Papers for

industrial control system engineers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Preemption changes optimal routing to favor slower delayed paths

Worse Routes, Fresher Information: Preemption Redefines Which Route is Best

Abstract: This paper studies joint sampling, route activation, and preemption control over $N$ heterogeneous transmission routes under a long-run objective combining Age of Information (AoI) with sampling and transmission costs. We formulate the problem as an average-cost impulse-control model, derive a vector-form integral average-cost optimality equation over a hybrid state space, and establish structural reductions on the state. We consider a two-route case and compute optimized policies using discretized policy iteration. Surprisingly, the results show that preemption can reverse routing preferences: the optimized policy may exclusively use a route with both a larger mean delay and a larger delay variance while avoiding the seemingly superior route. Relative to baselines that do not allow preemption, the proposed policy reduces average AoI by up to $96\%$.

Sat 12 SeptInformation Theory

The gist

This paper looks at how to best send updates through different network paths when balancing freshness of information with the cost of sending. The authors found that allowing new transmissions to interrupt ongoing ones can make it better to choose routes that are actually slower or more variable in delay. Surprisingly, this strategy can improve how quickly fresh information is received by up to 96%. This shows that when interruptions are allowed, the route that looks worse on average can actually be best for keeping information up to date.

Open → 2609.13622v1

Hybrid neural model boosts power grid cyberattack detection accuracy

Robust Industrial Cyber Physical Classification Using Neuromorphic Temporal Embeddings and Hybrid SNN XGBoost Under Machine Unlearning Attacks

Abstract: The digitalisation of electrical distribution networks has increased the exposure of power-grid infrastructure to cyber attacks. Existing intrusion detection systems (IDSs), however, often rely on computationally expensive deep learning models that are difficult to deploy at the edge. Periodic retraining also exposes these systems to machine unlearning attacks, where selective data removal can degrade detection performance. We propose a hybrid Spiking Neural Network (SNN) and XGBoost architecture that combines efficient temporal encoding with a lightweight classifier and provides structural resilience to such attacks. The SNN is trained once on clean data and used as a fixed feature extractor, while only the XGBoost classifier is retrained during model updates. Evaluated on two real-world public power-system datasets, the proposed method achieves 99.9\% accuracy (F1-macro 0.999) on the Synchrophasor dataset and 95.0\% accuracy (F1-macro 0.943) on the MSU/ORNL dataset, outperforming standalone baselines. Under selective label-flipping attacks, the hybrid model loses only 0.9\% F1-macro at 10\% poisoning and delays target-class collapse from 60\% to 70\% poisoning compared with raw models. These results demonstrate that neuromorphic temporal encoding can provide both accurate cyber-attack detection and improved resilience to data poisoning in cyber-physical systems.

Wed 9 SeptMachine LearningCryptography and SecurityNeural and Evolutionary Computing

The gist

Power grids are more vulnerable to cyberattacks because of increased digital connections. The authors created a method combining two types of machine learning models—spiking neural networks and XGBoost—that is both fast and tough against data tampering attacks. Their system detects cyberattacks with very high accuracy on real datasets and keeps working well even when attackers try to confuse it by altering training data. This approach makes protecting critical power systems more reliable and efficient.

Open → 2609.09564v1