arXiv Machine Learning

Enhancing Multiclass Malware Classification in Resource-Constrained Environments

The paper presents a lightweight machine‑learning approach for multi‑class malware detection on resource‑constrained devices. Using a LightGBM classifier with SMOTE oversampling, SOM‑US undersampling, and Genetic‑Algorithm feature selection, the authors achieve 89.1 % accuracy on four malware families and 76 % on 16 individual malware types. A second Random‑Forest model further improves family classification to 91.2 % and individual classification to 78.7 %.

arXiv Machine Learning

Aug 5

On the Performance of Malware Detection Classifiers Using Hardware Performance Counters

arXiv:2608. 02671v1 Announce Type: cross Abstract: Malware detection using Hardware Performance Counters (HPC) has emerged as a promising solution to improve the security of computing systems as a complement to antivirus software.

By Alireza Abolhasani Zeraatkar, Parnian Shabani Kamran, Inderpreet Kaur, Nagabindu Ramu, Tyler Sheaves, Hussain Al-Asaad

arXiv AI

Jun 3

High-Precision APT Malware Attribution with Out-of-Scope Resilience

arXiv:2606. 03523v1 Announce Type: cross Abstract: Early attribution of Advanced Persistent Threat (APT) activity can help defenders prioritise investigation, select countermeasures, and reduce the impact of an intrusion.

By Peter Williams, Adam Sobey, Erisa Karafili

arXiv AI

Jun 30

A Multi-task Mixture of Experts Framework for Malware Classification, Packing Detection, and Family Attribution

arXiv:2606. 30572v1 Announce Type: cross Abstract: Malware classification remains a challenging problem due to its inherent heterogeneity, the presence of packed binaries, and the diverse distribution of malware families.

By Jithin S., Roshin Sleeba C., Anvin Mariya P. B., Asmitha K. A., Vinod P., Serena Nicolazzo, Antonino Nocera

safety

arXiv AI

Jun 3

A Hybrid Approach For Malware Classification Using Secondary Features Fusion

arXiv:2606. 03432v1 Announce Type: cross Abstract: The number of malware (either variant or novel) is rapidly increasing, making malware detection and mitigation a complex problem.

By Raja Khurram Shahzad, Muhammad Mustaqeem, Haroon Elahi

arXiv Machine Learning

2d ago

Multi-Class, Multi-Tier Network Intrusion Detection: A Comprehensive and Reproducible Benchmark

arXiv:2609.36039v1 Announce Type: cross Abstract: Machine learning (ML) and deep learning (DL) have dominated Intrusion Detection System (IDS) research in recent years. Unfortunately, many existing s...

By Yufeng Xin, Bryant Goseland, Mohamed Rahouti

benchmarks

arXiv AI

Aug 14

Concept Drift Detection and Adaptive Retraining of Malware Classification Models

arXiv:2608. 13465v1 Announce Type: cross Abstract: Concept drift refers to changes over time in the statistical properties of data, as compared to the data that was used to train a learning model.

By Christofer Washington Berruz Chungata, Martin Jurecek, Katerina Potika, William B. Andreopoulos, Mark Stamp

arXiv Machine Learning

Sep 22

An LLM-Assisted AutoML Framework for Intrusion Detection in IoT Networks

arXiv:2609.23097v1 Announce Type: cross Abstract: Internet of Things (IoT) systems are increasingly deployed in smart homes, transportation, energy systems, and critical infrastructure. This broad co...

By Li Yang

llms

arXiv AI

Jun 2

Improving IoT Intrusion Detection Through SMOTE-Based Oversampling and Extended Multi-Model Evaluation on Side-Channel Power Data

arXiv:2606. 00161v1 Announce Type: cross Abstract: The detection of intrusions in IoT-based networks poses challenges that cannot be overcome using traditional machine learning methods.

By Muhammad Khuram Shahzad, Haseeb Khan, Muhammad Masood Khan, Mubashra Bibi

arXiv AI

Jul 1

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks

arXiv:2606. 31594v1 Announce Type: cross Abstract: The Internet of Things (IoT) is rapidly growing and expanding into various sectors, such as healthcare, transportation, smart homes, and more.

By Rana Alharbi, Chuadhry Mujeeb Ahmed

safety

arXiv Machine Learning

Sep 25

Classifier-Dependent Benefits of Pseudo-Labeling for Semi-Supervised Android Malware Attribution

The study evaluates pseudo‑labeling for semi‑supervised learning on Android malware attribution using six classifiers. Results show that the benefit of SSL varies strongly by classifier: SVM gains the most, LightGBM improves modestly, and Random Forest can be harmed at low label ratios. The approach particularly helps hard‑to‑classify families and achieves near‑optimal performance with about 800 labeled samples.

By Md Rafid Islam, Zahid Hasan, Hafiz Abdur Rahman

arXiv Machine Learning

Aug 5

ShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection

arXiv:2608. 03250v1 Announce Type: cross Abstract: The rapid advancement of modern technology has led to a significant increase in the use of smart devices, such as smartphones and tablets, resulting in the widespread adoption of mobile applications.

By Md Faisal Ahmed, Zarin Tasnim Biash, Abu Raihan Shakil, Ahmed Ann Noor Ryen, Arman Hossain, Faisal Bin Ashraf, Muhammad Iqbal Hossain

Hugging Face Trending Papers

Jun 29

A Hybrid Framework For Crypto-Ransomware Detection In Enterprise Shared Storage

Most corporate workplace environments enforce policies and technical controls that limit the storage of sensitive data on client endpoints. Consequently, ransomware operators have evolved variants that expand their attack surface from local systems to network drives and shared storage resources.

Related stories