Novel Malware

Novel malware research focuses on developing robust and adaptable detection methods to counter the ever-evolving tactics of malicious software. Current efforts concentrate on improving the accuracy and efficiency of machine learning models, including neural networks (RNNs, others) and ensemble methods, often incorporating both static and dynamic analysis of malware behavior and leveraging features extracted from diverse sources like antivirus scan reports and binary image representations. These advancements aim to enhance cybersecurity defenses by improving detection rates, reducing false positives, and enabling faster identification of novel malware variants, ultimately contributing to more secure computing environments.

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

April 19, 2024

Towards Robust Real-Time Hardware-based Mobile Malware Detection using Multiple Instance Learning Formulation
Harshit Kumar, Sudarshan Sharma, Biswadeep Chakraborty, Saibal Mukhopadhyay
Real Time Multiple Instance Learning Android Malware Malware Representation Novel Malware Malware Feature Malicious Training

October 27, 2023

Enhancing Enterprise Network Security: Comparing Machine-Level and Process-Level Analysis for Dynamic Malware Detection
Baskoro Adi Pratomo, Toby Jackson, Pete Burnap, Andrew Hood, Eirini Anthi
Malware Detection Detection Model Malware Family Malware Analysis Novel Malware Malicious Instruction Process Insight Network Security

September 4, 2023

MalwareDNA: Simultaneous Classification of Malware, Malware Families, and Novel Malware
Maksim E. Eren, Manish Bhattarai, Kim Rasmussen, Boian S. Alexandrov, Charles Nicholas
Machine Learning Feature Selection Malware Attack Malware Family Novel Malware Metamorphic Malware

August 21, 2023

Neural Networks Optimizations Against Concept and Data Drift in Malware Detection
William Maillet, Benjamin Marais
Neural Network Machine Learning Model Concept Drift Malware Detection High Impact Concept Data Drift Novel Malware Binary Cross Entropy

August 9, 2023

A Feature Set of Small Size for the PDF Malware Detection
Ran Liu, Charles Nicholas
Machine Learning Feature Wise Malware Detection Novel Malware Based Malware Detection

June 9, 2023

AVScan2Vec: Feature Learning on Antivirus Scan Data for Production-Scale Malware Corpora
Robert J. Joyce, Tirth Patel, Charles Nicholas, Edward Raff
Feature Learning Malware Family Malware Datasets Novel Malware

May 3, 2023

Can Feature Engineering Help Quantum Machine Learning for Malware Detection?
Ran Liu, Maksim Eren, Charles Nicholas
Machine Learning Quantum Machine Learning Malware Detection Malware Classification Novel Malware

September 6, 2022

Orchestrating Collaborative Cybersecurity: A Secure Framework for Distributed Privacy-Preserving Threat Intelligence Sharing
Juan R. Trocoso-Pastoriza, Alain Mermoud, Romain Bouyé, Francesco Marino, Jean-Philippe Bossuat, Vincent Lenders, Jean-Pierre Hubaux
Data Sharing Threat Intelligence Novel Malware Agentive Permission

September 2, 2022

BinImg2Vec: Augmenting Malware Binary Image Classification with Data2Vec
Joon Sern Lee, Kai Keng Tay, Zong Fu Chua
Self Supervised Learning Malware Classification Malware Family Novel Malware

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

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 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

Novel Malware

Papers

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

Towards Robust Real-Time Hardware-based Mobile Malware Detection using Multiple Instance Learning Formulation

Enhancing Enterprise Network Security: Comparing Machine-Level and Process-Level Analysis for Dynamic Malware Detection

MalwareDNA: Simultaneous Classification of Malware, Malware Families, and Novel Malware

Neural Networks Optimizations Against Concept and Data Drift in Malware Detection

A Feature Set of Small Size for the PDF Malware Detection

AVScan2Vec: Feature Learning on Antivirus Scan Data for Production-Scale Malware Corpora

Can Feature Engineering Help Quantum Machine Learning for Malware Detection?

Orchestrating Collaborative Cybersecurity: A Secure Framework for Distributed Privacy-Preserving Threat Intelligence Sharing

BinImg2Vec: Augmenting Malware Binary Image Classification with Data2Vec

Comprehensive Efficiency Analysis of Machine Learning Algorithms for Developing Hardware-Based Cybersecurity Countermeasures

Using Static and Dynamic Malware features to perform Malware Ascription

HAPSSA: Holistic Approach to PDF Malware Detection Using Signal and Statistical Analysis