SelectedWorks - Edoardo Serra

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Selected Works of Edoardo Serra

Assistant Professor, Department of Computer Science

Starting at Boise State University in 2015, Dr. Edoardo Serra is an Assistant Professor for the Department of Computer Science. He received a Ph.D. in Systems and Computer Engineering and an M.Sc. in Computer Engineering, and a B.Sc. in Computer Engineering all from the University of Calabria, Italy. Dr. Serra’s research interests currently include the areas of cybersecurity, artificial intelligence, and game theory to name a few. Some of his research has focused on subjects such as adversarial defense of enterprise systems, optimal monitoring, and malware detection prediction by using host behavioral models. His professional experience includes teaching Introduction of Artificial Intelligence at the University of Maryland and assisting with such courses as Languages and Formal Systems and Object-Oriented Programming for the University of Calabria.

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About Edoardo Serra

Starting at Boise State University in 2015, Dr. Edoardo Serra is an Assistant Professor for the Department of Computer Science. He received a Ph.D. in Systems and Computer Engineering and an M.Sc. in Computer Engineering, and a B.Sc. in Computer Engineering all from the University of Calabria, Italy. Dr. Serra’s research interests currently include the areas of cybersecurity, artificial intelligence, and game theory to name a few. Some of his research has focused on subjects such as adversarial defense of enterprise systems, optimal monitoring, and malware detection prediction by using host behavioral models. His professional experience includes teaching Introduction of Artificial Intelligence at the University of Maryland and assisting with such courses as Languages and Formal Systems and Object-Oriented Programming for the University of Calabria.

2015 - Present	Assistant Professor, Boise State University ‐ Department of Computer Sciences
Institution Organization Position Type Position Title Time Period to Options Current Position Affiliate my profile with this Institution

Electrical and Computer Engineering

Systems and Computer Engineering and Information Technology

2018 - Best Paper Award for "Inferring Bad Entities Through the Panama Papers Network", FOSINT-SI in conjunction with IEEE/ACM

http://orcid.org/0000-0003-0689-5063

Articles (19)

The Geometrical Shapes of Violence: Predicting and Explaining Terrorist Operations Through Graph Embeddings (Gian Maria Campedelli, Janet Layne, Jack Herzoff, et al.) Journal of Complex Networks (2022)

Behaviours across terrorist groups differ based on a variety of factors, such as groups’ resources or objectives. We here show that organizations can also be distinguished by network representations of their operations. We provide evidence ...

Machine Learning Methods for Generating High Dimensional Discrete Datasets (Giuseppe Manco, Ettore Ritacco, Antonino Rullo, et al.) WIREs: Data Mining and Knowledge Discovery (2022)

The development of platforms and techniques for emerging Big Data and Machine Learning applications requires the availability of real-life datasets. A possible solution is to synthesize datasets that reflect patterns of real ones using a ...

cuTS: Scaling Subgraph Isomorphism on Distributed Multi-GPU Systems Using Trie Based Data Structure (Lizhi Xiang, Arif Khan, Edoardo Serra, et al.) SC '21: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (2021)

Subgraph isomorphism is a pattern-matching algorithm widely used in many domains such as chem-informatics, bioinformatics, databases, and social network analysis. It is computationally expensive and is a proven NP-hard problem. The massive parallelism in GPUs ...

A Multi-Perspective Approach for the Analysis of Complex Business Processes Behavior (Antonella Guzzo, Mikel Joaristi, Antonino Rullo, et al.) Expert Systems with Applications (2021)

Business processes are often monitored by transactional information systems that produce massive dataset called event logs. Such logs contain the process execution traces, typically characterized by heterogeneous and high-dimensional data. Process mining techniques offer a ...

SIR-GN: A Fast Structural Iterative Representation Learning Approach for Graph Nodes (Mikel Joaristi and Edoardo Serra) ACM Transactions on Knowledge Discovery from Data (2021)

Graph representation learning methods have attracted an increasing amount of attention in recent years. These methods focus on learning a numerical representation of the nodes in a graph. Learning these representations is a powerful instrument ...

