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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 201–250 of 431 papers

TitleStatusHype
There is Limited Correlation between Coverage and Robustness for Deep Neural Networks—0
The State-of-the-Art in AI-Based Malware Detection Techniques: A Review—0
The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey—0
Towards Accurate Labeling of Android Apps for Reliable Malware Detection—0
Towards an Automated Pipeline for Detecting and Classifying Malware through Machine Learning—0
Towards an in-depth detection of malware using distributed QCNN—0
Towards a Robust Classifier: An MDL-Based Method for Generating Adversarial Examples—0
Towards Deep Federated Defenses Against Malware in Cloud Ecosystems—0
Towards Interpretable Ensemble Learning for Image-based Malware Detection—0
Towards interpreting ML-based automated malware detection models: a survey—0
Towards Novel Malicious Packet Recognition: A Few-Shot Learning Approach—0
Towards Obfuscated Malware Detection for Low Powered IoT Devices—0
Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization—0
Towards Robust Real-Time Hardware-based Mobile Malware Detection using Multiple Instance Learning Formulation—0
Toward the Detection of Polyglot Files—0
Traffic Analytics Development Kits (TADK): Enable Real-Time AI Inference in Networking Apps—0
Transfer Learning in Pre-Trained Large Language Models for Malware Detection Based on System Calls—0
Transformers for End-to-End InfoSec Tasks: A Feasibility Study—0
Trust Under Siege: Label Spoofing Attacks against Machine Learning for Android Malware Detection—0
Unmasking the Shadows: Pinpoint the Implementations of Anti-Dynamic Analysis Techniques in Malware Using LLM—0
Unraveling the Key of Machine Learning Solutions for Android Malware Detection—0
Unsupervised representation learning with Hebbian synaptic and structural plasticity in brain-like feedforward neural networks—0
Use of Multi-CNNs for Section Analysis in Static Malware Detection—0
Using Deep Neural Network for Android Malware Detection—0
Using Randomness to Improve Robustness of Machine-Learning Models Against Evasion Attacks—0
Virus-MNIST: A Benchmark Malware Dataset—0
Weakly Supervised Anomaly Detection via Knowledge-Data Alignment—0
WebEye - Automated Collection of Malicious HTTP Traffic—0
Word Embedding Techniques for Malware Evolution Detection—0
Would a File by Any Other Name Seem as Malicious?—0
Understanding the efficacy, reliability and resiliency of computer vision techniques for malware detection and future research directions—0
Efficient Malware Analysis Using Metric Embeddings—0
Efficient Malware Detection with Optimized Learning on High-Dimensional Features—0
Empirical Quantification of Spurious Correlations in Malware Detection—0
EMULATOR vs REAL PHONE: Android Malware Detection Using Machine Learning—0
Enhanced Attacks on Defensively Distilled Deep Neural Networks—0
Enhancing Enterprise Network Security: Comparing Machine-Level and Process-Level Analysis for Dynamic Malware Detection—0
Enhancing Malware Detection by Integrating Machine Learning with Cuckoo Sandbox—0
Enhancing Robustness of Neural Networks through Fourier Stabilization—0
Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach—0
Examining Adversarial Learning against Graph-based IoT Malware Detection Systems—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
Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges—0
Explainable Malware Detection through Integrated Graph Reduction and Learning Techniques—0
Explainable Malware Detection with Tailored Logic Explained Networks—0
Explaining Black-box Android Malware Detection—0
Explaining high-dimensional text classifiers—0
Exploring Adversarial Examples in Malware Detection—0
GLSearch: Maximum Common Subgraph Detection via Learning to Search—0
Feature Cross-Substitution in Adversarial Classification—0
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