SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 226–250 of 431 papers

TitleStatusHype
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
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
Evasion Attacks against Machine Learning at Test Time—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
Show:102550
← PrevPage 10 of 18Next →

No leaderboard results yet.