Lattice-based cryptography, as an active branch of post-quantum cryptography (PQC), has drawn great attention from side-channel analysis researchers in recent years. Despite the various side-channel targets examined in previous studies, detail on revealing the secret-dependent information efficiently is less studied. In this paper, we propose adaptive EM side-channel attacks with carefully constructed ciphertexts on Kyber, which is a finalist of NIST PQC standardization project. We demonstrate that specially chosen ciphertexts allow an adversary to modulate the leakage of a target device and enable full key extraction with a small number of traces through simple power analysis. Compared to prior research, our techniques require fewer traces and avoid building complex templates. We practically evaluate our methods using both a reference implementation and the ARM-specific implementation in pqm4 library. For the reference implementation, we target the leakage of the output of the inverse NTT computation and recover the full key with only four traces. For the pqm4 implementation, we develop a message-recovery attack that leads to extraction of the full secret key with between eight and 960 traces, depending on the compiler optimization level. We discuss the relevance of our findings to other lattice-based schemes and explore potential countermeasures.

In an environment with multiple static obstacles, UAVs usually communicate with each other to avoid collisions during trajectory planning. However, such communication may become infeasible or unreliable due to interference or jam in practice. This paper introduces a neighbors trajectory prediction algorithm based on model predictive control (MPC), which enables each UAV to predict the motion behavior of its neighbors without communication. By solving the MPC model of its neighbors, an UAV can predict their trajectories and then avoid collision with them in the future. To prove the practicability, we integrate the proposed algorithm into distributed model predictive control (DMPC) framework to realize multi-UAV trajectory planning without communication and with static obstacles. The performance of our method is verified by simulation experiments in two scenes.

Link flooding attacks (LFAs) have always been a security concern as the impact of volumetric attacks on transit links are increasingly severe. Capacity expansion, while being effective in combating LFAs, involves considerable deployment costs. Therefore, how to efficiently manage the link resource among spatio-temporal dynamic customers remains a challenge for Internet service providers (ISPs). In this paper, we study the differential pricing strategy for bandwidth allocation with LFA resilience by leveraging a multi-leader-multi-follower (MLMF) Stackelberg game approach. Based on network capabilities, we employ pricing approaches instead of empirical assignments to regulate the privileged channel allocation, economically facilitating the domain-level resource coordination. And we formulate the bandwidth pricing and allocation decision problem by using a Stackelberg game-theoretic approach to capture the interactions between providers and customers. Then, we analyze the Stackelberg game equilibrium of uniform pricing strategy and differential pricing strategy, where differential pricing is applied to adjust the prices that individual customers receive according to heterogeneous factors. Furthermore, we give the optimal solution derivation in the case of differential pricing. By comparing with other relevant pricing strategies, our numerical results show that the differential pricing strategy achieves congestion-free property and provides enough incentives for ISPs to deploy.

As an important part of cyberphysical systems (CPSs), multiple aerial drone systems are widely used in various scenarios, and research scenarios are becoming increasingly complex. However, planning strategies for the formation flying of aerial swarms in dense environments typically lack the capability of large-scale breakthrough because the amount of communication and computation required for swarm control grows exponentially with scale. To address this deficiency, we present a mean-field game (MFG) control-based method that ensures collision-free trajectory generation for the formation flight of a large-scale swarm. In this paper, two types of differentiable mean-field terms are proposed to quantify the overall similarity distance between large-scale 3-D formations and the potential energy value of dense 3-D obstacles, respectively. We then formulate these two terms into a mean-field game control framework, which minimizes energy cost, formation similarity error, and collision penalty under the dynamical constraints, so as to achieve spatiotemporal planning for the desired trajectory. The classical task of a distributed large-scale aerial swarm system is simulated by numerical examples, and the feasibility and effectiveness of our method are verified and analyzed. The comparison with baseline methods shows the advanced nature of our method.

In this paper, we study the enhancement of channel transmission information by expanding the transmission channel in the frequency-hopping rate dimension in a communication system. This is achieved using the frequency-hopping spread spectrum (FHSS) without increasing communication resources, such as power and bandwidth. The anti-jamming capability of the original information is maintained during this process. The spectral characteristics of the extended signal for frequency-hopping (FH) transmission are investigated, a demodulation method based on carrier reconstruction is proposed, the bit error rate performance is simulated, and the capacity enhancement and anti-jamming ability of the extended signal for FH transmission are analyzed.

As one of the important issues of multi-agent collaboration, the large-scale agents’ cooperative attack–defense evolution requires a large number of agents to make stress-effective strategies to achieve their goals in complex environments. Multi-agent attack and defense in high-dimensional environments (3D obstacle scenarios) present the challenge of being able to accurately control high-dimensional state quantities. Moreover, the large scale makes the dynamic interactions in the attack and defense problems increase dramatically, which, using traditional optimal control techniques, can cause a dimensional explosion. How to model and solve the cooperative attack–defense evolution problem of large-scale agents in high-dimensional environments have become a challenge. We jointly considered energy consumption, inter-group attack and defense, intra-group collision avoidance, and obstacle avoidance in their cost functions. Meanwhile, the high-dimensional state dynamics were used to describe the motion of agents under environmental interference. Then, we formulated the cooperative attack–defense evolution of large-scale agents in high-dimensional environments as a multi-population high-dimensional stochastic mean-field game (MPHD-MFG), which significantly reduced the communication frequency and computational complexity. We tractably solved the MPHD-MFG with a generative-adversarial-network (GAN)-based method using the MFGs’ underlying variational primal–dual structure. Based on our approach, we carried out an integrative experiment in which we analytically showed the fast convergence of our cooperative attack–defense evolution algorithm by the convergence of the Hamilton–Jacobi–Bellman equation’s residual errors. The experiment also showed that a large number of drones can avoid obstacles and smoothly evolve their attack and defense behaviors while minimizing their energy consumption. In addition, the comparison with the baseline methods showed that our approach is advanced.