Metamorphic Malware

Metamorphic malware employs techniques to evade detection by constantly altering its code while maintaining malicious functionality. Current research focuses on developing robust machine learning models, including ensemble methods, random forests, and generative adversarial networks (GANs), to identify and classify these evolving threats, often leveraging features extracted from program behavior or code structure. This work is crucial for improving cybersecurity defenses, as the ability to accurately detect and categorize metamorphic malware is essential for mitigating its damaging effects. The development of more effective detection methods is a significant ongoing challenge in the field.

Papers

October 31, 2024

Metamorphic Malware Evolution: The Potential and Peril of Large Language Models
Pooria Madani
Full Model Full Potential Large Language Malware Analysis Code Transformation Metamorphic Malware Mutation Testing

August 23, 2024

Obfuscated Memory Malware Detection
Sharmila S P, Aruna Tiwari, Narendra S Chaudhari
Cyber Attack Malware Attack Malware Sample Metamorphic Malware Malware Feature

June 27, 2024

Synthetic Cancer -- Augmenting Worms with LLMs
Benjamin Zimmerman, David Zollikofer
Large Language Model Malware Attack Advanced Large Language Model Synthetic Tumor Metamorphic Malware Malicious Attachment

September 25, 2023

On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectors
Trong-Nghia To, Danh Le Kim, Do Thi Thu Hien, Nghi Hoang Khoa, Hien Do Hoang, Phan The Duy, Van-Hau Pham
Adversarial Attack Diverse Ensemble Adversarial Sample Malware Detection Malware Analysis Based Malware Metamorphic Malware

September 4, 2023

MalwareDNA: Simultaneous Classification of Malware, Malware Families, and Novel Malware
Maksim E. Eren, Manish Bhattarai, Kim Rasmussen, Boian S. Alexandrov, Charles Nicholas
Machine Learning Feature Selection Malware Attack Malware Family Novel Malware Metamorphic Malware

January 30, 2023

Behavioural Reports of Multi-Stage Malware
Marcus Carpenter, Chunbo Luo
Malware Detection Intrusion Detection System Behavioral Data Malware Sample Metamorphic Malware

December 12, 2022

Machine Learning for Detecting Malware in PE Files
Collin Connors, Dilip Sarkar
Machine Learning Malware Detection Malware Attack Cyber Threat Based Malware Metamorphic Malware

May 24, 2022

Fast & Furious: Modelling Malware Detection as Evolving Data Streams
Fabrício Ceschin, Marcus Botacin, Heitor Murilo Gomes, Felipe Pinagé, Luiz S. Oliveira, André Grégio
Concept Drift Malware Detection Malware Classifier Concept Drift Detection Metamorphic Malware

April 12, 2022

Malware Analysis with Symbolic Execution and Graph Kernel
Charles-Henry Bertrand Van Ouytsel, Axel Legay
Graph Kernel Graph Based Learning Malware Analysis Symbolic Execution Metamorphic Malware