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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 301–350 of 431 papers

TitleStatusHype
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
XAI and Android Malware Models—0
COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection—0
Machine Learning for Detecting Malware in PE Files—0
Network Threat Detection: Addressing Class Imbalanced Data with Deep Forest—0
A Combination Method for Android Malware Detection Based on Control Flow Graphs and Machine Learning Algorithms—0
Feature Extraction for Novelty Detection in Network Traffic—0
A Comparison of Adversarial Learning Techniques for Malware Detection—0
A Comparison of Static, Dynamic, and Hybrid Analysis for Malware Detection—0
Investigating Feature and Model Importance in Android Malware Detection: An Implemented Survey and Experimental Comparison of ML-Based Methods—0
ActDroid: An active learning framework for Android malware detection—0
Adapting Novelty towards Generating Antigens for Antivirus systems—0
Adaptive and Scalable Android Malware Detection through Online Learning—0
Adversarial Patterns: Building Robust Android Malware Classifiers—0
Adversarial Perturbations Against Deep Neural Networks for Malware Classification—0
Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective—0
Adversarial Samples on Android Malware Detection Systems for IoT Systems—0
Adversary Resistant Deep Neural Networks with an Application to Malware Detection—0
AdvMS: A Multi-source Multi-cost Defense Against Adversarial Attacks—0
A Feature Set of Small Size for the PDF Malware Detection—0
Agent-based Vs Agent-less Sandbox for Dynamic Behavioral Analysis—0
A Hierarchical Convolutional Neural Network for Malware Classification—0
AiDroid: When Heterogeneous Information Network Marries Deep Neural Network for Real-time Android Malware Detection—0
A Malware Classification Survey on Adversarial Attacks and Defences—0
A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods—0
A multi-task learning model for malware classification with useful file access pattern from API call sequence—0
A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization—0
Analysing Safety Risks in LLMs Fine-Tuned with Pseudo-Malicious Cyber Security Data—0
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