Author name cluster

Edoardo Serra

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8 papers

1 author row

TIST Journal 2025 Journal Article

GenFighter: A Generative and Evolutive Textual Attack Removal

Md Athikul Islam
Edoardo Serra
Sushil Jajodia

Adversarial attacks pose significant challenges to deep neural networks (DNNs) such as Transformer models in natural language processing (NLP). This article introduces a novel defense strategy, called GenFighter, which enhances adversarial robustness by learning and reasoning on the training classification distribution. GenFighter identifies potentially malicious instances deviating from the distribution, transforms them into semantically equivalent instances aligned with the training data, and employs ensemble techniques for a unified and robust response. By conducting extensive experiments, we show that GenFighter outperforms state-of-the-art defenses in accuracy under attack and attack success rate metrics while maintaining the same or superior generalization capabilities. Additionally, it requires a high number of queries per attack, making the attack more challenging in real scenarios. Finally, The ablation study shows that our approach proficiently integrates transfer learning, a generative/evolutive procedure, and an ensemble method, providing an effective defense against NLP adversarial attacks.

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TIST Journal 2024 Journal Article

Analyzing Robustness of Automatic Scientific Claim Verification Tools against Adversarial Rephrasing Attacks

Janet Layne
Qudrat E. Alahy Ratul
Edoardo Serra
Sushil Jajodia

The coronavirus pandemic has fostered an explosion of misinformation about the disease, including the risk and effectiveness of vaccination. AI tools for automatic Scientific Claim Verification (SCV) can be crucial to defeat misinformation campaigns spreading through social media channels. However, over the past years, many concerns have been raised about the robustness of AI to adversarial attacks, and the field of automatic SCV is not exempt. The risk is that such SCV tools may reinforce and legitimize the spread of fake scientific claims rather than refute them. This article investigates the problem of generating adversarial attacks for SCV tools and shows that it is far more difficult than the generic NLP adversarial attack problem. The current NLP adversarial attack generators, when applied to SCV, often generate modified claims with entirely different meaning from the original. Even when the meaning is preserved, the modification of the generated claim is too simplistic (only a single word is changed), leaving many weaknesses of the SCV tools undiscovered. We propose T5-ParEvo, an iterative evolutionary attack generator, that is able to generate more complex and creative attacks while better preserving the semantics of the original claim. Using detailed quantitative and qualitative analyses, we demonstrate the efficacy of T5-ParEvo in comparison with existing attack generators.

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AAAI Conference 2024 Short Paper

Power Grid Anomaly Detection via Hybrid LSTM-GIN Model (Student Abstract)

Amelia Jobe
Richard Ky
Sandra Luo
Akshay Dhamsania
Sumit Purohit
Edoardo Serra

Cyberattacks on power grids pose significant risks to national security. Power grid attacks typically lead to abnormal readings in power output, frequency, current, and voltage. Due to the interconnected structure of power grids, abnormalities can spread throughout the system and cause widespread power outages if not detected and dealt with promptly. Our research proposes a novel anomaly detection system for power grids that prevents overfitting. We created a network graph to represent the structure of the power grid, where nodes represent power grid components like generators and edges represent connections between nodes such as overhead power lines. We combine the capabilities of Long Short-Term Memory (LSTM) models with a Graph Isomorphism Network (GIN) in a hybrid model to pinpoint anomalies in the grid. We train our model on each category of nodes that serves a similar structural purpose to prevent overfitting of the model. We then assign each node in the graph a unique signature using a GIN. Our model achieved a 99.92% accuracy rate, which is significantly higher than a version of our model without structural encoding, which had an accuracy level of 97.30%. Our model allows us to capture structural and temporal components of power grids and develop an attack detection system with high accuracy without overfitting.

