Search Results for author: Enrico Mariconti

Found 6 papers, 0 papers with code

Cerberus: Exploring Federated Prediction of Security Events

no code implementations7 Sep 2022 Mohammad Naseri, Yufei Han, Enrico Mariconti, Yun Shen, Gianluca Stringhini, Emiliano De Cristofaro

Modern defenses against cyberattacks increasingly rely on proactive approaches, e. g., to predict the adversary's next actions based on past events.

Federated Learning

MaMaDroid2.0 -- The Holes of Control Flow Graphs

no code implementations28 Feb 2022 Harel Berger, Chen Hajaj, Enrico Mariconti, Amit Dvir

The changes in the ratio between benign and malicious samples have a clear effect on each one of the models, resulting in a decrease of more than 40% in their detection rate.

Android Malware Detection Malware Detection

Tiresias: Predicting Security Events Through Deep Learning

no code implementations24 May 2019 Yun Shen, Enrico Mariconti, Pierre-Antoine Vervier, Gianluca Stringhini

With the increased complexity of modern computer attacks, there is a need for defenders not only to detect malicious activity as it happens, but also to predict the specific steps that will be taken by an adversary when performing an attack.

A Family of Droids -- Android Malware Detection via Behavioral Modeling: Static vs Dynamic Analysis

no code implementations9 Mar 2018 Lucky Onwuzurike, Mario Almeida, Enrico Mariconti, Jeremy Blackburn, Gianluca Stringhini, Emiliano De Cristofaro

Aiming to counter them, detection techniques based on either static or dynamic analysis that model Android malware, have been proposed.

Cryptography and Security

MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models (Extended Version)

no code implementations20 Nov 2017 Lucky Onwuzurike, Enrico Mariconti, Panagiotis Andriotis, Emiliano De Cristofaro, Gordon Ross, Gianluca Stringhini

Aiming to assess whether MaMaDroid's effectiveness mainly stems from the API abstraction or from the sequencing modeling, we also evaluate a variant of it that uses frequency (instead of sequences), of abstracted API calls.

MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models

no code implementations13 Dec 2016 Enrico Mariconti, Lucky Onwuzurike, Panagiotis Andriotis, Emiliano De Cristofaro, Gordon Ross, Gianluca Stringhini

Finally, we compare against DroidAPIMiner, a state-of-the-art system that relies on the frequency of API calls performed by apps, showing that MaMaDroid significantly outperforms it.

Cryptography and Security

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