Ransomware Detection

Ransomware detection research focuses on developing robust and timely methods to identify and classify ransomware attacks, mitigating their devastating impact on individuals and organizations. Current approaches heavily leverage machine learning, employing diverse architectures such as convolutional neural networks (CNNs), decision trees, multilayer perceptrons, stacked autoencoders, and long short-term memory (LSTM) networks, often combined with feature selection techniques to improve accuracy and efficiency. These models analyze various data sources, including system calls, storage I/O traces, process memory access privileges, and resource utilization patterns, to detect malicious behavior and classify ransomware families. The ultimate goal is to create effective, real-time detection systems that are resilient to evasion techniques and minimize damage from ransomware attacks.

Papers

September 10, 2024

Ransomware Detection Using Machine Learning in the Linux Kernel
Adrian Brodzik, Tomasz Malec-Kruszyński, Wojciech Niewolski, Mikołaj Tkaczyk, Krzysztof Bocianiak, Sok-Yen Loui
Machine Learning Kernel Extension Cloud Environment Ransomware Attack Ransomware Detection Packet Classification

May 1, 2024

Detection of ransomware attacks using federated learning based on the CNN model
Hong-Nhung Nguyen, Ha-Thanh Nguyen, Damien Lescos
LeArning Abstract Data Detection Ransomware Attack Ransomware Attack Modeling Technique Ransomware Detection

March 12, 2024

WannaLaugh: A Configurable Ransomware Emulator -- Learning to Mimic Malicious Storage Traces
Dionysios Diamantopoulos, Roman Pletka, Slavisa Sarafijanovic, A. L. Narasimha Reddy, Haris Pozidis
Ransomware Attack Ransomware Attack Modeling Technique Novel Mimic Algorithm Malicious Data Ransomware Detection

February 17, 2024

Ransomware detection using stacked autoencoder for feature selection
Mike Nkongolo, Mahmut Tokmak
Long Short Term Memory Supervised Autoencoder Feature Selection Stacked Autoencoder Ransomware Detection

November 5, 2023

Ransomware Detection and Classification using Machine Learning
Kavitha Kunku, ANK Zaman, Kaushik Roy
Machine Learning Classification Code Random Forest Classifier Ransomware Attack Ransomware Detection

January 26, 2023

Minerva: A File-Based Ransomware Detector
Dorjan Hitaj, Giulio Pagnotta, Fabio De Gaspari, Lorenzo De Carli, Luigi V. Mancini
Ransomware Attack Ransomware Attack Modeling Technique Ransomware Detection

July 5, 2022

AI-based Malware and Ransomware Detection Models
Benjamin Marais, Tony Quertier, Stéphane Morucci
Deep Learning Model Malware Detection Malware Classification Ransomware Attack BotNet Detection Ransomware Detection

March 31, 2022

Ransomware Detection using Process Memory
Avinash Singh, Richard Adeyemi Ikuesan, Hein Venter
Dynamic Memory Memory Access Ransomware Attack Ransomware Attack Modeling Technique Ransomware Detection

January 12, 2022

Detecting Ransomware Execution in a Timely Manner
Anthony Melaragno, William Casey
Cyber Physical System Change Point Detection Ransomware Attack Ransomware Attack Modeling Technique Ransomware Detection