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

February 8, 2022

IoT Malware Detection Architecture using a Novel Channel Boosted and Squeezed CNN
Muhammad Asam, Saddam Hussain Khan, Tauseef Jamal, Asifullah Khan
Deep Convolutional Neural Network Malware Detection Channel Importance

January 20, 2022

RoboMal: Malware Detection for Robot Network Systems
Upinder Kaur, Haozhe Zhou, Xiaxin Shen, Byung-Cheol Min, Richard M. Voyles
Malware Detection Robot Network Robotics Software

December 25, 2021

An Ensemble of Pre-trained Transformer Models For Imbalanced Multiclass Malware Classification
Ferhat Demirkıran, Aykut Çayır, Uğur Ünal, Hasan Dağ
Deep Learning Model Diverse Ensemble Pre Trained Transformer Malware Detection Malware Classification Malware Family Malware Classifier Forest Classifier

November 30, 2021

Benchmark Static API Call Datasets for Malware Family Classification
Berkant Düzgün, Aykut Çayır, Ferhat Demirkıran, Ceyda Nur Kahya, Buket Gençaydın, Hasan Dağ
Malware Detection Malware Attack Static Analysis Malware Family Malware Sample

November 8, 2021

HAPSSA: Holistic Approach to PDF Malware Detection Using Signal and Statistical Analysis
Tajuddin Manhar Mohammed, Lakshmanan Nataraj, Satish Chikkagoudar, Shivkumar Chandrasekaran, B. S. Manjunath
Malware Detection Abnormal Signal Holistic Approach Malware Analysis Statistical Analysis Novel Malware Malware Sample Malicious Attachment

November 3, 2021

A Survey of Machine Learning Algorithms for Detecting Malware in IoT Firmware
Erik Larsen, Korey MacVittie, John Lilly
Timely Survey Machine Learning Algorithm Malware Detection Internet of Thing Device Random Forest Classifier IoT Firmware