Malware Sample

Malware sample analysis focuses on identifying and classifying malicious software to improve cybersecurity defenses. Current research emphasizes developing robust detection methods using machine learning, particularly employing algorithms like random forests, gradient boosting, and graph neural networks, often incorporating diverse feature sets such as API calls, binary code representations (visualized as images or audio signals), and system provenance graphs. These advancements aim to enhance the accuracy and explainability of malware detection, addressing challenges posed by obfuscation techniques and the ever-evolving nature of malware, ultimately contributing to more effective and resilient cybersecurity systems.

Papers

November 8, 2024

Unmasking the Shadows: Pinpoint the Implementations of Anti-Dynamic Analysis Techniques in Malware Using LLM
Haizhou Wang, Nanqing Luo, Peng LIu
Large Language Model Quantum Shadow Reverse Engineering Malware Analysis Implementation Detail Malware Sample Analytical Method

August 23, 2024

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

April 2, 2024

Generative AI-Based Effective Malware Detection for Embedded Computing Systems
Sreenitha Kasarapu, Sanket Shukla, Rakibul Hassan, Avesta Sasan, Houman Homayoun, Sai Manoj Pudukotai Dinakarrao
Generative AI Malware Detection Embedded System Malware Sample

June 1, 2023

Explaining Provenance-Based GNN Detectors with Graph Structural Features
Kunal Mukherjee, Joshua Wiedemeier, Tianhao Wang, Muhyun Kim, Feng Chen, Murat Kantarcioglu, Kangkook Jee
Intrusion Detection System Data Provenance Explainable Model Machine Translated Malware Sample Cybersecurity Task

January 30, 2023

Behavioural Reports of Multi-Stage Malware
Marcus Carpenter, Chunbo Luo
Malware Detection Intrusion Detection System Behavioral Data Malware Sample 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 25, 2022

OOG- Optuna Optimized GAN Sampling Technique for Tabular Imbalanced Malware Data
S. M Towhidul Islam Tonmoy, S. M Mehedi Zaman
Generative Adversarial Network GAN Model Malware Sample

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

January 5, 2022

Comprehensive Efficiency Analysis of Machine Learning Algorithms for Developing Hardware-Based Cybersecurity Countermeasures
Darren Cobian
Machine Learning Algorithm Malware Classification Hardware Aware Novel Malware Malware Sample Hardware Attack Envelopment Analysis

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