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

November 3, 2022

Reliable Malware Analysis and Detection using Topology Data Analysis
Lionel Nganyewou Tidjon, Foutse Khomh
Data Detection Topological Data Analysis Malware Detection Malware Family Malware Analysis Malware Feature

October 25, 2022

Flexible Android Malware Detection Model based on Generative Adversarial Networks with Code Tensor
Zhao Yang, Fengyang Deng, Linxi Han
Generative Adversarial Network Malware Detection Android Malware Tensor Code

October 12, 2022

The State-of-the-Art in AI-Based Malware Detection Techniques: A Review
Adam Wolsey
Narrative Review State of the Art Malware Detection Artificial Intelligence Technique Malware Representation

October 1, 2022

Adversarial Attacks on Transformers-Based Malware Detectors
Yash Jakhotiya, Heramb Patil, Jugal Rawlani, Dr. Sunil B. Mane
Adversarial Attack Malware Detection

September 6, 2022

August 17, 2022

An Efficient Multi-Step Framework for Malware Packing Identification
Jong-Wouk Kim, Yang-Sae Moon, Mi-Jung Choi
Malware Detection Multi Step Malware Analysis Malware Classifier

August 15, 2022

Self-Supervised Vision Transformers for Malware Detection
Sachith Seneviratne, Ridwan Shariffdeen, Sanka Rasnayaka, Nuran Kasthuriarachchi
Self Supervised Learning Malware Detection Self Supervised Vision Transformer Malware Classifier

August 3, 2022

Design of secure and robust cognitive system for malware detection
Sanket Shukla
Adversarial Attack Product Design Malware Detection Resistive Random Access Memory Cognitive System

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 Ransomware Detection BotNet Detection

July 2, 2022

PhilaeX: Explaining the Failure and Success of AI Models in Malware Detection
Zhi Lu, Vrizlynn L. L. Thing
Artificial Intelligence Model Malware Detection AI Model Financial Success Explainable Artificial Intelligence Method Software Failure Explanation Quality Quality Explanation

June 28, 2022

Parallel Instance Filtering for Malware Detection
Martin Jureček, Olha Jurečková
Malware Detection Instance Selection

June 27, 2022

Multifamily Malware Models
Samanvitha Basole, Fabio Di Troia, Mark Stamp
Machine Learning Model Malware Detection Malware Family

June 26, 2022

Malware Detection and Prevention using Artificial Intelligence Techniques
Md Jobair Hossain Faruk, Hossain Shahriar, Maria Valero, Farhat Lamia Barsha, Shahriar Sobhan, Md Abdullah Khan, Michael Whitman, Alfredo Cuzzocreak, Dan Lo, Akond Rahman, Fan Wu
Malware Detection Artificial Intelligence Technique Effective Prevention Malicious Code

June 13, 2022

On the impact of dataset size and class imbalance in evaluating machine-learning-based windows malware detection techniques
David Illes
Global Impact Class Imbalance Malware Detection Balanced Dataset Shi Detector Dataset Size Small Scale Datasets

June 7, 2022

Marvolo: Programmatic Data Augmentation for Practical ML-Driven Malware Detection
Michael D. Wong, Edward Raff, James Holt, Ravi Netravali
Malware Detection Malware Datasets Preserving Transformation Based Malware Detection Malicious Training

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

May 17, 2022

A two-steps approach to improve the performance of Android malware detectors
Nadia Daoudi, Kevin Allix, Tegawendé F. Bissyandé, Jacques Klein
System Performance Malware Detection Android Malware Two Step

May 8, 2022

SeqNet: An Efficient Neural Network for Automatic Malware Detection
Jiawei Xu, Wenxuan Fu, Haoyu Bu, Zhi Wang, Lingyun Ying
Malware Detection Detection Model Efficient Detection Efficient Neural Network

April 16, 2022

SETTI: A Self-supervised Adversarial Malware Detection Architecture in an IoT Environment
Marjan Golmaryami, Rahim Taheri, Zahra Pooranian, Mohammad Shojafar, Pei Xiao
Internet of Thing Malware Detection Internet of Thing Environment Adversarial Malware