77 citations · 86 across the 17 of their papers we have counts for
17 papers
CVE-TTP KG: Knowledge Graph Linking Software Vulnerabilities to Attack Behaviors
Swati Yadav, Dincy R. Arikkat, Basant Agarwal +3
In the evolving threat landscape, adversaries exploit software vulnerabilities to launch sophisticated attacks, challenging traditional defenses. Although databases like CVE and NV…
An AI-Based Solution for Secure Service Provisioning in IoT
Marco Arazzi, Mert Cihangiroglu, Serena Nicolazzo +2
As the Internet of Things (IoT) continues its rapid expansion, the attack surface grows accordingly, with emerging threats targeting smart objects and their interactions. In this e…
A Multi-task Mixture of Experts Framework for Malware Classification, Packing Detection, and Family Attribution
Jithin S., Roshin Sleeba C., Anvin Mariya P. B. +4
Malware classification remains a challenging problem due to its inherent heterogeneity, the presence of packed binaries, and the diverse distribution of malware families. Tradition…
GAS-Leak-LLM: Genetic Algorithm-Based Suffix Optimization for Black-Box LLM Jailbreaking
Aman Anifer, Vignesh Kumar Kembu, Vishnu M +4
Large Language Models (LLMs) constitute pivotal components within the AI-dominated information technology ecosystem. To mitigate risks associated with harmful or policy-violating o…
Towards Certified Malware Detection: Provable Guarantees Against Evasion Attacks
Nandakrishna Giri, Asmitha K. A., Serena Nicolazzo +2
Machine learning-based static malware detectors remain vulnerable to adversarial evasion techniques, such as metamorphic engine mutations. To address this vulnerability, we propose…
Security in LLM-as-a-Judge: A Comprehensive SoK
Aiman Al Masoud, Antony Anju, Marco Arazzi +6
LLM-as-a-Judge (LaaJ) is a novel paradigm in which powerful language models are used to assess the quality, safety, or correctness of generated outputs. While this paradigm has sig…
GShield: Mitigating Poisoning Attacks in Federated Learning
Sameera K. M., Serena Nicolazzo, Antonino Nocera +2
Federated Learning (FL) has recently emerged as a revolutionary approach to collaborative training Machine Learning models. In particular, it enables decentralized model training w…
Protecting Deep Neural Network Intellectual Property with Chaos-Based White-Box Watermarking
Sangeeth B, Serena Nicolazzo, Deepa K. +1
The rapid proliferation of deep neural networks (DNNs) across several domains has led to increasing concerns regarding intellectual property (IP) protection and model misuse. Train…
CTI Dataset Construction from Telegram
Dincy R. Arikkat, Sneha B. T., Serena Nicolazzo +4
Cyber Threat Intelligence (CTI) enables organizations to anticipate, detect, and mitigate evolving cyber threats. Its effectiveness depends on high-quality datasets, which support…
Enhancing Android Malware Detection with Retrieval-Augmented Generation
Saraga S., Anagha M. S., Dincy R. Arikkat +4
The widespread use of Android applications has made them a prime target for cyberattacks, significantly increasing the risk of malware that threatens user privacy, security, and de…
When Forgetting Triggers Backdoors: A Clean Unlearning Attack
Marco Arazzi, Antonino Nocera, Vinod P
Machine unlearning has emerged as a key component in ensuring ``Right to be Forgotten'', enabling the removal of specific data points from trained models. However, even when the un…
XBreaking: Understanding how LLMs security alignment can be broken
Marco Arazzi, Vignesh Kumar Kembu, Antonino Nocera +1
Large Language Models are fundamental actors in the modern IT landscape dominated by AI solutions. However, security threats associated with them might prevent their reliable adopt…
How Secure is Forgetting? Linking Machine Unlearning to Machine Learning Attacks
Muhammed Shafi K. P., Serena Nicolazzo, Antonino Nocera +1
As Machine Learning (ML) evolves, the complexity and sophistication of security threats against this paradigm continue to grow as well, threatening data privacy and model integrity…
SeCTIS: A Framework to Secure CTI Sharing
Dincy R. Arikkat, Mert Cihangiroglu, Mauro Conti +4
The rise of IT-dependent operations in modern organizations has heightened their vulnerability to cyberattacks. As a growing number of organizations include smart, interconnected d…
Privacy-Preserving in Blockchain-based Federated Learning Systems
Sameera K. M., Serena Nicolazzo, Marco Arazzi +4
Federated Learning (FL) has recently arisen as a revolutionary approach to collaborative training Machine Learning models. According to this novel framework, multiple participants…
NLP-Based Techniques for Cyber Threat Intelligence
Marco Arazzi, Dincy R. Arikkat, Serena Nicolazzo +4
In the digital era, threat actors employ sophisticated techniques for which, often, digital traces in the form of textual data are available. Cyber Threat Intelligence~(CTI) is rel…
Discerning Reliable Cyber Threat Indicators for Timely Cyber Threat Intelligence
Dincy R Arikkat, Vinod P., Rafidha Rehiman K. A. +3
In today's dynamic cybersecurity landscape, timely and accurate threat intelligence is essential for proactive defense. This study explores the potential of social media platforms…