475 citations · 555 across the 41 of their papers we have counts for
12 papers · 1 filter
Feature Analysis of Encrypted Malicious Traffic
Anish Singh Shekhawat, Fabio Di Troia, Mark Stamp
In recent years there has been a dramatic increase in the number of malware attacks that use encrypted HTTP traffic for self-propagation or communication. Antivirus software and fi…
Social Media Bot Detection using Dropout-GAN
Anant Shukla, Martin Jurecek, Mark Stamp
Bot activity on social media platforms is a pervasive problem, undermining the credibility of online discourse and potentially leading to cybercrime. We propose an approach to bot…
Classifying World War II Era Ciphers with Machine Learning
Brooke Dalton, Mark Stamp
We determine the accuracy with which machine learning and deep learning techniques can classify selected World War II era ciphers when only ciphertext is available. The specific ci…
On the Steganographic Capacity of Selected Learning Models
Rishit Agrawal, Kelvin Jou, Tanush Obili +6
Machine learning and deep learning models are potential vectors for various attack scenarios. For example, previous research has shown that malware can be hidden in deep learning m…
A Comparison of Adversarial Learning Techniques for Malware Detection
Pavla Louthánová, Matouš Kozák, Martin Jureček +1
Machine learning has proven to be a useful tool for automated malware detection, but machine learning models have also been shown to be vulnerable to adversarial attacks. This arti…
Hidden Markov Models with Random Restarts vs Boosting for Malware Detection
Aditya Raghavan, Fabio Di Troia, Mark Stamp
Effective and efficient malware detection is at the forefront of research into building secure digital systems. As with many other fields, malware detection research has seen a dra…
A Natural Language Processing Approach to Malware Classification
Ritik Mehta, Olha Jurečková, Mark Stamp
Many different machine learning and deep learning techniques have been successfully employed for malware detection and classification. Examples of popular learning techniques in th…
Keystroke Dynamics for User Identification
Atharva Sharma, Martin Jureček, Mark Stamp
In previous research, keystroke dynamics has shown promise for user authentication, based on both fixed-text and free-text data. In this research, we consider the more challenging…
Steganographic Capacity of Deep Learning Models
Lei Zhang, Dong Li, Olha Jurečková +1
As machine learning and deep learning models become ubiquitous, it is inevitable that there will be attempts to exploit such models in various attack scenarios. For example, in a s…
Creating Valid Adversarial Examples of Malware
Matouš Kozák, Martin Jureček, Mark Stamp +1
Machine learning is becoming increasingly popular as a go-to approach for many tasks due to its world-class results. As a result, antivirus developers are incorporating machine lea…
An Empirical Analysis of the Shift and Scale Parameters in BatchNorm
Yashna Peerthum, Mark Stamp
Batch Normalization (BatchNorm) is a technique that improves the training of deep neural networks, especially Convolutional Neural Networks (CNN). It has been empirically demonstra…
A Comparison of Graph Neural Networks for Malware Classification
Vrinda Malhotra, Katerina Potika, Mark Stamp
Managing the threat posed by malware requires accurate detection and classification techniques. Traditional detection strategies, such as signature scanning, rely on manual analysi…