Search Results for author: Giorgio Giacinto

Found 11 papers, 4 papers with code

Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware?

no code implementations4 May 2020 Marco Melis, Michele Scalas, Ambra Demontis, Davide Maiorca, Battista Biggio, Giorgio Giacinto, Fabio Roli

While machine-learning algorithms have demonstrated a strong ability in detecting Android malware, they can be evaded by sparse evasion attacks crafted by injecting a small set of fake components, e. g., permissions and system calls, without compromising intrusive functionality.

Adversarial Robustness Android Malware Detection +1

PowerDrive: Accurate De-Obfuscation and Analysis of PowerShell Malware

1 code implementation23 Apr 2019 Denis Ugarte, Davide Maiorca, Fabrizio Cara, Giorgio Giacinto

We used PowerDrive to successfully analyze thousands of PowerShell attacks extracted from various malware vectors and executables.

Cryptography and Security

Poisoning Behavioral Malware Clustering

no code implementations25 Nov 2018 Battista Biggio, Konrad Rieck, Davide Ariu, Christian Wressnegger, Igino Corona, Giorgio Giacinto, Fabio Roli

Clustering algorithms have become a popular tool in computer security to analyze the behavior of malware variants, identify novel malware families, and generate signatures for antivirus systems.

Computer Security Malware Clustering

On the Effectiveness of System API-Related Information for Android Ransomware Detection

no code implementations24 May 2018 Michele Scalas, Davide Maiorca, Francesco Mercaldo, Corrado Aaron Visaggio, Fabio Martinelli, Giorgio Giacinto

The attained results showed that systems based on System API could detect ransomware and generic malware with very good accuracy, comparable to systems that employed more complex information.

Android Malware Detection Malware Detection

Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables

1 code implementation12 Mar 2018 Bojan Kolosnjaji, Ambra Demontis, Battista Biggio, Davide Maiorca, Giorgio Giacinto, Claudia Eckert, Fabio Roli

Machine-learning methods have already been exploited as useful tools for detecting malicious executable files.

Cryptography and Security

Explaining Black-box Android Malware Detection

no code implementations9 Mar 2018 Marco Melis, Davide Maiorca, Battista Biggio, Giorgio Giacinto, Fabio Roli

In this work, we generalize this approach to any black-box machine- learning model, by leveraging a gradient-based approach to identify the most influential local features.

Android Malware Detection BIG-bench Machine Learning +1

IntelliAV: Building an Effective On-Device Android Malware Detector

no code implementations4 Feb 2018 Mansour Ahmadi, Angelo Sotgiu, Giorgio Giacinto

Several anti-malware vendors have claimed and advertised the application of machine learning in their products in which the inference phase is performed on servers and high-performance machines, but the feasibility of such approaches on mobile devices with limited computational resources has not yet been assessed by the research community, vendors still being skeptical.

Cryptography and Security

Evasion Attacks against Machine Learning at Test Time

1 code implementation21 Aug 2017 Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Srndic, Pavel Laskov, Giorgio Giacinto, Fabio Roli

In security-sensitive applications, the success of machine learning depends on a thorough vetting of their resistance to adversarial data.

BIG-bench Machine Learning Malware Detection +1

Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection

no code implementations28 Apr 2017 Ambra Demontis, Marco Melis, Battista Biggio, Davide Maiorca, Daniel Arp, Konrad Rieck, Igino Corona, Giorgio Giacinto, Fabio Roli

To cope with the increasing variability and sophistication of modern attacks, machine learning has been widely adopted as a statistically-sound tool for malware detection.

Cryptography and Security

Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification

17 code implementations13 Nov 2015 Mansour Ahmadi, Dmitry Ulyanov, Stanislav Semenov, Mikhail Trofimov, Giorgio Giacinto

This paradigm is presented and discussed in the present paper, where emphasis has been given to the phases related to the extraction, and selection of a set of novel features for the effective representation of malware samples.

Computer Security General Classification +1

Security Evaluation of Support Vector Machines in Adversarial Environments

no code implementations30 Jan 2014 Battista Biggio, Igino Corona, Blaine Nelson, Benjamin I. P. Rubinstein, Davide Maiorca, Giorgio Fumera, Giorgio Giacinto, and Fabio Roli

Support Vector Machines (SVMs) are among the most popular classification techniques adopted in security applications like malware detection, intrusion detection, and spam filtering.

Intrusion Detection Malware Detection

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