Flocking is a fundamental self-organizing behavior observed in networked agent systems (NASs), wherein agents achieve coordinated group dynamics through mutual interactions. While in dynamic environmental contexts, the demand for flocking behavior to demonstrate rapid responsiveness and robust stability becomes more critical. With this in mind, this paper addresses the adaptive bipartite flocking control problem for NASs, particularly in the presence of compound uncertainties and convergence time constraints. A robust adaptive neural network-based prescribed-time bipartite flocking controller is developed to ensure that, despite uncertainties, all agents achieve flocking behavior within a predefined time. Notably, the settling time can be predefined and remains independent of system parameters such as controller gain , initial agent states, and the communication topology among agents. Additionally, by analyzing the stability conditions of the closed-loop error system, an adaptive weight update law for the neural network estimator is formulated. This updated law allows for effective uncertainty estimation through backpropagation of the flocking control error. Finally, the effectiveness and superiority of the proposed prescribed-time bipartite flocking control scheme are validated through numerical simulations.

Software-defined networking (SDN) has significantly enriched network functions by separating the control plane from the data plane. Meanwhile, the unique architecture of SDN brings new security challenges. Recent studies show that attackers can fabricate inter-switch links to hijack the traffic or interfere with network services. In this paper, we uncover two new vulnerabilities that can tamper with the topology view of the SDN controller. Then, we present two novel attacks named Cluster Splitting and Cluster Amnesia according to such flaws. We split or forget partial network topology by establishing a broadcast domain port or external link. As a result, it affects the module responsible for computing topology instances and disrupts the routing calculations. To defend against such attacks, we design a two-stage algorithm to verify the switch port and link in real-time. With the principle of saving the limited control channel resources and not extending the LLDP protocol, we propose LldpChecker. As a lightweight extension for SDN controllers, it can filter malicious broadcast domain ports and external links. We conduct a series of experiments to evaluate the effectiveness and efficiency of LldpChecker. The results show that LldpChecker can effectively mitigate these two novel attacks with negligible overhead.

Software-Defined Networking (SDN) enables the deployment of diversified networking applications by providing global visibility and open programmability on a centralized controller. As SDN enters its second decade, several well-developed open source controllers have been widely adopted in industry, and various commercial SDN applications are built to meet the surging demand of network innovation. This complex ecosystem inevitably introduces new security threats, as malicious applications can significantly disrupt network operations. In this paper, we introduce a new vulnerability in existing SDN controllers that enable adversaries to create a backdoor and further deploy malicious applications to disrupt network service via a series of topology poisoning attacks. The root cause of this vulnerability is that SDN systems simply process received Packet-In messages without checking the integrity, and thus can be misguided by manipulated messages. We discover that five popular SDN controllers (i.e., Floodlight, ONOS, OpenDaylight, POX and Ryu) are potentially vulnerable to the disclosed attack, and further propose six new attacks exploiting this vulnerability to disrupt SDN services from different layers. We evaluate the effectiveness of these attacks with experiments in real SDN testbeds, and discuss feasible countermeasures.