activity
20182026
most citedA Comparison of Static, Dynamic, and Hybrid Analysis for Malware Detection

475 citations · 482 across the 4 of their papers we have counts for

collaborators
Showing cs.CRShow all

5 papers · 1 filter

cs.CR2023

Discerning Reliable Cyber Threat Indicators for Timely Cyber Threat Intelligence

Dincy R Arikkat, Vinod P., Rafidha Rehiman K. A. +3

In today's dynamic cybersecurity landscape, timely and accurate threat intelligence is essential for proactive defense. This study explores the potential of social media platforms…

cs.CR2022475 cited

A Comparison of Static, Dynamic, and Hybrid Analysis for Malware Detection

Anusha Damodaran, Fabio Di Troia, Visaggio Aaron Corrado +2

In this research, we compare malware detection techniques based on static, dynamic, and hybrid analysis. Specifically, we train Hidden Markov Models (HMMs ) on both static and dyna…

cs.CR20217 cited

GANG-MAM: GAN based enGine for Modifying Android Malware

Renjith G, Sonia Laudanna, Aji S +2

Malware detectors based on machine learning are vulnerable to adversarial attacks. Generative Adversarial Networks (GAN) are architectures based on Neural Networks that could produ…

cs.CR2019

Malware Detection Using Dynamic Birthmarks

Swapna Vemparala, Fabio Di Troia, Corrado A. Visaggio +2

In this paper, we explore the effectiveness of dynamic analysis techniques for identifying malware, using Hidden Markov Models (HMMs) and Profile Hidden Markov Models (PHMMs), both…

cs.CR2018

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

Michele Scalas, Davide Maiorca, Francesco Mercaldo +3

Ransomware constitutes a significant threat to the Android operating system. It can either lock or encrypt the target devices, and victims are forced to pay ransoms to restore thei…