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

June 9, 2023

A Survey on Cross-Architectural IoT Malware Threat Hunting
Anandharaju Durai Raju, Ibrahim Abualhaol, Ronnie Salvador Giagone, Yang Zhou, Shengqiang Huang
Timely Survey Internet of Thing Device Malware Attack Malware Analysis

May 2, 2023

MDENet: Multi-modal Dual-embedding Networks for Malware Open-set Recognition
Jingcai Guo, Yuanyuan Xu, Wenchao Xu, Yufeng Zhan, Yuxia Sun, Song Guo
Recognition Rate Open Set Recognition Malware Attack Modal Embeddings Malware Representation Malware Feature

April 14, 2023

Combining Generators of Adversarial Malware Examples to Increase Evasion Rate
Matouš Kozák, Martin Jureček
Adversarial Attack Malware Detection Malware Attack Malware Analysis Adversarial Malware Multi Layer Generator

February 11, 2023

Sequential Embedding-based Attentive (SEA) classifier for malware classification
Muhammad Ahmed, Anam Qureshi, Jawwad Ahmed Shamsi, Murk Marvi
Natural Language Processing Simple Classifier Malware Detection Upper Ocean Malware Attack Malware Classification Effective Classifier

January 30, 2023

Investigating Feature and Model Importance in Android Malware Detection: An Implemented Survey and Experimental Comparison of ML-Based Methods
Ali Muzaffar, Hani Ragab Hassen, Hind Zantout, Michael A Lones
Machine Learning Model Random Forest Content Based Feature Malware Attack High Detection Android Malware Experimental Comparison Model Importance

January 28, 2023

Harnessing the Power of Decision Trees to Detect IoT Malware
Marwan Omar
Data Detection Real Power Decision Tree Internet of Thing Malware Attack Malware Family

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

December 5, 2022

From Malware Samples to Fractal Images: A New Paradigm for Classification. (Version 2.0, Previous version paper name: Have you ever seen malware?)
Ivan Zelinka, Miloslav Szczypka, Jan Plucar, Nikolay Kuznetsov
Classification Code Malware Detection New Paradigm Malware Attack Malware Analysis Fractal Structure Malware Representation Malware Sample

November 18, 2022

Clustering based opcode graph generation for malware variant detection
Kar Wai Fok, Vrizlynn L. L. Thing
Malware Detection Malware Attack Malware Family Depth Based Variant Search Algorithm

November 4, 2022

MalGrid: Visualization Of Binary Features In Large Malware Corpora
Tajuddin Manhar Mohammed, Lakshmanan Nataraj, Satish Chikkagoudar, Shivkumar Chandrasekaran, B. S. Manjunath
Interactive Visualization Malware Attack Malware Datasets Binary Feature Malware Sample

October 25, 2022

Multi-view Representation Learning from Malware to Defend Against Adversarial Variants
James Lee Hu, Mohammadreza Ebrahimi, Weifeng Li, Xin Li, Hsinchun Chen
Malware Attack Multi View Representation Learning Based Malware

May 13, 2022

Representation learning with function call graph transformations for malware open set recognition
Jingyun Jia, Philip K. Chan
Individual Representation Self Supervised Pre Training Simple Function Open Set Recognition Malware Attack Malware Classification Malware Family Malware Classifier Malware Representation Graph Transformation

April 13, 2022

Stealing and Evading Malware Classifiers and Antivirus at Low False Positive Conditions
Maria Rigaki, Sebastian Garcia
Surrogate Model Malware Detection Malware Attack Adversarial Malware Malware Classifier False Negative Rate

January 20, 2022

Android Malware Detection using Feature Ranking of Permissions
Muhammad Suleman Saleem, Jelena Mišić, Vojislav B. Mišić
Random Forest Malware Attack Android Malware Malware Family Random Forest Classifier Feature Ranking Agentive Permission

December 10, 2021

Malware Classification Using Static Disassembly and Machine Learning
Zhenshuo Chen, Eoin Brophy, Tomas Ward
Machine Learning Obfuscation Technique Malware Attack Malware Classification Static Analysis Malware Family Automatic Disassembly Behavioral Feature

December 5, 2021

Using Static and Dynamic Malware features to perform Malware Ascription
Jashanpreet Singh Sraw, Keshav Kumar
Machine Learning Machine Learning Algorithm Malware Attack Novel Malware

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 10, 2021

A framework for comprehensible multi-modal detection of cyber threats
Jan Kohout, Čeněk Škarda, Kyrylo Shcherbin, Martin Kopp, Jan Brabec
Data Detection New Framework Multi Modal Malware Attack Malicious User Cyber Threat

November 3, 2021

Virus-MNIST: Machine Learning Baseline Calculations for Image Classification
Erik Larsen, Korey MacVittie, John Lilly
Image Classification Supervised Baseline MNIST Dataset Malware Attack MNIST Canadian Institute for Advanced MNIST Image Character Classifier