Malware Detection
91 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
EMBER
MalNet
IoT-23
Latest papers with no code
How to Train your Antivirus: RL-based Hardening through the Problem-Space
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
Existing research on malware detection focuses almost exclusively on the detection rate.
Weakly Supervised Anomaly Detection via Knowledge-Data Alignment
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
Android malware detection serves as the front line against malicious apps.
Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach
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.
ActDroid: An active learning framework for Android malware detection
The growing popularity of Android requires malware detection systems that can keep up with the pace of new software being released.
MORPH: Towards Automated Concept Drift Adaptation for Malware Detection
Concept drift is a significant challenge for malware detection, as the performance of trained machine learning models degrades over time, rendering them impractical.
Malware Detection in IOT Systems Using Machine Learning Techniques
Comparative analysis against prevalent techniques demonstrated the efficacy of the proposed model, highlighting its potential for enhancing IoT security.
Small Effect Sizes in Malware Detection? Make Harder Train/Test Splits!
Industry practitioners care about small improvements in malware detection accuracy because their models are deployed to hundreds of millions of machines, meaning a 0. 1\% change can cause an overwhelming number of false positives.
Towards an in-depth detection of malware using distributed QCNN
In order to enhance the performances of our quantum algorithms for malware detection using images, without increasing the resources needed in terms of qubits, we implement a new preprocessing of our dataset using Grayscale method, and we couple it with a model composed of five distributed quantum convolutional networks and a scoring function.
