Malware Detection

Malware detection research aims to develop robust and efficient methods for identifying malicious software, focusing on overcoming challenges like obfuscation and the emergence of novel attack techniques. Current efforts concentrate on leveraging deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and large language models (LLMs), often incorporating techniques like transfer learning, self-supervised learning, and few-shot learning to improve accuracy and generalization. These advancements are crucial for enhancing cybersecurity defenses across various platforms (Windows, Android, IoT) and mitigating the ever-evolving threat landscape, with a growing emphasis on explainable AI to increase transparency and trust in automated detection systems.

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 14, 2024

Deep Learning Based XIoT Malware Analysis: A Comprehensive Survey, Taxonomy, and Research Challenges
Rami Darwish, Mahmoud Abdelsalam, Sajad Khorsandroo
Deep Learning Comprehensive Survey Comprehensive Taxonomy Malware Detection IOT Technology Malware Family Research Challenge

October 11, 2024

MOZART: Ensembling Approach for COVID-19 Detection using Chest X-Ray Imagery
Mohammed Shabo, Nazar Siddig
Convolutional Neural Network Constructive Approach Chest X Ray Malware Detection COVID 19 Detection Visual Imagery

September 28, 2024

Accelerating Malware Classification: A Vision Transformer Solution
Shrey Bavishi, Shrey Modi
Malware Detection Malware Classification Novel Architecture Vision Transformer Model Malware Classifier Threat Landscape

September 26, 2024

Revolutionizing Payload Inspection: A Self-Supervised Journey to Precision with Few Shots
Kyle Stein, Arash Mahyari, Guillermo Francia III, Eman El-Sheikh
Malware Detection Multidimensional Local Precision Rate Deep Packet Inspection

September 22, 2024

A Visualized Malware Detection Framework with CNN and Conditional GAN
Fang Wang (Florence Wong), Hussam Al Hamadi, Ernesto Damiani
Synthetic Image CNN Network Conditional Generative Adversarial Network Malware Detection Detection Model Conditional GAN Malware Analysis

September 17, 2024

Towards Novel Malicious Packet Recognition: A Few-Shot Learning Approach
Kyle Stein, Andrew A. Mahyari, Guillermo Francia III, Eman El-Sheikh
Malware Detection Learning Approach Malware Representation Deep Packet Inspection Packet Classification Malware Feature

September 9, 2024

Explainable Malware Analysis: Concepts, Approaches and Challenges
Harikha Manthena, Shaghayegh Shajarian, Jeffrey Kimmell, Mahmoud Abdelsalam, Sajad Khorsandroo, Maanak Gupta
Technical Challenge Constructive Approach Malware Detection Concept Identification Malware Classification Malware Analysis Based Malware Detection

July 27, 2024

A Survey of Malware Detection Using Deep Learning
Ahmed Bensaoud, Jugal Kalita, Mahmoud Bensaoud
Deep Learning Timely Survey Malware Detection Malware Datasets

July 7, 2024

Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach
Oladipo A. Madamidola, Felix Ngobigha, Adnane Ez-zizi
Machine Learning Malware Detection Malware Feature

May 23, 2024

May 15, 2024

Transfer Learning in Pre-Trained Large Language Models for Malware Detection Based on System Calls
Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdrán, Gérôme Bovet, Gregorio Martínez Pérez
Large Language Model Transfer Learning Pre Trained Malware Detection Vulnerability Detection

May 9, 2024

Deep Multi-Task Learning for Malware Image Classification
Ahmed Bensaoud, Jugal Kalita
Multi Task Learning Multi Task Malware Detection Multi Task Learning Framework Malware Classification Malware Feature

May 7, 2024

Leveraging LSTM and GAN for Modern Malware Detection
Ishita Gupta, Sneha Kumari, Priya Jha, Mohona Ghosh
Deep Learning Deep Learning Model GAN Model LSTM Network Malware Detection

May 6, 2024

May 5, 2024

Explainable Malware Detection with Tailored Logic Explained Networks
Peter Anthony, Francesco Giannini, Michelangelo Diligenti, Martin Homola, Marco Gori, Stefan Balogh, Jan Mojzis
Interpretable Model Malware Detection Interpretable Neural Network Human Understandable Explanation

May 1, 2024

Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing
Daniel Gibert, Luca Demetrio, Giulio Zizzo, Quan Le, Jordi Planes, Battista Biggio
Malware Detection Adversarial Patch Adversarial Text Smoothing Factor Patch Attack Insertion Based

April 29, 2024

Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Research
Daniel Gibert
Deep Learning Machine Learning Classification Code NCD Method Malware Detection Based Malware Current Research

Malware Detection

Papers

Assessing the Impact of Packing on Machine Learning-Based Malware Detection and Classification Systems

Deep Learning Based XIoT Malware Analysis: A Comprehensive Survey, Taxonomy, and Research Challenges

MOZART: Ensembling Approach for COVID-19 Detection using Chest X-Ray Imagery

Accelerating Malware Classification: A Vision Transformer Solution

Revolutionizing Payload Inspection: A Self-Supervised Journey to Precision with Few Shots

A Visualized Malware Detection Framework with CNN and Conditional GAN

Towards Novel Malicious Packet Recognition: A Few-Shot Learning Approach

Explainable Malware Analysis: Concepts, Approaches and Challenges

A Survey of Malware Detection Using Deep Learning

Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach

A New Formulation for Zeroth-Order Optimization of Adversarial EXEmples in Malware Detection

SLIFER: Investigating Performance and Robustness of Malware Detection Pipelines

Transfer Learning in Pre-Trained Large Language Models for Malware Detection Based on System Calls

Deep Multi-Task Learning for Malware Image Classification

Leveraging LSTM and GAN for Modern Malware Detection

Detecting Android Malware: From Neural Embeddings to Hands-On Validation with BERTroid

Online Clustering of Known and Emerging Malware Families

Explainable Malware Detection with Tailored Logic Explained Networks

Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing

Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Research