Results 111 to 120 of about 2,335 (206)

Radar: a realistic dataset for advancing ransomware detection

Cybersecurity
Ransomware threats are growing in frequency and severity, posing significant challenges to cybersecurity defences. Machine learning (ML) has gained attention as a promising tool for detecting ransomware, but the lack of realistic ransomware datasets for ...
Jamil Ispahany, Oscar Blessed Deho, Md Rafiqul Islam, M. Arif Khan, Md Zahidul Islam   +4 more
doaj   +1 more source

Study on artificial intelligence-based ransomware detection for digital substations

Ransomware is a modern form of malware that prevents victims from accessing their computer systems, important document files/folders, etc. The attacker would not release them until a ransom was paid through some secret channels.
Alvee, Syed Raqueed Bin
core  

Feature Selection with Random Forest for Ransomware Detection

Ransomware continues to be a significant cybersecurity threat, requiring advanced detection techniques to mitigate its impact. In this study, we investigate the effectiveness of feature selection using the Random Forest machine learning algorithm for ...
Liu, Qingzhong
core   +2 more sources

Ransomware Detection with Deep Neural Networks

Proceedings of the 8th International Conference on Information Systems Security and Privacy, 2022
Matan Davidian, Natalia Vanetik, Michael Kiperberg   +2 more
openaire   +1 more source

Analysis and detection of ransomware

, 2019
This phD thesis takes a look atransomware, presents an autonomous malwareanalysis platform and proposes countermeasure against these types of attacks. Our countermeasures are real-time and are deployed on a machine(i.e., end-hosts). In 2013, the ransomware become a hot subject of discussion again, before becomingone of the biggest cyberthreats ...
openaire   +1 more source

ProtectNIC - SmartNIC Ransomware Detection

Ransomware, a form of malware that restricts access to data until a ransom is paid, accounts for 20% of all cyber crimes. Although companies and organizations often require their personnel to take training for awareness of such bad actors, social ...
Xu, Anson, Choudhury, Arnav, Liu, Eason
core  

A survey on ransomware detection using AI models

Data centers and cloud environments are compromised as they are at great risk from ransomware attacks, which attack data integrity and security. Through this survey, we explore how AI, especially machine learning and deep learning (DL), is being used to ...
Gupta, Arpita, Badrinath, Goteti
core   +1 more source

Ensemble machine learning for proactive android ransomware detection using network traffic. [PDF]

Sci Rep
Kirubavathi G, Padma Mayuri B, Pranathasree S, Alagappan R, Ajayan A, Ismael WM, Rehman AU.   +6 more
europepmc   +1 more source

An Intelligent Sensing Framework for Early Ransomware Detection Using MHSA-LSTM Machine Learning. [PDF]

Sensors (Basel)
Alqahtani A, Ohemeng MO, Sheldon FT.
europepmc   +1 more source

Local And Network Ransomware Detection Comparison

, 2019
Background. Ransomware is a malicious application encrypting important files on a victim's computer. The ransomware will ask the victim for a ransom to be paid through cryptocurrency. After the system is encrypted there is virtually no way to decrypt the
Ahlgren, Filip
core