2 citations · 3 across the 11 of their papers we have counts for
11 papers
Image-Based Techniques and Ensemble Soft Voting for Malware Classification
Sushant Rakesh Lokhande, Fabio Di Troia, Martin Jurecek +1
In this chapter, we investigate image-based malware family classification using an ensemble learning framework and a soft voting strategy. We consider malware binaries that have be…
On the Effect of Bit-Level Parameter Perturbations in Machine Learning and Deep Learning Models
Akanksha Raghapur, Mark Stamp
In this chapter, we investigate how classical machine learning models respond to small, targeted modifications in their parameters. We compare and contrast these results to analogo…
CAM-Guided Saliency Cutout and Image-Based Malware Classification
Yasaman Ebrahimi, Martin Jurecek, Mark Stamp
Dropout regularization is commonly used to reduce overfitting by removing parts of a neural network during training. For Convolutional Neural Networks (CNN), cutouts serve a somewh…
RawMal-TF: Raw Malware Dataset Labeled by Type and Family
David Bálik, Martin Jureček, Mark Stamp
This work addresses the challenge of malware classification using machine learning by developing a novel dataset labeled at both the malware type and family levels. Raw binaries we…
Transforming Chatbot Text: A Sequence-to-Sequence Approach
Natesh Reddy, Mark Stamp
Due to advances in Large Language Models (LLMs) such as ChatGPT, the boundary between human-written text and AI-generated text has become blurred. Nevertheless, recent work has dem…
Energy Considerations for Large Pretrained Neural Networks
Leo Mei, Mark Stamp
Increasingly complex neural network architectures have achieved phenomenal performance. However, these complex models require massive computational resources that consume substanti…
Multimodal Techniques for Malware Classification
Jonathan Jiang, Mark Stamp
The threat of malware is a serious concern for computer networks and systems, highlighting the need for accurate classification techniques. In this research, we experiment with mul…
Malware Classification using a Hybrid Hidden Markov Model-Convolutional Neural Network
Ritik Mehta, Olha Jureckova, Mark Stamp
The proliferation of malware variants poses a significant challenges to traditional malware detection approaches, such as signature-based methods, necessitating the development of…
Distinguishing Chatbot from Human
Gauri Anil Godghase, Rishit Agrawal, Tanush Obili +1
There have been many recent advances in the fields of generative Artificial Intelligence (AI) and Large Language Models (LLM), with the Generative Pre-trained Transformer (GPT) mod…
Online Clustering of Known and Emerging Malware Families
Olha Jurečková, Martin Jureček, Mark Stamp
Malware attacks have become significantly more frequent and sophisticated in recent years. Therefore, malware detection and classification are critical components of information se…
Classification and Online Clustering of Zero-Day Malware
Olha Jurečková, Martin Jureček, Mark Stamp +2
A large amount of new malware is constantly being generated, which must not only be distinguished from benign samples, but also classified into malware families. For this purpose,…