Clustering Malware at Scale: A First Full-Benchmark Study

Recent years have shown that malware attacks still happen with high frequency. Malware experts seek to categorize and classify incoming samples to confirm their trustworthiness or prove their maliciousness. One of the ways in which groups of malware samples can be identified is through malware...

KEENHash: Hashing Programs into Function-Aware Embeddings for Large-Scale Binary Code Similarity Analysis

Binary code similarity analysis BCSA is a crucial research area in many fields such as cybersecurity. Specifically, function-level diffing tools are the most widely used in BCSA: they perform function matching one by one for evaluating the similarity between binary programs. However, such methods...

Determinação Automática de Limiar de Detecção de Ataques em Redes de Computadores Utilizando Autoencoders

Currently, digital security mechanisms like Anomaly Detection Systems using Autoencoders AE show great potential for bypassing problems intrinsic to the data, such as data imbalance. Because AE use a non-trivial and nonstandardized separation threshold to classify the extracted reconstruction...

Differentially Private Federated $K$-Means Clustering with Server-Side Data

Clustering is a cornerstone of data analysis that is particularly suited to identifying coherent subgroups or substructures in unlabeled data, as are generated continuously in large amounts these days. However, in many cases traditional clustering methods are not applicable, because data are...

Clustering and Dimensionality Reduction: Understanding the “Magic” Behind Machine Learning

These days we hear about machine learning and artificial intelligence AI in all aspects of life. We see machines that learn and imitate the human brain in order to automate human processes. There are autonomous cars that learn the road conditions to drive, personal assistants we can converse with...