Multi-Modal Social and Psycho-Linguistic Embedding via Recurrent Neural Networks to Identify Depressed Users in Online Forums (Anu Shrestha, Edoardo Serra and Francesca Spezzano) Network Modeling Analysis in Health Informatics and Bioinformatics (2020)

Depression is the most common mental illness in the US, with 6.7% of all adults experiencing a major depressive episode. Unfortunately, depression extends to teens and young users as well and researchers have observed an ...

Top-κ User-Specified Preferred Answers in Massive Graph Databases (Noseong Park, Andrea Pugliese, Edoardo Serra, et al.) Data & Knowledge Engineering (2020)

There are numerous applications where users wish to identify subsets of vertices in a social network or graph database that are of interest to them. They may specify sets of patterns and vertex properties, and ...

PIE: A Data-Driven Payoff Inference Engine for Strategic Security Applications (Haipeng Chen, Mohammad T. Hajiaghayi, Sarit Kraus, et al.) IEEE Transactions on Computational Social Systems (2020)

Although most game theory models assume that payoff matrices are provided as input, getting payoff matrices in strategic games (e.g., corporate negotiations and counter-terrorism operations) has proven difficult. To tackle this challenge, we propose a ...

Detecting Suspicious Entities in Offshore Leaks Networks (Mikel Joaristi, Edoardo Serra and Francesca Spezzano) Social Network Analysis and Mining (2019)

The ICIJ Offshore Leaks Database represents a large set of relationships between people, companies, and organizations involved in the creation of offshore companies in tax-heaven territories, mainly for hiding their assets. This data are organized ...

Extending Inverse Frequent Itemsets Mining to Generate Realistic Datasets: Complexity, Accuracy and Emerging Applications (Domenico Saccá, Edoardo Serra and Antonio Rullo) Data Mining and Knowledge Discovery (2019)

The development of novel platforms and techniques for emerging “Big Data” applications requires the availability of real-life datasets for data-driven experiments, which are however not accessible in most cases for various reasons, e.g., confidentiality, privacy ...

Hybrid Adversarial Defense: Merging Honeypots and Traditional Security Methods (Tanmoy Chakraborty, Sushil Jajodia, Noseong Park, et al.) Journal of Computer Security (2018)

Most past work on honeypots has made two assumptions: (i) they assume that the only defensive measure used is a honeypot mechanism, and (ii) they do not consider both rational and subrational adversaries and do ...

A Probabilistic Logic of Cyber Deception (Sushil Jajodia, Noseong Park, Fabio Pierazzi, et al.) IEEE Transactions on Information Forensics and Security (2017)

Malicious attackers often scan nodes in a network in order to identify vulnerabilities that they may exploit as they traverse the network. In this paper, we propose that the system generates a mix of true ...

Pareto Optimal Security Resource Allocation for Internet of Things (Antonino Rullo, Daniele Midi, Edoardo Serra, et al.) ACM Transactions on Privacy and Security (TOPS) (2017)

In many Internet of Thing (IoT) application domains security is a critical requirement, because malicious parties can undermine the effectiveness of IoT-based systems by compromising single components and/or communication channels. Thus, a security infrastructure is ...

Using Temporal Probabilistic Logic for Optimal Monitoring of Security Events with Limited Resources (Sushil Jajodia, Noseong Park, Edoardo Serra, et al.) Journal of Computer Security (2016)

Managed security services (MSS) are becoming increasingly popular today. In MSS, enterprises contract a security firm such as Symantec or IBM to manage security of their enterprise network. MSS vendors thus have a small pool ...

Using Temporal Probabilistic Logic for Optimal Monitoring of Security Events with Limited Resources (Sushil Jajodia, Noseong Park, Edoardo Serra, et al.) Journal of Computer Security (2016)

Managed security services (MSS) are becoming increasingly popular today. In MSS, enterprises contract a security firm such as Symantec or IBM to manage security of their enterprise network. MSS vendors thus have a small pool ...

