3407 matches found
EUVD-2018-15611
Malware in sbrugna...
EUVD-2019-18150
Malware in sbrugna...
EUVD-2020-18145
Malware in sbrugna...
EUVD-2021-0418
Malware in sbrugna...
EUVD-2021-0254
Malware in sbrugna...
EUVD-2021-0449
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-2024-1835
Malicious code in bioql PyPI...
EUVD-2022-0317
Malicious code in bioql PyPI...
EUVD-2025-23165
Malicious code in bioql PyPI...
EUVD-2022-0313
Malicious code in bioql PyPI...
EUVD-2022-0312
Malicious code in bioql PyPI...
EUVD-2022-38793
Malicious code in bioql PyPI...
EUVD-2022-6903
Malicious code in bioql PyPI...
EUVD-2022-6970
Malicious code in bioql PyPI...
EUVD-2022-6739
Malicious code in bioql PyPI...