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Hamed Haddadi

7 papers hereh-index 263 citations9 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author1
  • last author6

Across the 7 of 7 papers where every author was matched, so the position is known.

fields
  • cs.CR4
  • cs.AI1
  • cs.CL1
  • cs.DC1
same name
  • Hamed Haddadi — 17 papers, h 8
  • Hamed Haddadi — 12 papers, h 3
  • Hamed Haddadi — 11 papers, h 4
  • Hamed Haddadi — 8 papers, h 4
  • Hamed Haddadi — 8 papers, h 2
  • Hamed Haddadi — 5 papers

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedTrustRAG: Enhancing Robustness and Trustworthiness in Retrieval-Augmented Generation

2 citations · 2 across the 6 of their papers we have counts for

collaborators
Showing cs.CRShow all

4 papers · 1 filter

cs.CR2025

AegisMCP: Online Graph Intrusion Detection for Tool-Augmented LLMs on Edge Devices

Zhonghao Zhan, Amir Al Sadi, Krinos Li +1

In this work, we study security of Model Context Protocol (MCP) agent toolchains and their applications in smart homes. We introduce AegisMCP, a protocol-level intrusion detector.…

cs.CR2025

REAL-IoT: Characterizing GNN Intrusion Detection Robustness under Practical Adversarial Attack

Zhonghao Zhan, Huichi Zhou, Hamed Haddadi

Graph Neural Network (GNN)-based network intrusion detection systems (NIDS) are often evaluated on single datasets, limiting their ability to generalize under distribution drift. F…

cs.CR2025

Poster: Enhancing GNN Robustness for Network Intrusion Detection via Agent-based Analysis

Zhonghao Zhan, Huichi Zhou, Hamed Haddadi

Graph Neural Networks (GNNs) show great promise for Network Intrusion Detection Systems (NIDS), particularly in IoT environments, but suffer performance degradation due to distribu…

cs.CR2025

Dynamic Probabilistic Noise Injection for Membership Inference Defense

Javad Forough, Hamed Haddadi

Membership Inference Attacks (MIAs) expose privacy risks by determining whether a specific sample was part of a model's training set. These threats are especially serious in sensit…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.