Saving Rhinos with Predictive Analytics (Noseong Park, Edoardo Serra and V. S. Subrahmanian) IEEE Intelligent Systems (2015)

This article, the first entry in the new Predictive Analytics column, looks at the problem of animal poaching. The authors describe their Anti-Poaching Engine system, which builds on behavior models of both rhinos and poachers ...

Pareto-Optimal Adversarial Defense of Enterprise Systems (Edoardo Serra, Sushil Jajodia, Andrea Pugliese, et al.) ACM Transactions on Information and System Security (2015)

The National Vulnerability Database (NVD) maintained by the US National Institute of Standards and Technology provides valuable information about vulnerabilities in popular software, as well as any patches available to address these vulnerabilities. Most enterprise ...

An Effective GPU-Based Approach to Probabilistic Query Confidence Computation (Edoardo Serra and Francesca Spezzano) IEEE Transactions on Knowledge and Data Engineering (2015)

—In recent years, probabilistic data management has received a lot of attention due to several applications that deal with uncertain data: RFID systems, sensor networks, data cleaning, scientific and biomedical data management, and approximate schema ...

A Survey of Quantitative Models of Terror Group Behavior and an Analysis of Strategic Disclosure of Behavioral Models (Edoardo Serra and V. S. Subrahmanian) IEEE Transactions on Computational Social Systems (2014)

There are many applications (e.g., counter-terrorism) where we can automatically learn a quantitative model from realworld data about terror group behavior. In this paper, we first provide a survey of quantitative models of terrorist groups. ...

Disinformation in Open Online Media: 4th Multidisciplinary International Symposium, MISDOOM 2022, Boise, ID, USA, October 11-12, 2022, Proceedings (Francesca Spezzano, Adriana Amaral, Davide Ceolin, et al.) Faculty & Staff Authored Books (2022)

This book constitutes the refereed proceedings of the 4th Multidisciplinary International Symposium on Disinformation in Open Online Media, MISDOOM 2022, held in October 2022. The 7 full papers and 3 short papers were carefully reviewed ...

Optimal Placement of Security Resources for the Internet of Things (Antonio Rullo, Edoardo Serra, Elisa Bertino, et al.) The Internet of Things for Smart Urban Ecosystems (2019)

In many Internet of Thing application domains security is a critical requirement, because malicious parties can undermine the effectiveness of IoT-based systems by compromising single components and/or communication channels. Thus, a security infrastructure is needed ...

Redundancy as a Measure of Fault-Tolerance for the Internet of Things: A Review (Antonio Rullo, Edoardo Serra and Jorge Lobo) Policy-Based Autonomic Data Governance (2019)

In this paper we review and analyze redundancy-based fault-tolerant techniques for the IoT as a paradigm to support two of the main goals of computer security: availability and integrity. We organized the presentation in terms ...

A Novel Method to Enable Transfer Learning of Structural Graph Representations (Janet Layne and Edoardo Serra) 2023 IEEE International Conference on Big Data (BigData) (2023)

Graph Representation Learning (GRL) methods which effectively capture a node’s neighborhood structure in their representations can show excellent performance on important machine learning tasks such as node and graph classification. Recent work has focused on ...

Generating Synthetic Discrete Datasets with Machine Learning (Giuseppe Manco, Ettore Ritacco, Antonino Rullo, et al.) CEUR Workshop Proceedings (2022)

The real data are not always available/accessible/sufficient or in many cases they are incomplete and lacking in semantic content necessary to the definition of optimization processes. In this paper we discuss about the synthetic data ...

A Few Shot Transfer Learning Approach Identifying Private Images with Fast User Personalization (Edoardo Serra, Sujeet Ayyapureddi, Qudrat E. Alahy Ratul, et al.) 2021 IEEE International Conference on Big Data (Big Data) (2021)

As online image sharing has become commonplace, researchers have acknowledged the need to assist users in detecting sensitive (or private) images. However, image privacy classification tasks have shown to be nontrivial, as the designation of ...