2 citations · 4 across the 7 of their papers we have counts for
8 papers
Beyond the Hype: Evaluating LLM Integration and Practical Limitations in Security Operation Centers
Elnaz Rabieinejad, Ali Dehghantanha, Fattane Zarrinkalam +1
Large Language Models (LLMs) are increasingly being explored within Security Operation Centers (SOCs) to support text-heavy analytical work such as alert contextualization, inciden…
Diagnosing and Repairing Factual Errors in RAG under Budget Constraints
Soroush Hashemifar, Havva Alizadeh Noughabi, Fattane Zarrinkalam +1
Retrieval-Augmented Generation (RAG) improves the factuality of large language models by grounding responses in external evidence, yet real-world deployments remain fragile. Failur…
From Noise to Order: Learning to Rank via Denoising Diffusion
Sajad Ebrahimi, Bhaskar Mitra, Negar Arabzadeh +4
In information retrieval (IR), learning-to-rank (LTR) methods have traditionally limited themselves to discriminative machine learning approaches that model the probability of the…
Uncovering the Persuasive Fingerprint of LLMs in Jailbreaking Attacks
Havva Alizadeh Noughabi, Julien Serbanescu, Fattane Zarrinkalam +1
Despite recent advances, Large Language Models remain vulnerable to jailbreak attacks that bypass alignment safeguards and elicit harmful outputs. While prior research has proposed…
Quantifying Security Vulnerabilities: A Metric-Driven Security Analysis of Gaps in Current AI Standards
Keerthana Madhavan, Abbas Yazdinejad, Fattane Zarrinkalam +1
As AI systems integrate into critical infrastructure, security gaps in AI compliance frameworks demand urgent attention. This paper audits and quantifies security risks in three ma…
P3GNN: A Privacy-Preserving Provenance Graph-Based Model for APT Detection in Software Defined Networking
Hedyeh Nazari, Abbas Yazdinejad, Ali Dehghantanha +2
Software Defined Networking (SDN) has brought significant advancements in network management and programmability. However, this evolution has also heightened vulnerability to Advan…