Malware Detection

88 papers with code • 2 benchmarks • 4 datasets

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

Latest papers with no code

A Transformer-Based Framework for Payload Malware Detection and Classification

no code yet • 27 Mar 2024

Techniques such as Deep Packet Inspection (DPI) have been introduced to allow IDSs analyze the content of network packets, providing more context for identifying potential threats.

Holographic Global Convolutional Networks for Long-Range Prediction Tasks in Malware Detection

no code yet • 23 Mar 2024

Malware detection is an interesting and valuable domain to work in because it has significant real-world impact and unique machine-learning challenges.

Shifting the Lens: Detecting Malware in npm Ecosystem with Large Language Models

no code yet • 18 Mar 2024

Our baseline comparison demonstrates a notable improvement over static analysis in precision scores above 25% and F1 scores above 15%.

Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection

no code yet • 4 Mar 2024

This study conducts a thorough examination of malware detection using machine learning techniques, focusing on the evaluation of various classification models using the Mal-API-2019 dataset.

Improving Android Malware Detection Through Data Augmentation Using Wasserstein Generative Adversarial Networks

no code yet • 1 Mar 2024

This research explores the effectiveness of utilizing GAN-generated data to train a model for the detection of Android malware.

How to Train your Antivirus: RL-based Hardening through the Problem-Space

no code yet • 29 Feb 2024

It also makes possible to provide theoretical guarantees on the robustness of the model against a particular set of adversarial capabilities.

Use of Multi-CNNs for Section Analysis in Static Malware Detection

no code yet • 6 Feb 2024

Existing research on malware detection focuses almost exclusively on the detection rate.

Weakly Supervised Anomaly Detection via Knowledge-Data Alignment

no code yet • 6 Feb 2024

In this paper, we introduce a novel framework Knowledge-Data Alignment (KDAlign) to integrate rule knowledge, typically summarized by human experts, to supplement the limited labeled data.

Unraveling the Key of Machine Learning Solutions for Android Malware Detection

no code yet • 5 Feb 2024

Android malware detection serves as the front line against malicious apps.

Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach

no code yet • 4 Feb 2024

Adversarial Malware Generation (AMG), the gen- eration of adversarial malware variants to strengthen Deep Learning (DL)-based malware detectors has emerged as a crucial tool in the development of proactive cyberdefense.