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Malware Detection

Malware Detection is a significant part of endpoint security including workstations, servers, cloud instances, and mobile devices. Malware Detection is used to detect and identify malicious activities caused by malware. With the increase in the variety of malware activities on CMS based websites such as malicious malware redirects on WordPress site (Aka, WordPress Malware Redirect Hack) where the site redirects to spam, being the most widespread, the need for automatic detection and classifier amplifies as well. The signature-based Malware Detection system is commonly used for existing malware that has a signature but it is not suitable for unknown malware or zero-day malware

Source: The Threat of Adversarial Attacks on Machine Learning in Network Security - A Survey

Papers

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Showing 101–150 of 431 papers

TitleStatusHype
Android Malware Detection Based on RGB Images and Multi-feature Fusion—0
Improving Adversarial Robustness in Android Malware Detection by Reducing the Impact of Spurious CorrelationsCode0
Obfuscated Memory Malware Detection—0
Natural Language Outlines for Code: Literate Programming in the LLM Era—0
A Survey of Malware Detection Using Deep Learning—0
Explainable AI-based Intrusion Detection System for Industry 5.0: An Overview of the Literature, associated Challenges, the existing Solutions, and Potential Research Directions—0
A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes—0
Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach—0
On the Abuse and Detection of Polyglot Files—0
Unsupervised representation learning with Hebbian synaptic and structural plasticity in brain-like feedforward neural networks—0
Hyperbolic Benchmarking Unveils Network Topology-Feature Relationship in GNN PerformanceCode0
A New Formulation for Zeroth-Order Optimization of Adversarial EXEmples in Malware Detection—0
SLIFER: Investigating Performance and Robustness of Malware Detection Pipelines—0
Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities—0
Transfer Learning in Pre-Trained Large Language Models for Malware Detection Based on System Calls—0
Deep Multi-Task Learning for Malware Image Classification—0
Leveraging LSTM and GAN for Modern Malware Detection—0
Online Clustering of Known and Emerging Malware Families—0
Detecting Android Malware: From Neural Embeddings to Hands-On Validation with BERTroid—0
Explainable Malware Detection with Tailored Logic Explained Networks—0
Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing—0
Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Research—0
Towards Robust Real-Time Hardware-based Mobile Malware Detection using Multiple Instance Learning Formulation—0
Counteracting Concept Drift by Learning with Future Malware Predictions—0
Optimization of Lightweight Malware Detection Models For AIoT Devices—0
Obfuscated Malware Detection: Investigating Real-world Scenarios through Memory Analysis—0
Generative AI-Based Effective Malware Detection for Embedded Computing Systems—0
A Transformer-Based Framework for Payload Malware Detection and Classification—0
Holographic Global Convolutional Networks for Long-Range Prediction Tasks in Malware Detection—0
Leveraging Large Language Models to Detect npm Malicious Packages—0
Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection—0
Improving Android Malware Detection Through Data Augmentation Using Wasserstein Generative Adversarial Networks—0
How to Train your Antivirus: RL-based Hardening through the Problem-SpaceCode0
Weakly Supervised Anomaly Detection via Knowledge-Data Alignment—0
Use of Multi-CNNs for Section Analysis in Static Malware Detection—0
Unraveling the Key of Machine Learning Solutions for Android Malware Detection—0
Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach—0
ActDroid: An active learning framework for Android malware detection—0
MORPH: Towards Automated Concept Drift Adaptation for Malware Detection—0
Malware Detection in IOT Systems Using Machine Learning Techniques—0
Small Effect Sizes in Malware Detection? Make Harder Train/Test Splits!—0
Discovering Malicious Signatures in Software from Structural Interactions—0
Towards an in-depth detection of malware using distributed QCNN—0
Android Malware Detection with Unbiased Confidence Guarantees—0
A Malware Classification Survey on Adversarial Attacks and Defences—0
Explaining high-dimensional text classifiers—0
Machine learning-based malware detection for IoT devices using control-flow data—0
Enhancing Malware Detection by Integrating Machine Learning with Cuckoo Sandbox—0
Enhancing Enterprise Network Security: Comparing Machine-Level and Process-Level Analysis for Dynamic Malware Detection—0
Light up that Droid! On the Effectiveness of Static Analysis Features against App Obfuscation for Android Malware Detection—0
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