Applications

@ BetterHelp | Mountain View, California, United States

Information Security Analyst II

@ SOPHiA GENETICS | Lausanne, Vaud, Switzerland

Product Security Engineer

@ Elastic | United States

Senior Network Exploitation Analyst

@ Barbaricum | Washington, DC

Junior Security Engineer, Blue Team

@ BetterHelp | Mountain View, California, United States

Security Analyst, Security Operations (Threat Hunting, Operations, and Response)

@ GitHub | Remote - US

Security Engineer III - Information Security, Active Directory

@ Riot Games, Inc. | Los Angeles, USA

Staff Security Engineer, Application Security

@ Lyft | Mexico City, Mexico