Malware Attack

Malware attacks, aiming to compromise systems and data, are a persistent cybersecurity threat. Current research focuses on improving malware detection and classification using machine learning, particularly employing techniques like multi-label classification, random forests, and deep learning models (including convolutional and message-passing neural networks) to analyze diverse data sources such as memory dumps, network traffic, and code features. These advancements are crucial for enhancing system security and mitigating the significant economic and societal impacts of malware infections. The development of robust, adaptable defense mechanisms against increasingly sophisticated and obfuscated malware remains a central challenge.

Papers

October 31, 2024

Assessing the Impact of Packing on Machine Learning-Based Malware Detection and Classification Systems
Daniel Gibert, Nikolaos Totosis, Constantinos Patsakis, Giulio Zizzo, Quan Le
Data Detection New Machine Malware Detection Malware Attack Static Analysis Classification System Novel Malware

October 19, 2024

A Novel Reinforcement Learning Model for Post-Incident Malware Investigations
Dipo Dunsin, Mohamed Chahine Ghanem, Karim Ouazzane, Vassil Vassilev
Q Learning Comprehensive Investigation Novel Reinforcement Learning Malware Attack Malware Representation Multi Purpose Malware

September 25, 2024

Examining the Rat in the Tunnel: Interpretable Multi-Label Classification of Tor-based Malware
Ishan Karunanayake, Mashael AlSabah, Nadeem Ahmed, Sanjay Jha
Malware Attack Malware Classification Interpretable Classifier Malicious Traffic Rat Image ToR Network

August 23, 2024

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

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

April 3, 2024

Obfuscated Malware Detection: Investigating Real-world Scenarios through Memory Analysis
S M Rakib Hasan, Aakar Dhakal
Memory Trace Malware Detection Obfuscation Technique Malware Attack Real World Scenario Fading Memory

March 18, 2024

Shifting the Lens: Detecting Malware in npm Ecosystem with Large Language Models
Nusrat Zahan, Philipp Burckhardt, Mikola Lysenko, Feross Aboukhadijeh, Laurie Williams
Camera Lens Supply Chain Malware Attack Malware Classification Open Source Ecosystem

March 6, 2024

Do You Trust Your Model? Emerging Malware Threats in the Deep Learning Ecosystem
Dorjan Hitaj, Giulio Pagnotta, Fabio De Gaspari, Sediola Ruko, Briland Hitaj, Luigi V. Mancini, Fernando Perez-Cruz
Deep Learning Model Full Model Malware Attack

January 30, 2024

ActDroid: An active learning framework for Android malware detection
Ali Muzaffar, Hani Ragab Hassen, Hind Zantout, Michael A Lones
Active Learning Concept Drift Malware Detection Active Learning Framework Malware Attack Android Malware

December 19, 2023

Towards an in-depth detection of malware using distributed QCNN
Tony Quertier, Grégoire Barrué
Quantum Physic Quantum Algorithm Quantum Machine Malware Attack Multiple Physical Qubits

December 6, 2023

Feature Analysis of Encrypted Malicious Traffic
Anish Singh Shekhawat, Fabio Di Troia, Mark Stamp
Machine Learning Model Traffic Data Malware Attack Malicious Traffic Feature Mining Malicious Data

November 20, 2023

Machine learning-based malware detection for IoT devices using control-flow data
Gergely Hevesi
New Machine Malware Detection Internet of Thing Device Malware Attack Botnet Related Malware

September 24, 2023

Smart OMVI: Obfuscated Malware Variant Identification using a novel dataset
Suleman Qamar
Novel Dataset Cyber Attack Malware Attack Malware Analysis Malware Datasets

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

August 31, 2023

The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learning
Maria Rigaki, Sebastian Garcia
Adversarial Attack Real Power Model Based Reinforcement Learning Malware Detection Malware Attack Adversarial Malware

August 19, 2023

A Comparison of Adversarial Learning Techniques for Malware Detection
Pavla Louthánová, Matouš Kozák, Martin Jureček, Mark Stamp
Adversarial Attack Adversarial Learning Consistent Comparison Malware Detection Malware Attack Adversarial Method Adversarial Malware

August 17, 2023

RatGPT: Turning online LLMs into Proxies for Malware Attacks
Mika Beckerich, Laura Plein, Sergio Coronado
Generative AI Cybersecurity Perspective Malware Attack Proxy Dataset State of the Art Large Malicious Sample Malicious Content

June 27, 2023

RansomAI: AI-powered Ransomware for Stealthy Encryption
Jan von der Assen, Alberto Huertas Celdrán, Janik Luechinger, Pedro Miguel Sánchez Sánchez, Gérôme Bovet, Gregorio Martínez Pérez, Burkhard Stiller
Deep Q Learning Malware Attack Ransomware Attack Ransomware Attack Modeling Technique

June 23, 2023

Creating Valid Adversarial Examples of Malware
Matouš Kozák, Martin Jureček, Mark Stamp, Fabio Di Troia
Adversarial Attack Adversarial Example Malware Attack Malware Analysis Adversarial Malware

June 12, 2023

On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities
Masoud Mehrabi Koushki, Ibrahim AbuAlhaol, Anandharaju Durai Raju, Yang Zhou, Ronnie Salvador Giagone, Huang Shengqiang
Machine Learning Timely Survey Technical Challenge New Machine Machine Learning Algorithm Malware Detection Malware Attack Android Malware Energy Pipeline