Quanqiang Zhou

11 papers Journal 11

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A semi-supervised detection approach for group shilling attacks based on pseudo-labels. Expert Syst. Appl. Quanqiang Zhou, Kang Li
2025	J	`jnl`	Autoencoder-Based Poisoning Attack Detection in Graph Recommender Systems. Inf. Quanqiang Zhou, Xi Zhao, Xiaoyue Zhang
2024	J	`jnl`	A recommendation attack detection approach integrating CNN with Bagging. Comput. Secur. Quanqiang Zhou, Cheng Huang
2024	J	`jnl`	PileNet: A high-and-low pass complementary filter with multi-level feature refinement for salient object detection. J. Vis. Commun. Image Represent. Xiaoqi Yang, Liangliang Duan, Quanqiang Zhou
2023	J	`jnl`	Recommendation attack detection based on improved Meta Pseudo Labels. Knowl. Based Syst. Quanqiang Zhou, Kang Li, Liangliang Duan
2021	J	`jnl`	Semi-supervised recommendation attack detection based on Co-Forest. Comput. Secur. Quanqiang Zhou, Liangliang Duan
2020	J	`jnl`	Recommendation attack detection based on deep learning. J. Inf. Secur. Appl. Quanqiang Zhou, Jinxia Wu, Liangliang Duan
2016	J	`jnl`	Supervised approach for detecting average over popular items attack in collaborative recommender systems. IET Inf. Secur. Quanqiang Zhou
2015	J	`jnl`	Ensemble detection model for profile injection attacks in collaborative recommender systems based on BP neural network. IET Inf. Secur. Fuzhi Zhang, Quanqiang Zhou
2014	J	`jnl`	HHT-SVM: An online method for detecting profile injection attacks in collaborative recommender systems. Knowl. Based Syst. Fuzhi Zhang, Quanqiang Zhou
2012	J	`jnl`	A Meta-learning-based Approach for Detecting Profile Injection Attacks in Collaborative Recommender Systems. J. Comput. Fuzhi Zhang, Quanqiang Zhou

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.

In 2025, he co-authored Assessing the Effectiveness of the Tigress Obfuscator Against MOPSA and BinaryNinja, published at CheckMATE. The paper evaluates the effectiveness of obfuscation techniques and their impact on source-level static analysis and binary-level decompilation.

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.