As DRAM scales to smaller nodes, commercial off-the-shelf (COTS) memory achieves higher density and lower cost but faces increased inherent single-event upset (SEU) susceptibility, complicating the adoption of energyefficient Low-Power Double Data Rate (LPDDR) SDRAM, ideal for power-constrained satellites like CubeSats. Radiationhardened memory, while more SEU-resistant, is increasingly becoming impractical for modern space systems due to lower bit density, higher cost, and infeasibility with computeintensive tasks like AI/ML applications. Additionally, limited public SEU radiation test data on LPDDR devices, particularly for emerging failure modes, constrains reliability assessment of COTS ECC adequacy in masking radiation-induced memory faults. This also hinders the development of fault-tolerance measures that could enable wider LPDDR adoption in space systems through improved reliability. In this paper, we evaluate the SEU susceptibility of COTS LPDDR memory through proton irradiation tests using 20-58 MeV proton beams on two architecturally distinct SoC platforms: the Raspberry Pi Zero 2 W (512 MB LPDDR2 SDRAM) and the NXP i.MX 8M Plus (4096 MB LPDDR4 SDRAM). Constant memory stress testing through Linux during irradiation exposed several previously unreported failure modes, including SBUs, MBUs, row-hammer faults, and stress-ng-detected modulo-x, gray code, rand-set, and modulo X faults. The resulting dataset provides key insights for COTS ECC optimization and informs the suitability of LPDDR for modern space systems.

The use of traditional defense mechanisms or intrusion detection systems presents a disadvantage for defenders against attackers since these mechanisms are essentially reactive. Moving target defense (MTD) has emerged as a proactive defense mechanism to reduce this disadvantage by randomly and continuously changing the attack surface of a system to confuse attackers. Although significant progress has been made recently in analyzing the security effectiveness of MTD mechanisms, critical gaps still exist, especially in maximizing security levels and estimating network reconfiguration speed for given attack power. In this paper, we propose a set of Petri Net models and use them to perform a comprehensive evaluation regarding key security metrics of Software-Defined Network (SDNs) based systems adopting a time-based MTD mechanism. We evaluate two use-case scenarios considering two different types of attacks to demonstrate the feasibility and applicability of our models. Our analyses showed that a time-based MTD mechanism could reduce the attackers' speed by at least 78% compared to a system without MTD. Also, in the best-case scenario, it can reduce the attack success probability by about ten times.

Critical infrastructures have to withstand advanced and persistent threats, which can be addressed using Byzantine fault tolerant state-machine replication (BFT-SMR). In practice, unattended cyberdefense systems rely on threat level detectors that synchronously inform them of changing threat levels. However, to have a BFT-SMR protocol operate unattended, the state-of-the-art is still to configure them to withstand the highest possible number of faulty replicas$f$they might encounter, which limits their performance, or to make the strong assumption that a trusted external reconfiguration service is available, which introduces a single point of failure. In this work, we present ThreatAdaptive the first BFT-SMR protocol that is automatically strengthened or optimized by its replicas in reaction to threat level changes. We first determine under which conditions replicas can safely reconfigure a BFT-SMR system, i.e., adapt the number of replicas$n$and the fault threshold$f$so as to outpace an adversary. Since replicas typically communicate with each other using an asynchronous network they cannot rely on consensus to decide how the system should be reconfigured. ThreatAdaptive avoids this pitfall by proactively preparing the reconfiguration that may be triggered by an increasing threat when it optimizes its performance. Our evaluation shows that ThreatAdaptive can meet the latency and throughput of BFT baselines configured statically for a particular level of threat, and adapt 30% faster than previous methods, which make stronger assumptions to provide safety.