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Found 23 papers, 9 papers with code
Living off the Analyst: Harvesting Features from Yara Rules for Malware Detection
A strategy used by malicious actors is to "live off the land," where benign systems and tools already available on a victim's systems are used and repurposed for the malicious actor's intent.
Stabilizing Linear Passive-Aggressive Online Learning with Weighted Reservoir Sampling
While such algorithms enjoy low theoretical regret, in real-world deployment they can be sensitive to individual outliers that cause the algorithm to over-correct.
A Walsh Hadamard Derived Linear Vector Symbolic Architecture
Vector Symbolic Architectures (VSAs) are one approach to developing Neuro-symbolic AI, where two vectors in $\mathbb{R}^d$ are `bound' together to produce a new vector in the same space.
Is Function Similarity Over-Engineered? Building a Benchmark
Binary analysis is a core component of many critical security tasks, including reverse engineering, malware analysis, and vulnerability detection.
High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates
As the size of datasets used in statistical learning continues to grow, distributed training of models has attracted increasing attention.
Optimizing the Optimal Weighted Average: Efficient Distributed Sparse Classification
While distributed training is often viewed as a solution to optimizing linear models on increasingly large datasets, inter-machine communication costs of popular distributed approaches can dominate as data dimensionality increases.
Assemblage: Automatic Binary Dataset Construction for Machine Learning
Our results illustrate the practical need for robust corpora of high-quality Windows PE binaries in training modern learning-based binary analyses.
Holographic Global Convolutional Networks for Long-Range Prediction Tasks in Malware Detection
Malware detection is an interesting and valuable domain to work in because it has significant real-world impact and unique machine-learning challenges.
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.
Exploring the Sharpened Cosine Similarity
Convolutional layers have long served as the primary workhorse for image classification.
Recasting Self-Attention with Holographic Reduced Representations
In recent years, self-attention has become the dominant paradigm for sequence modeling in a variety of domains.
A Coreset Learning Reality Check
Subsampling algorithms are a natural approach to reduce data size before fitting models on massive datasets.
Efficient Malware Analysis Using Metric Embeddings
In this paper, we explore the use of metric learning to embed Windows PE files in a low-dimensional vector space for downstream use in a variety of applications, including malware detection, family classification, and malware attribute tagging.
Lempel-Ziv Networks
Recurrent neural nets have been successful in processing sequences for a number of tasks; however, they are known to be both ineffective and computationally expensive when applied to very long sequences.
Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations
Due to the computational cost of running inference for a neural network, the need to deploy the inferential steps on a third party's compute environment or hardware is common.
Marvolo: Programmatic Data Augmentation for Practical ML-Driven Malware Detection
Data augmentation has been rare in the cyber security domain due to technical difficulties in altering data in a manner that is semantically consistent with the original data.
Proceedings of the Artificial Intelligence for Cyber Security (AICS) Workshop at AAAI 2022
These challenges are widely studied in enterprise networks, but there are many gaps in research and practice as well as novel problems in other domains.
Out of Distribution Data Detection Using Dropout Bayesian Neural Networks
We explore the utility of information contained within a dropout based Bayesian neural network (BNN) for the task of detecting out of distribution (OOD) data.
Learning with Holographic Reduced Representations
HRRs today are not effective in a differentiable solution due to numerical instability, a problem we solve by introducing a projection step that forces the vectors to exist in a well behaved point in space.
Leveraging Uncertainty for Improved Static Malware Detection Under Extreme False Positive Constraints
The detection of malware is a critical task for the protection of computing environments.
Getting Passive Aggressive About False Positives: Patching Deployed Malware Detectors
We propose a strategy for fixing false positives in production after a model has already been deployed.
Automatic Yara Rule Generation Using Biclustering
Yara rules are a ubiquitous tool among cybersecurity practitioners and analysts.
RelExt: Relation Extraction using Deep Learning approaches for Cybersecurity Knowledge Graph Improvement
A cybersecurity knowledge graph can be paramount in aiding a security analyst to detect cyber threats because it stores a vast range of cyber threat information in the form of semantic triples which can be queried.
