Results 71 to 80 of about 1,242 (180)

NETWORK TRAFFIC BASED RANSOMWARE DETECTION

International Education and Research Journal
Introduces a novel framework designed to bolster cybersecurity defenses against ransomware attacks. This system integrates an advanced Intrusion Detection and Prevention System (IDPS) with cutting-edge machine learning algorithms to efficiently identify and neutralize ransomware threats in real-time.
null Sivaguru R., null Srinath R., null Sathiya Rubha M., null Yasmin Banu R., null Sathish Kumar K.   +4 more
openaire   +1 more source

Earlier Decision on Detection of Ransomware Identification: A Comprehensive Systematic Literature Review

Information
Cybersecurity is normally defined as protecting systems against all kinds of cyberattacks; however, due to the rapid and permanent expansion of technology and digital transformation, the threats are also increasing. One of those new threats is ransomware,
Latifa Albshaier, Seetah Almarri, M. M. Hafizur Rahman   +2 more
doaj   +1 more source

A Multi-Classifier Network-Based Crypto Ransomware Detection System: A Case Study of Locky Ransomware

IEEE Access, 2019
Ransomware is a type of advanced malware that has spread rapidly in recent years, causing significant financial losses for a wide range of victims, including organizations, healthcare facilities, and individuals.
Ahmad O. Almashhadani, Mustafa Kaiiali, Sakir Sezer, Philip O'Kane   +3 more
doaj   +1 more source

Android Ransomware Detection Using Supervised Machine Learning Techniques Based on Traffic Analysis. [PDF]

Sensors (Basel), 2023
Albin Ahmed A, Shaahid A, Alnasser F, Alfaddagh S, Binagag S, Alqahtani D.   +5 more
europepmc   +1 more source

A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware

Future Internet
Ransomware, a significant cybersecurity threat, encrypts files and causes substantial damage, making early detection crucial yet challenging. This paper introduces a novel multi-phase framework for early ransomware detection, designed to enhance accuracy
Mazen Gazzan, Bader Alobaywi, Mohammed Almutairi, Frederick T. Sheldon   +3 more
doaj   +1 more source

Effective Ransomware Detection Using Entropy Estimation of Files for Cloud Services. [PDF]

Sensors (Basel), 2023
Lee K, Lee J, Lee SY, Yim K.
europepmc   +1 more source

IRDS4C–CTIB: A Blockchain-Driven Deception Architecture for Ransomware Detection and Intelligence Sharing

Future Internet
This paper introduces a cybersecurity framework that combines a deception-based ransomware detection system, called the Intrusion and Ransomware Detection System for Cloud (IRDS4C), with a blockchain-enabled Cyber Threat Intelligence platform (CTIB). The
Ahmed El-Kosairy, Heba Aslan, Nashwa AbdelBaki   +2 more
doaj   +1 more source

Dynamic Feature Dataset for Ransomware Detection Using Machine Learning Algorithms. [PDF]

Sensors (Basel), 2023
Herrera-Silva JA, Hernández-Álvarez M.
europepmc   +1 more source

eMIFS: A Normalized Hyperbolic Ransomware Deterrence Model Yielding Greater Accuracy and Overall Performance

Sensors
Early detection of ransomware attacks is critical for minimizing the potential damage caused by these malicious attacks. Feature selection plays a significant role in the development of an efficient and accurate ransomware early detection model.
Abdullah Alqahtani, Frederick T. Sheldon
doaj   +1 more source

Detecting Ransomware

, 2018
Ransomware attacks -- in contrast to other cyber attacks -- must not be detected and especially not blocked or recovered on first sight. This relaxation is supported by the rareness of ransomware attacks. Certainly, the uprising of ransomware families, which are able to circumvent the detection mechanism, integrated into the local machine, prevents the
openaire   +1 more source