Due to the widespread development of microgrid (MG) systems, this paper mainly focuses on the edge-based prescribed-time adaptive secondary control problem for direct current (DC) MG systems. Firstly, a new voltage regulator is designed by the time-varying scaling function and the dynamic average consensus protocol of multi-agent systems. Meanwhile, in order to avoid the dependence of the algorithm on the global network information, an edge-based adaptive control update rate is proposed. Secondly, by applying the prescribed-time stability theory, it is rigorously proven that the bus voltage can be restored to its desired value under the presented voltage regulator within a prescribed time, and the current is allocated according to the droop coefficient ratio. Finally, the effectiveness and feasibility of the proposed algorithm are verified through Matlab/Simulink simulation under different working conditions, and it is compared with the existing asymptotic consensus algorithm to demonstrate the superiority of the algorithm presented in this paper.

In this article, the prescribed-time optimization problem is considered for multiagent systems (MASs) with inconsistent constraint sets. First, the constrained optimization problem is transformed into an unconstrained one using the sliding mode control approach, in which the sliding surface is constructed based on the projection errors. Subsequently, three distinct prescribed-time optimization control protocols are developed by utilizing the sliding mode variables and optimization algorithms. In particular, compared with the existing works in which the gain of each controller approaches infinity at the prescribed instant, this article effectively mitigates singularity by integrating fixed-time theory with the time-domain transformation method. Furthermore, it can be verified that all agents achieve optimization consensus under the three different algorithms by the Lyapunov stability theory and convex optimization analysis. Finally, we provide an application example to verify the validity of theoretical results.

This article studies the prescribed-time practical consensus problem for nonlinear multi-agent systems (MASs) subject to denial-of-service (DoS) attacks via an intermittent-based event-triggered mechanism. To this end, we first propose a time-varying scaling function based on the intermittent period, and then use this function to derive a new practical prescribed-time stability theory. Second, this paper further designs an intermittent-based event-triggered control protocol, along with a corresponding triggered function. The main innovation of this protocol is its ability to avoid singular phenomena as time approaches the prescribed instant and to reduce communication costs among all agents. Additionally, some sufficient conditions for achieving leader-following practical consensus are deduced by the proposed prescribed-time stability theory while also excluding Zeno behavior. Finally, two examples are given to verify the effectiveness and feasibility of the theoretical results.

Smart speakers bring convenience to people's daily lives. However, various attacks can be launched against smart speakers to execute malicious commands, which may cause serious safety or security issues. The existing solutions against sophisticated attacks such as voice replay attacks and voice synthesis attacks require intrusive modifications of the smart speaker hardware and/or software, which are impractical for general users. In this work, we present a novel security scheme- VoiceGuard that can effectively detect and block unauthorized voice commands to smart speakers. VoiceGuard does not require any modification to smart speakers' hardware or software. We implement a prototype of VoiceGuard on two popular smart speakers: Amazon Echo Dot and Google Home Mini, and evaluate the scheme in three real-world testbeds, which include both single-user and multi-user scenarios. The experimental results show that VoiceGuard achieves an accuracy of 97% in blocking malicious voice commands issued by illegitimate sources while having a negligible impact on the user experience.

In recent years, intelligent and automated device control features have led to a significant increase in the adoption of smart home IoT systems. Each IoT device sends its events to (and receives commands from) the corresponding IoT server/platform, which executes automation rules set by the user. Recent studies have shown that IoT messages, including events and commands, are subject to stealthy delays ranging from several seconds to minutes, or even hours, without raising any alerts. Exploiting this vulnerability, adversaries can intentionally delay crucial events (e.g., fire alarms) or commands (e.g., locking a door), as well as alter the order of IoT messages that dictate automation rule execution. This manipulation can deceive IoT servers, leading to incorrect command issuance and jeopardizing smart home safety. In this paper, we present MP-Mediator, which is the first defense system that can detect and handle the new, stealthy, and widely applicable delay attacks on IoT messages. For IoT devices lacking accessible APIs, we propose innovative methods leveraging virtual devices and virtual rules as a bridge for indirect integration with MP-Mediator. Furthermore, a VPN-based component is proposed to handle command delay attacks on critical links. We implement and evaluate MP-Mediator in a real-world smart home testbed with twenty-two popular IoT devices and two major IoT automation platforms (IFTTT and Samsung SmartThings). The experimental results show that MP-Mediator can quickly and accurately detect the delay attacks on both IoT events and commands with a precision of more than 96% and a recall of 100%, as well as effectively handle the delay attacks.

In recent years, with the rapid development of machine learning in various domains, more and more studies have shown that machine learning models are vulnerable to adversarial attacks. However, most existing researches on adversarial machine learning study non-graph data, such as images and text. Though some previous works on graph data have shown that adversaries can make graph-based classification methods unreliable by adding perturbations to features or adjacency matrices of existing nodes, these kinds of attacks sometimes have limitations for real-world applications. For example, to launch such attacks in real social networks, the attacker cannot force two good users to change (e.g., remove) the connection between them, which means that the attacker can not launch such attacks. In this paper, we propose a novel attack on collective classification methods by adding fake nodes into existing graphs. Our attack is more realistic and practical than the attack mentioned above. For instance, in a real social network, an attacker only needs to create some fake accounts and connect them to existing users without modifying the connections among existing users. We formulate the new attack as an optimization problem and utilize a gradient-based method to generate edges of newly added fake nodes. Our extensive experiments show that the attack can not only make new fake nodes evade detection, but also make the detector misclassify most of the target nodes. The proposed new attack is very effective and can achieve up to 100% False Negative Rates (FNRs) for both the new node set and the target node set.

Nowadays, The Internet of Things (IoT) has been widely used in various fields due to its smart sensing and communication capabilities. IoT devices serve as bridges for the cyber system to interact with the physical environment by providing various useful sensing capabilities such as battlefield surveillance, home monitoring, traffic control, etc. These capabilities also make IoT an important role in tactical missions in the military, including Reconnaissance, Intelligence, Surveillance, and Target Acquisition (RISTA). Nevertheless, IoT devices are known to have critical issues on security due to constraints on cost and resources. Most existing researches are based on smart sensors that have comparatively more computing and communication resources, while security solutions for dumb sensors are still lacking. Some IoT sensors that are deployed in a hostile environment are dumb due to limitations on cost and power supply, making them more vulnerable to attacks. In this work, we try to tackle this problem by proposing effective authentication solutions between a UAV and dumb IoT devices (also referred to as dumb sensors) within an example application of a UAV-sensor collaborative RISTA mission. We present two different schemes for two-way mutual authentication between the UAV and dumb sensors which utilize non-cryptographic physical layer cover channel and neighboring devices' signal sensing correlations respectively. We demonstrate the feasibility and effectiveness of our schemes with extensive real-world experiments on our prototype deployment.