475 citations · 555 across the 41 of their papers we have counts for
42 papers
AGRO: An Autonomous AI Rover for Precision Agriculture
Simar Ghumman, Fabio Di Troia, William Andreopoulos +2
Unmanned Ground Vehicles (UGVs) are emerging as a crucial tool in the world of precision agriculture. The combination of UGVs with machine learning allows us to find solutions for…
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…
Predicting Pedestrian Crosswalk Behavior Using Convolutional Neural Networks
Eric Liang, Mark Stamp
A common yet potentially dangerous task is the act of crossing the street. Pedestrian accidents contribute a significant amount to the high number of annual traffic casualties, whi…
Multifamily Malware Models
Samanvitha Basole, Fabio Di Troia, Mark Stamp
When training a machine learning model, there is likely to be a tradeoff between accuracy and the diversity of the dataset. Previous research has shown that if we train a model to…
Darknet Traffic Classification and Adversarial Attacks
Nhien Rust-Nguyen, Mark Stamp
The anonymous nature of darknets is commonly exploited for illegal activities. Previous research has employed machine learning and deep learning techniques to automate the detectio…
Generative Adversarial Networks and Image-Based Malware Classification
Huy Nguyen, Fabio Di Troia, Genya Ishigaki +1
For efficient malware removal, determination of malware threat levels, and damage estimation, malware family classification plays a critical role. In this paper, we extract feature…
Hidden Markov Models with Momentum
Andrew Miller, Fabio Di Troia, Mark Stamp
Momentum is a popular technique for improving convergence rates during gradient descent. In this research, we experiment with adding momentum to the Baum-Welch expectation-maximiza…
Convolutional Neural Networks for Image Spam Detection
Tazmina Sharmin, Fabio Di Troia, Katerina Potika +1
Spam can be defined as unsolicited bulk email. In an effort to evade text-based filters, spammers sometimes embed spam text in an image, which is referred to as image spam. In this…
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…
Evaluating Deep Learning Models and Adversarial Attacks on Accelerometer-Based Gesture Authentication
Elliu Huang, Fabio Di Troia, Mark Stamp
Gesture-based authentication has emerged as a non-intrusive, effective means of authenticating users on mobile devices. Typically, such authentication techniques have relied on cla…
Clickbait Detection in YouTube Videos
Ruchira Gothankar, Fabio Di Troia, Mark Stamp
YouTube videos often include captivating descriptions and intriguing thumbnails designed to increase the number of views, and thereby increase the revenue for the person who posted…
Computer-Aided Diagnosis of Low Grade Endometrial Stromal Sarcoma (LGESS)
Xinxin Yang, Mark Stamp
Low grade endometrial stromal sarcoma (LGESS) is rare form of cancer, accounting for about 0.2% of all uterine cancer cases. Approximately 75% of LGESS patients are initially misdi…
Machine Learning for Malware Evolution Detection
Lolitha Sresta Tupadha, Mark Stamp
Malware evolves over time and antivirus must adapt to such evolution. Hence, it is critical to detect those points in time where malware has evolved so that appropriate countermeas…