EUVD-2021-0266

Malware in sbrugna...

EUVD-2021-0426

Malware in sbrugna...

EUVD-2021-0327

Malware in sbrugna...

EUVD-2021-0360

Malware in sbrugna...

EUVD-2021-0324

Malware in sbrugna...

EUVD-2021-0441

Malware in sbrugna...

EUVD-2021-0283

Malware in sbrugna...

EUVD-2021-0349

Malware in sbrugna...

EUVD-2021-0435

Malware in sbrugna...

EUVD-2021-0333

Malware in sbrugna...

EUVD-2021-0321

Malware in sbrugna...

EUVD-2021-0292

Malware in sbrugna...

EUVD-2021-0377

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...

Pilot Contamination Attacks Detection with Machine Learning for Multi-User Massive MIMO

Massive multiple-input multiple-output MMIMO is essential to modern wireless communication systems, like 5G and 6G, but it is vulnerable to active eavesdropping attacks. One type of such attack is the pilot contamination attack PCA, where a malicious user copies pilot signals from an authentic us...

EUVD-2023-1006

Malicious code in bioql PyPI...

EUVD-2022-6837

Malicious code in bioql PyPI...

EUVD-2022-6859

Malicious code in bioql PyPI...

EUVD-2022-5149

Malicious code in bioql PyPI...