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AAAI Conference 2022 Short Paper

Deep Learning Based Side Channel Attacks on Lightweight Cryptography (Student Abstract)

Alexander Benjamin
Jack Herzoff
Liljana Babinkostova
Edoardo Serra

Computing devices continue to be increasingly spread out within our everyday environments. Computers are embedded into everyday devices in order to serve the functionality of electronic components or to enable new services in their own right. Existing Substitution-Permutation Network (SPN) ciphers, such as the Advanced Encryption Standard (AES), are not suitable for devices where memory, power consumption or processing power is limited. Lightweight SPN ciphers, such as GIFT-128 provide a solution for running cryptography on low resource devices. The GIFT-128 cryptographic scheme is a building block for GIFT-COFB (Authenticated Encryption with Associated Data), one of the finalists in the ongoing NIST lightweight cryptography standardization process (NISTIR 8369). Determination of an adequate level of security and providing subsequent mechanisms to achieve it, is one of the most pressing problems regarding embedded computing devices. In this paper we present experimental results and comparative study of Deep Learning (DL) based Side Channel Attacks on lightweight GIFT-128. To our knowledge, this is the first study of the security of GIFT-128 against DL-based SCA attacks.

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AAAI Conference 2022 Short Paper

Identifying ATT&CK Tactics in Android Malware Control Flow Graph through Graph Representation Learning and Interpretability (Student Abstract)

Jeffrey Fairbanks
Andres Orbe
Christine Patterson
Edoardo Serra
Marion Scheepers

To mitigate a malware threat it is important to understand the malware’s behavior. The MITRE ATT&ACK ontology specifies an enumeration of tactics, techniques, and procedures (TTP) that characterize malware. However, absent are automated procedures that would characterize, given the malware executable, which part of the execution flow is connected with a specific TTP. This paper provides an automation methodology to locate TTP in a sub-part of the control flow graph that describes the execution flow of a malware executable. This methodology merges graph representation learning and tools for machine learning explanation.

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AAAI Conference 2022 Short Paper

Predicting RNA Mutation Effects through Machine Learning of High-Throughput Ribozyme Experiments (Student Abstract)

Joseph Kitzhaber
Ashlyn Trapp
James Beck
Edoardo Serra
Francesca Spezzano
Eric Hayden
Jessica Roberts

The ability to study ”gain of function” mutations has important implications for identifying and mitigating risks to public health and national security associated with viral infections. Numerous respiratory viruses of concern have RNA genomes (e. g. , SARS and flu). These RNA genomes fold into complex structures that perform several critical functions for viruses. However, our ability to predict the functional consequence of mutations in RNA structures continues to limit our ability to predict gain of function mutations caused by altered or novel RNA structures. Biological research in this area is also limited by the considerable risk of direct experimental work with viruses. Here we used small functional RNA molecules (ribozymes) as a model system of RNA structure and function. We used combinatorial DNA synthesis to generate all of the possible individual and pairs of mutations and used high-throughput sequencing to evaluate the functional consequence of each single- and double-mutant sequence. We used this data to train a Long Short-Term Memory model. This model was also used to predict the function of sequences found in the genomes of mammals with three mutations, which were not in our training set. We found a strong prediction correlation in all of our experiments.

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IS Journal 2015 Journal Article

Saving rhinos with predictive analytics

Noseong Park
Edoardo Serra
V.S. Subrahmanian

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 to protect as many animals as possible.

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IS Journal 2014 Journal Article

Behavior Informatics: A New Perspective

Longbing Cao
Thorsten Joachims
Can Wang
Eric Gaussier
Jinjiu Li
Yuming Ou
Dan Luo
Reza Zafarani

This installment of Trends & Controversies provides an array of perspectives on the latest research in behavior informatics. Longbing Cao introduces the work in "Behavior Informatics: A New Perspective. " Then, in "Behavior Computing, " Longbing Cao and Thorsten Joachims provide a basic overview of the topic. Next is "Coupled Behavior Representation, Modeling, Analysis, and Reasoning" by Can Wang, Longbing Cao, Eric Gaussier, Jinjiu Li, Yuming Ou, and Dan Luo. The fourth article is "Behavior Analysis in Social Media, " by Reza Zafarani and Huan Liu. The fifth article is "Group Recommendation and Behavior, " by Guandong Xu and Zhiang Wu. Gabriella Pasi wrote the sixth article, "Web Search and Behavior. " The seventh article, "Behaviors of IPTV Users, " is by Ya Zhang, Xiaokang Yang, and Hongyuan Zha. Finally, "Should Behavioral Models of Terror Groups Be Disclosed? " is by Edoardo Serra and V. S. Subrahmanian.

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