Intrusion detection systems (IDS) conventionally operate as binary classifiers, a design that creates two well-known failure modes: abrupt termination of benign processes through false positives and failure to detect genuinely novel attacks under zero-day conditions. Both failures arise from treating security primarily as pattern recognition rather than as a stability-control problem. This paper introduces the Kerimov-Alekberli constant gammaKA, a thermodynamically grounded damping threshold that reframes intrusion detection as Lyapunov stability monitoring. We define gammaKA as the maximum entropy-production rate that a system can absorb while remaining asymptotically stable, and we show that crossing this threshold can be interpreted as a first-passage-time event on a statistical manifold, corresponding to a thermodynamic phase transition from ordered to chaotic operation. From this threshold, we derive Entropic Filtering, a graduated response mechanism that throttles suspicious or malicious processes in proportion to their entropy excess, thereby reducing the collateral disruption associated with binary kill-switch responses. Validation on the NSL-KDD benchmark yields 71. 4 percent zero-day detection accuracy, more than double the best classical machine-learning baseline considered in this study, with a mean Lyapunov convergence time of 8. 3 ms and a false-positive rate of 2. 9 percent. A proof-of-concept deployment on ARM Cortex-A72 hardware produces a response latency of 18-25 ms, consistent with simulation predictions. The central contribution is a formally verifiable, data-independent stability criterion: dS/dt < gammaKA characterizes secure operation independently of specific attack signatures.
Karimov et al. (Mon,) studied this question.