Classification of various attacks and their defence mechanism in online social networks: a survey

SR Sahoo, BB Gupta - Enterprise Information Systems, 2019 - Taylor & Francis
Due to the popularity and user friendliness of the Internet, numbers of users of online social
networks (OSNs) and social media have grown significantly. However, globally utilised …

Learn to be efficient: Build structured sparsity in large language models

H Zheng, X Bai, X Liu, ZM Mao… - Advances in …, 2025 - proceedings.neurips.cc
Abstract Large Language Models (LLMs) have achieved remarkable success with their
billion-level parameters, yet they incur high inference overheads. The emergence of …

Mush: Multi-stimuli hawkes process based sybil attacker detector for user-review social networks

Z Qu, C Lyu, CH Chi - IEEE Transactions on Network and …, 2022 - ieeexplore.ieee.org
User-Review Social Networks (URSNs) now become the targets of Sybil attacks, where fake
reviews are posted by attackers to raise the reputation of listed services or products. Unlike …

DatingSec: Detecting malicious accounts in dating apps using a content-based attention network

X He, Q Gong, Y Chen, Y Zhang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Dating apps have gained tremendous popularity during the past decade. Compared with
traditional offline dating means, dating apps ease the process of partner finding significantly …

DeepScan: Exploiting deep learning for malicious account detection in location-based social networks

Q Gong, Y Chen, X He, Z Zhuang… - IEEE …, 2018 - ieeexplore.ieee.org
Our daily lives have been immersed in widespread location-based social networks (LBSNs).
As an open platform, LBSNs typically allow all kinds of users to register accounts. Malicious …