Ammar Ayman Battah

dblp:278/1792 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2025
0000-0001-9238-3114ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1 · 1 first-author · 1 since 2021Software engineering, systems software and programming languages · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Systems and software security · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Systems and software security › vulnerability discovery › machine-learning-based vulnerability detection
LLM-based vulnerability detection
0.912025
SecureFalcon: Are We There Yet in Automated Software Vulnerability Detection With LLMs? · IEEE Trans. Software Eng. 2025
Systems and software security › vulnerability discovery
software vulnerability detection
0.912025
SecureFalcon: Are We There Yet in Automated Software Vulnerability Detection With LLMs? · IEEE Trans. Software Eng. 2025
Systems and software security
vulnerability discovery
0.912025
SecureFalcon: Are We There Yet in Automated Software Vulnerability Detection With LLMs? · IEEE Trans. Software Eng. 2025

Methods — techniques the papers use, named apart from their topics

large language model · 0.9
YearPublicationVenuePosition
2025 SecureFalcon: Are We There Yet in Automated Software Vulnerability Detection With LLMs?
Mohamed Amine Ferrag, Ammar Ayman Battah, Norbert Tihanyi, Ridhi Jain, Diana Maimut, Fatima Alwahedi, Thierry Lestable, Narinderjit Singh Thandi, Abdechakour Mechri, Mérouane Debbah, Lucas C. Cordeiro
IEEE Trans. Software Eng.2
2022 A Trust and Reputation System for IoT Service Interactions
abstract
The rise of the Internet has enabled new types of relationships and online domains where trust must be computed rather than earned. However, classic trust and reputation techniques designed to support computational trust among Internet entities do not meet the scalability requirements and the resource constraints typical of Internet of Things (IoT) devices. This paper provides a general framework for managing computational trust where Blockchain technology and reputation systems are used in conjunction. Our framework relies on a reward-penalty scheme to offer a customizable, secure, and scalable trust architecture for IoT devices and the services they access. We implemented and tested a smart contract-based proof-of-concept, carrying out an analysis of its cost and security to verify the practical viability of our solution.
Ammar Ayman Battah, Youssef Iraqi, Ernesto Damiani
IEEE Trans. Netw. Serv. Manag.1