EUVD-2021-0304

Malware in sbrugna...

EUVD-2021-0452

Malware in sbrugna...

EUVD-2021-0348

Malware in sbrugna...

EUVD-2021-0351

Malware in sbrugna...

EUVD-2021-0360

Malware in sbrugna...

EUVD-2021-0280

Malware in sbrugna...

EUVD-2021-0282

Malware in sbrugna...

EUVD-2021-0283

Malware in sbrugna...

EUVD-2021-0292

Malware in sbrugna...

EUVD-2021-0315

Malware in sbrugna...

EUVD-2021-0335

Malware in sbrugna...

EUVD-2021-0441

Malware in sbrugna...

EUVD-2021-0389

Malware in sbrugna...

EUVD-2021-0399

Malware in sbrugna...

EUVD-2020-18145

Malware in sbrugna...

EUVD-2021-0406

Malware in sbrugna...

EUVD-2021-0367

Malware in sbrugna...

EUVD-2021-0263

Malware in sbrugna...

How we trained an ML model to detect DLL hijacking

DLL hijacking is a common technique in which attackers replace a library called by a legitimate process with a malicious one. It is used by both creators of mass-impact malware, like stealers and banking Trojans, and by APT and cybercrime groups behind targeted attacks. In recent years, the numbe...

Detecting DLL hijacking with machine learning: real-world cases

Introduction Our colleagues from the AI expertise center recently developed a machine-learning model that detects DLL-hijacking attacks. We then integrated this model into the Kaspersky Unified Monitoring and Analysis Platform SIEM system. In a separate article, our colleagues shared how the mode...