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Fangzheng Zhang

dblp:179/6523 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2026
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Software-defined and programmable networks · 50% Network management and operations · 50%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Network management and operations › fault management › fault diagnosis
anomaly localization
0.512021
Network-Wide Forwarding Anomaly Detection and Localization in Software Defined Networks · IEEE/ACM Trans. Netw. 2021
Network management and operations › fault management
fault diagnosis
0.512021
Network-Wide Forwarding Anomaly Detection and Localization in Software Defined Networks · IEEE/ACM Trans. Netw. 2021
Software-defined and programmable networks › SDN threat detection
forwarding anomaly detection
0.512021
Network-Wide Forwarding Anomaly Detection and Localization in Software Defined Networks · IEEE/ACM Trans. Netw. 2021
Software-defined and programmable networks
SDN data plane
0.512021
Network-Wide Forwarding Anomaly Detection and Localization in Software Defined Networks · IEEE/ACM Trans. Netw. 2021

Methods — techniques the papers use, named apart from their topics

voting-based localization · 0.5flow conservation principle · 0.5
YearPublicationVenuePosition
2026 Frequency-Domain Adaptive Processing With Stepped-Frequency Radar for Deceptive Jamming Suppression
Jinhu Li, Fangzheng Zhang, Shilong Pan
IEEE Signal Process. Lett.2
2021 Network-Wide Forwarding Anomaly Detection and Localization in Software Defined Networks
abstract
A crucial requirement for Software Defined Network (SDN) is that data plane forwarding behaviors should always agree with control plane policies. Such requirement cannot be met when there areforwarding anomalies, where packets deviate from the paths specified by the controller. Most anomaly detection methods for SDN install dedicated rules to collect statistics of each flow, and check whether the statistics conform to the “flow conservation principle”. We find these methods have a limited detection scope: they look at one flow each time, thus can only check a small number of flows simultaneously. In addition, dedicated rules for statistics collection can impose a large overhead on flow tables of SDN switches. To this end, this paper presents FOCES, a network-wide forwarding anomaly detection and localization method in SDN. Different from previous methods, FOCES applies a new kind of flow conservation principle at network wide, and can check forwarding behaviors ofallflows in the network simultaneously, without installing any dedicated rules. Finally, FOCES applies a voting-based method to localize malicious switches when anomalies are detected. Experiments with four network topologies show that FOCES can achieve a detection precision higher than 90%, when the packet loss rate is no larger than 10%, and a localization accuracy of around 80% when the packet loss rate is no larger than 5%.
Peng Zhang 0011, Fangzheng Zhang, Shimin Xu, Zuoru Yang, Hao Li 0011, Qi Li 0002, Huanzhao Wang, Chao Shen 0001, Chengchen Hu
IEEE/ACM Trans. Netw.2