Tal Shapira - RankMe

21 papers A 1B 4Misc 1Journal 12Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	B	`conf`	FlowPicClip: Improving Network Traffic Classification Using Language Supervision. WiMob Daniel Shalev, Tal Shapira, Yuval Shavitt
2025	J	`jnl`	IRR-Based AS Type of Relationship Inference. CoRR Amit Zulan, Omer Miron, Tal Shapira, Yuval Shavitt
2025	B	`conf`	Non-uniformity is All You Need: Efficient and Timely Encrypted Traffic Classification With ECHO. ICNP Shilo Daum, Tal Shapira, Anat Bremler-Barr, David Hay
2025	B	`conf`	ZEAL in the Cloud: IoT Labeling System. NOMS Bar Meyuhas, Anat Bremler-Barr, Tal Shapira
2024	J	`jnl`	IoT Device Labeling Using Large Language Models. CoRR Bar Meyuhas, Anat Bremler-Barr, Tal Shapira
2024	J	`jnl`	Non-uniformity is All You Need: Efficient and Timely Encrypted Traffic Classification With ECHO. CoRR Shilo Daum, Tal Shapira, Anat Bremler-Barr, David Hay
2024	J	`jnl`	Self-Supervised Traffic Classification: Flow Embedding and Few-Shot Solutions. IEEE Trans. Netw. Serv. Manag. Eyal Horowicz, Tal Shapira, Yuval Shavitt
2024	J	`jnl`	Unveiling Hidden Links Between Unseen Security Entities. CoRR Daniel Alfasi, Tal Shapira, Anat Bremler Barr
2024	—	`conf`	VulnScopper: Unveiling Hidden Links Between Unseen Security Entities. GNNet@CoNEXT Daniel Alfasi, Tal Shapira, Anat Bremler-Barr
2023	Misc	`conf`	Next-Generation Security Entity Linkage: Harnessing the Power of Knowledge Graphs and Large Language. SYSTOR Daniel Alfasi, Tal Shapira, Anat Bremler-Barr
2022	A	`conf`	A few shots traffic classification with mini-FlowPic augmentations. IMC Eyal Horowicz, Tal Shapira, Yuval Shavitt
2022	J	`jnl`	AP2Vec: An Unsupervised Approach for BGP Hijacking Detection. IEEE Trans. Netw. Serv. Manag. Tal Shapira, Yuval Shavitt
2022	J	`jnl`	BGP2Vec: Unveiling the Latent Characteristics of Autonomous Systems. IEEE Trans. Netw. Serv. Manag. Tal Shapira, Yuval Shavitt
2022	J	`jnl`	Fast and lean encrypted Internet traffic classification. Comput. Commun. Sangita Roy, Tal Shapira, Yuval Shavitt
2022	J	`jnl`	SASA: Source-Aware Self-Attention for IP Hijack Detection. IEEE/ACM Trans. Netw. Tal Shapira, Yuval Shavitt
2021	J	`jnl`	FlowPic: A Generic Representation for Encrypted Traffic Classification and Applications Identification. IEEE Trans. Netw. Serv. Manag. Tal Shapira, Yuval Shavitt
2020	—	`conf`	A Deep Learning Approach for IP Hijack Detection Based on ASN Embedding. NetAI@SIGCOMM Tal Shapira, Yuval Shavitt
2020	J	`jnl`	Efficient Data-Dependent Learnability. CoRR Yaniv Fogel, Tal Shapira, Meir Feder
2020	B	`conf`	Unveiling the Type of Relationship Between Autonomous Systems Using Deep Learning. NOMS Tal Shapira, Yuval Shavitt
2019	—	`conf`	FlowPic: Encrypted Internet Traffic Classification is as Easy as Image Recognition. INFOCOM Workshops Tal Shapira, Yuval Shavitt
2018	J	`jnl`	Investigation of the Coin Snapping Phenomenon in Linearly Compliant Robot Grasps. IEEE Trans. Robotics Tal Shapira, Elon D. Rimon, Amir Shapiro

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.