Dr. Casimer Decusatis

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Academic Career Length

12Years (2015-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 May 2024 in Machine Learning and Knowledge Extraction

Journal Article

Quantum-Enhanced Representation Learning: A Quanvolutional Autoencoder Approach against DDoS Threats

Motivated by the growing threat of distributed denial-of-service (DDoS) attacks and the emergence of quantum computing, this study introduces a novel “quanvolutional autoencoder” architecture for learning representations. The architecture leverages the computational advantages of quantum mechanics to improve upon traditional machine learning techniques. Specifically, the quanvolutional autoencoder employs randomized quantum circuits to analyze time-series data from DDoS attacks, offering a robust alternative to classical convolutional neural networks. Experimental results suggest that the quanvolutional autoencoder performs similarly to classical models in visualizing and learning from DDoS hive plots and leads to faster convergence and learning stability. These findings suggest that quantum machine learning holds significant promise for advancing data analysis and visualization in cybersecurity. The study highlights the need for further research in this fast-growing field, particularly for unsupervised anomaly detection.

10 March 2023 in Cryptography

Journal Article

Cybersecurity Test Bed for Smart Contracts

Blockchain, smart contracts, and related concepts have emerged in recent years as a promising technology for cryptocurrency, NFTs, and other areas. However, there are still many security issues that must be addressed as these technologies evolve. This paper reviews some of the leading social engineering attacks on smart contracts, as well as several vulnerabilities which result from insecure code development. A smart contract test bed is constructed using Solidity and a Metamask wallet to evaluate vulnerabilities such as insecure arithmetic, denial of service, and re-entrancy attacks. Cross-chain vulnerabilities and potential vulnerabilities resulting from layer 2 side-chain processing were also investigated. Mitigation best practices are proposed based on the experimental results.

28 October 2020

Conference Paper

Towards Adversarially Robust DDoS-Attack Classification

Michael Guarino

Pablo Rivas

Casimer Decusatis

On the frontier of cybersecurity are a class of emergent security threats that learn to find vulnerabilities in machine learning systems. A supervised machine learning classifier learns a mapping from x to y where x is the input features and y is a vector of associated labels. Neural Networks are state of the art performers on most vision, audio, and natural language processing tasks. Neural Networks have been shown to be vulnerable to adversarial perturbations of the input, which cause them to misclassify with high confidence. Adversarial perturbations are small but targeted modifications to the input often undetectable by the human eye. Adversarial perturbations pose risk to applications that rely on machine learning models. Neural Networks have been shown to be able to classify distributed denial of service (DDoS) attacks by learning a dataset of attack characteristics visualized using three-axis hive plots. In this work we present a novel application of a classifier trained to classify DDoS attacks that is robust to some of the most common, known, classes of gradient-based and gradient-free adversarial attacks.

20 June 2019

Book Chapter

Design and Implementation of a Research and Education Cybersecurity Operations Center

Casimer Decusatis

R. Cannistra

A. Labouseur

M. Johnson

29 November 2023 in Electronics

Journal Article

A Black Box Comparison of Machine Learning Reverse Image Search for Cybersecurity OSINT Applications

Esther Nanjala Wekesa

Casimer Decusatis

Andy Zhu

Machine learning algorithms for reverse image search (a subset of open source intelligence or OSINT) provide a free, useful tool for determining the content of an image, where and when it was captured, and, in some cases, whether it has been digitally modified. Using a test data set of 24 images, we compared the performance of reverse image search for Google, Bing, and Yandex. Our black box experimental results are presented for three different categories of images (uncluttered images, images with significant background clutter, and facial recognition). The total number of correct images was highest for Google (65%), while Bing (55%) and Yandex (50%) yielded different results. Google was also the best at identifying cluttered, uncluttered, and facial images. We compare these results with previous studies and review how relative performance has changed over time. Accurate recognition rates for all reverse search platforms tested were higher for original images not previously uploaded, as opposed to images used in earlier studies. We validate our results using exchangeable image file format (EXIF) data and error level analysis (ELA) of selected images. Based on these results, best practices for OSINT image investigation are proposed.

4 July 2025 in Electronics

Journal Article

Cybersecurity Applications of Near-Term Large Language Models

This paper examines near-term generative large language models (GenLLM) for cybersecurity applications. We experimentally study three common use cases, namely the use of GenLLM as a digital assistant, analysts for threat hunting and incident response, and analysts for access management in zero trust systems. In particular, we establish that one of the most common GenLLMs, ChatGPT, can pass cybersecurity certification exams for security fundamentals, hacking and penetration testing, and mobile device security, as well as perform competitively in cybersecurity ethics assessments. We also identify issues associated with hallucinations in these environments. The ability of ChatGPT to analyze network scans and security logs is also evaluated. Finally, we attempt to jailbreak ChatGPT in order to assess its application to access management systems.

About Me

Dr. Casimer DeCusatis is an Associate Professor of Information Technology and Systems at the Marist University. He leads cybersecurity education and research for the School of Computer Science and Mathematics, including the cybersecurity major and minor, summer pre-college program, student education and research security operations center (SOC), and various National Science Foundation research efforts. He is a former IBM Distinguished Engineer and IBM Master Inventor. He is the recipient of several industry awards, including the IEEE Kiyo Tomiyasu Award, the Sigma Xi Walston Chubb Award for Innovation, the EDN Innovator of the Year Award, the Mensa Research Foundation Copper Black Award for Creative Achievement, the Penn State Outstanding Scholar Alumnus Award and Mark Luchinsky Memorial Lecture, and the IEEE/HKN Outstanding Young Electrical Engineer award (including a citation from the President of the United States and an American flag flown in his honor over the U.S. Capitol). He received the M.S. and Ph.D. degrees from Rensselaer Polytechnic Institute (Troy, N.Y.) in 1988 and 1990, respectively, and the B.S. degree magna cum laude in the Engineering Science Honors Program from the Pennsylvania State University (University Park, PA) in 1986. He is a Fellow of the IEEE, Optical Society of America, and SPIE (the International Optical Engineering Society), a member of the Order of the Engineer, Tau Beta Pi, Eta Kappa Nu, and various other professional organizations.

Dr. Casimer Decusatis

Dr. Casimer Decusatis

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About Me