8 citations · 10 across the 5 of their papers we have counts for
6 papers
Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performance
Savino Dambra, Yufei Han, Simone Aonzo +5
Many studies have proposed machine-learning (ML) models for malware detection and classification, reporting an almost-perfect performance. However, they assemble ground-truth in di…
One Size Does not Fit All: Quantifying the Risk of Malicious App Encounters for Different Android User Profiles
Savino Dambra, Leyla Bilge, Platon Kotzias +2
Previous work has investigated the particularities of security practices within specific user communities defined based on country of origin, age, prior tech abuse, and economic st…
A Deep Dive into VirusTotal: Characterizing and Clustering a Massive File Feed
Kevin van Liebergen, Juan Caballero, Platon Kotzias +1
Online scanners analyze user-submitted files with a large number of security tools and provide access to the analysis results. As the most popular online scanner, VirusTotal (VT) i…
Longitudinal Study of the Prevalence of Malware Evasive Techniques
Lorenzo Maffia, Dario Nisi, Platon Kotzias +3
By their very nature, malware samples employ a variety of techniques to conceal their malicious behavior and hide it from analysis tools. To mitigate the problem, a large number of…
Unsupervised Detection and Clustering of Malicious TLS Flows
Gibran Gomez, Platon Kotzias, Matteo Dell'Amico +2
Malware abuses TLS to encrypt its malicious traffic, preventing examination by content signatures and deep packet inspection. Network detection of malicious TLS flows is an importa…
How Did That Get In My Phone? Unwanted App Distribution on Android Devices
Platon Kotzias, Juan Caballero, Leyla Bilge
Android is the most popular operating system with billions of active devices. Unfortunately, its popularity and openness makes it attractive for unwanted apps, i.e., malware and po…