Representative orientations of water and ACN at different surface polarities.

PMFs of 1,3-propanediol and n-pentane at the methanol–silica interface.

Network assaults pose significant security concerns to network services; hence, new technical solutions must be used to enhance the efficacy of intrusion detection systems. Existing approaches pay insufficient attention to data preparation and inadequately identify unknown network threats. This paper presents a network intrusion detection model (ID-RDRL) based on RFE feature extraction and deep reinforcement learning. ID-RDRL filters the optimum subset of features using the RFE feature selection technique, feeds them into a neural network to extract feature information and then trains a classifier using DRL to recognize network intrusions. We utilized CSE-CIC-IDS2018 as a dataset and conducted tests to evaluate the model’s performance, which is comprised of a comprehensive collection of actual network traffic. The experimental results demonstrate that the proposed ID-RDRL model can select the optimal subset of features, remove approximately 80% of redundant features, and learn the selected features through DRL to enhance the IDS performance for network attack identification. In a complicated network environment, it has promising application potential in IDS.

Typical configurations of ethanol during polarity modulation.

Traditional task allocation methods are threatened by the complexity and adversarial nature of modern battlefields. This work focuses on the modeling, optimization, and simulation verification of UAV swarm multi-domain fighting under the constraint of task resilience in order to address the issues created by various ways of deliberate enemy attack. Initially, a novel idea of equivalent load is proposed, considering it as the fundamental unit of reconnaissance, assault, communication, and other activities, in order to construct the capability load matrix of our single UAV and the needed load matrix of attacking each fighting unit in each battle region. Then, by integrating the strike probability and task completion degree, the task resilience capability index was developed, which improved the current UAV swarm task resilience measurement process. Due to the difficulty of traditional task allocation optimization methods in dealing with dynamic changes of optimization indexes before and after attacks, a resilience compensation load relaxation variable was added to the traditional Integral Linear Programming (ILP) problem description model of a UAV swarm. On the basis of a bilevel nested structure, a task allocation optimization method is created. Before an assault, the lower layer's ILP optimizer uses the swarm load cost as the target. The uppermost layer is comprised of Particle Swarm Optimization (PSO), which targets the comprehensive indices of UAV swarm load cost and task resilience after attack. It effectively resolves the multi-objective optimization problem of UAV swarms taking task difficulty into account. Ultimately, the test scenarios of three conflict domains, five basic battle units, and five load kinds were constructed, and the Ranchester battle model was used to simulate and validate the rationale and efficacy of the bilevel nested optimization method based on PSO-ILP.