Farah Jemili - RankMe

38 papers B 3C 14Misc 2Journal 15Unranked 4

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Deep learning for intrusion detection in IoT networks. Peer Peer Netw. Appl. Mehdi Selem, Farah Jemili, Ouajdi Korbaa
2025	J	`jnl`	Detecting unknown intrusions from large heterogeneous data through ensemble learning. Intell. Syst. Appl. Farah Jemili, Khaled Jouini, Ouajdi Korbaa
2025	J	`jnl`	Improved human activity recognition through controllable GAN-Generated synthetic data and large Language models for classification. Clust. Comput. Mohamed Hedi Djemaa, Farah Jemili, Imen Megdiche, Rafika Thabet, Elyes Lamine, Ouajdi Korbaa
2025	J	`jnl`	Intelligent Control Agent For IoT Network Attack Detection: A Machine Learning Approach. IT Prof. Hussam Hussein Abu Munshar, Farah Jemili, Ouajdi Korbaa, Mohammad Alauthman
2025	J	`jnl`	Intrusion detection based on concept drift detection and online incremental learning. Int. J. Pervasive Comput. Commun. Farah Jemili, Khaled Jouini, Ouajdi Korbaa
2024	C	`conf`	A Deep Learning System for Early Detection of Diabetic Retinopathy. AICCSA Mayssa Emmales, Farah Jemili, Ouajdi Korbaa, Ferid Kamel
2024	J	`jnl`	Active intrusion detection and prediction based on temporal big data analytics. Int. J. Knowl. Based Intell. Eng. Syst. Farah Jemili, Ouajdi Korbaa
2024	C	`conf`	Enhanced Human Activity Recognition Using Controllable GANs for Synthetic Data Generation. AICCSA Mohamed Hedi Djemaa, Imen Megdiche, Farah Jemili, Rafika Thabet, Elyes Lamine, Ouajdi Korbaa
2024	J	`jnl`	Intrusion detection based on ensemble learning for big data classification. Clust. Comput. Farah Jemili, Rahma Meddeb, Ouajdi Korbaa
2024	J	`jnl`	Intrusion detection in cyber-physical system using rsa blockchain technology. Multim. Tools Appl. Ahmed Aljabri, Farah Jemili, Ouajdi Korbaa
2024	J	`jnl`	Real-time data fusion for intrusion detection in industrial control systems based on cloud computing and big data techniques. Clust. Comput. Ahlem Abid, Farah Jemili, Ouajdi Korbaa
2023	J	`jnl`	A deep learning-based intrusion detection approach for mobile Ad-hoc network. Soft Comput. Rahma Meddeb, Farah Jemili, Bayrem Triki, Ouajdi Korbaa
2023	J	`jnl`	Distributed deep learning approach for intrusion detection system in industrial control systems based on big data technique and transfer learning. J. Inf. Telecommun. Ahlem Abid, Farah Jemili, Ouajdi Korbaa
2023	J	`jnl`	Intelligent intrusion detection based on fuzzy Big Data classification. Clust. Comput. Farah Jemili
2023	C	`conf`	Intrusion Detection based on Incremental Learning. CW Islem Chouchene, Farah Jemili
2023	C	`conf`	Towards Deep Learning and Blockchain-based Intrusion Detection System. CW Mohamed Ala Eddine Bahri, Farah Jemili, Ouajdi Korbaa
2023	J	`jnl`	Towards data fusion-based big data analytics for intrusion detection. J. Inf. Telecommun. Farah Jemili
2022	—	`conf`	Distributed Architecture of an Intrusion Detection System in Industrial Control Systems. ICCCI (CCIS Volume) Ahlem Abid, Farah Jemili, Ouajdi Korbaa
2022	—	`conf`	Ensemble Learning Based Big Data Classification for Intrusion Detection. ISDA (1) Kamel Yasmine Kamel, Farah Jemili, Rahma Meddeb
2021	—	`conf`	A Deep Learning Approach for Intrusion Detection. HPCC/DSS/SmartCity/DependSys Roua Dhahbi, Farah Jemili
2021	—	`conf`	A Multi-Agent-Based System for Intrusion Detection. KES-AMSTA Tesnim Younes, Farah Jemili
2020	Misc	`conf`	A new features vector matching for big heterogeneous data in intrusion detection context. ATSIP Marwa Elayni, Farah Jemili, Ouajdi Korbaa, Basel Solaiman
2020	B	`conf`	Intrusion Detection based on Graph oriented Big Data Analytics. KES Ahlem Abid, Farah Jemili
2019	B	`conf`	Anomaly-based Behavioral Detection in Mobile Ad-Hoc Networks KES Rahma Meddeb, Farah Jemili, Bayrem Triki, Ouajdi Korbaa
2019	C	`conf`	Big Data Processing for Intrusion Detection System Context: A Review. ISDA Marwa Elayni, Farah Jemili, Ouajdi Korbaa, Basel Solaiman
2019	J	`jnl`	Comparative Study between Big Data Analysis Techniques in Intrusion Detection. Big Data Cogn. Comput. Mounir Hafsa, Farah Jemili
2019	C	`conf`	Dataset for Intrusion Detection in Mobile Ad-Hoc Networks. ISDA Rahma Meddeb, Bayrem Triki, Farah Jemili, Ouajdi Korbaa
2019	C	`conf`	Distributed Architecture of Snort IDS in Cloud Environment. ISDA Mondher Essid, Farah Jemili, Ouajdi Korbaa
2018	C	`conf`	An Effective IDS Against Routing Attacks on Mobile Ad-Hoc Networks. SoMeT Rahma Meddeb, Bayrem Triki, Farah Jemili, Ouajdi Korbaa
2017	Misc	`conf`	Neuro-fuzzy and genetic-fuzzy based approaches in intrusion detection: Comparative study. SoftCOM Imen Gaied, Farah Jemili, Ouajdi Korbaa
2016	C	`conf`	A Genetic-Fuzzy Classification Approach to Improve High-Dimensional Intrusion Detection System. ISDA Imen Gaied, Farah Jemili, Ouajdi Korbaa
2016	B	`conf`	Combining intrusion detection datasets using MapReduce. SMC Mondher Essid, Farah Jemili
2016	C	`conf`	Intrusion detection based on genetic fuzzy classification system. AICCSA Mariem Belhor, Farah Jemili
2015	C	`conf`	Intrusion detection based on Neuro-Fuzzy classification. AICCSA Imen Gaied, Farah Jemili, Ouajdi Korbaa
2009	J	`jnl`	Hybrid Intrusion Detection and Prediction multiAgent System HIDPAS CoRR Farah Jemili, Montaceur Zaghdoud, Mohamed Ben Ahmed
2009	C	`conf`	Intrusion detection based on "Hybrid" propagation in Bayesian Networks. ISI Farah Jemili, Montaceur Zaghdoud, Mohamed Ben Ahmed
2007	C	`conf`	A Framework for an Adaptive Intrusion Detection System using Bayesian Network. ISI Farah Jemili, Montaceur Zaghdoud, Mohamed Ben Ahmed
2006	C	`conf`	Comparative Study between Bayesian Network and Possibilistic Network in Intrusion Detection. SECRYPT Najla Arfaoui, Farah Jemili, Montaceur Zaghdoud, Mohamed Ben Ahmed

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.