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
Recasting Self-Attention with Holographic Reduced Representations
neuromorphiccomputationresearchprogram/hrrformer
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In recent years, self-attention has become the dominant paradigm for sequence modeling in a variety of domains.
DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness
shoumiksaha/drsm
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After showing how DRSM is theoretically robust against attacks with contiguous adversarial bytes, we verify its performance and certified robustness experimentally, where we observe only marginal accuracy drops as the cost of robustness.
PAD: Towards Principled Adversarial Malware Detection Against Evasion Attacks
deqangss/pad4amd
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To promote defense effectiveness, we propose a new mixture of attacks to instantiate PAD to enhance deep neural network-based measurements and malware detectors.
Sequential Embedding-based Attentive (SEA) classifier for malware classification
The tremendous growth in smart devices has uplifted several security threats.
Continuous Learning for Android Malware Detection
wagner-group/active-learning
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We propose a new hierarchical contrastive learning scheme, and a new sample selection technique to continuously train the Android malware classifier.
RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion
dovermore/randomized-deletion
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NeurIPS 2023
When applied to the popular MalConv malware detection model, our smoothing mechanism RS-Del achieves a certified accuracy of 91% at an edit distance radius of 128 bytes.
Behavioural Reports of Multi-Stage Malware
marcuscarpenter97/malware-data
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The extensive damage caused by malware requires anti-malware systems to be constantly improved to prevent new threats.
Reliable Malware Analysis and Detection using Topology Data Analysis
Next, we compare the different TDA techniques (i. e., persistence homology, tomato, TDA Mapper) and existing techniques (i. e., PCA, UMAP, t-SNE) using different classifiers including random forest, decision tree, xgboost, and lightgbm.
UniASM: Binary Code Similarity Detection without Fine-tuning
clm07/uniasm
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Binary code similarity detection (BCSD) is widely used in various binary analysis tasks such as vulnerability search, malware detection, clone detection, and patch analysis.
Avast-CTU Public CAPE Dataset
The benefit of using dynamic sandboxes is the realistic simulation of file execution in the target machine and obtaining a log of such execution.
