most citedExplainable and Transferable Adversarial Attack for ML-Based Network Intrusion Detectors

1 citations · 3 across the 7 of their papers we have counts for

collaborators

7 papers

cs.SI2024

Blockchain Takeovers in Web 3.0: An Empirical Study on the TRON-Steem Incident

Chao Li, Runhua Xu, Balaji Palanisamy +4

A fundamental goal of Web 3.0 is to establish a decentralized network and application ecosystem, thereby enabling users to retain control over their data while promoting value exch…

cs.CR2024

Tactics, Techniques, and Procedures (TTPs) in Interpreted Malware: A Zero-Shot Generation with Large Language Models

Ying Zhang, Xiaoyan Zhou, Hui Wen +4

Nowadays, the open-source software (OSS) ecosystem suffers from security threats of software supply chain (SSC) attacks. Interpreted OSS malware plays a vital role in SSC attacks,…

cs.LG2024

Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning

Xiaoting Lyu, Yufei Han, Wei Wang +5

Federated Learning (FL) is a collaborative machine learning technique where multiple clients work together with a central server to train a global model without sharing their priva…

cs.SE2024

A Large-scale Fine-grained Analysis of Packages in Open-Source Software Ecosystems

Xiaoyan Zhou, Feiran Liang, Zhaojie Xie +5

Package managers such as NPM, Maven, and PyPI play a pivotal role in open-source software (OSS) ecosystems, streamlining the distribution and management of various freely available…

cs.CR20241 cited

LTRDetector: Exploring Long-Term Relationship for Advanced Persistent Threats Detection

Xiaoxiao Liu, Fan Xu, Nan Wang +4

Advanced Persistent Threat (APT) is challenging to detect due to prolonged duration, infrequent occurrence, and adept concealment techniques. Existing approaches primarily concentr…

cs.CR20241 cited

Explainable and Transferable Adversarial Attack for ML-Based Network Intrusion Detectors

Hangsheng Zhang, Dongqi Han, Yinlong Liu +5

espite being widely used in network intrusion detection systems (NIDSs), machine learning (ML) has proven to be highly vulnerable to adversarial attacks. White-box and black-box ad…