In this paper, an intrusion detection system is introduced that uses data mining and machine learning concepts to detect network intrusion patterns. In the proposed method, an artificial neural network (ANN) is used as a learning technique in intrusion detection. The metaheuristic algorithm with the swarm-based approach is used to reduce intrusion detection errors. In the proposed method, the Grasshopper Optimization Algorithm (GOA) is used for better and more accurate learning of ANNs to reduce intrusion detection error rate. The role of the GOAMLP algorithm is to minimize the intrusion detection error in the neural network by selecting useful parameters such as weight and bias. Our implementation in MATLAB software and using the KDD and UNSW datasets show that the proposed method detects abnormal, malicious traffic and attacks with high accuracy. The GOAMLP method outperforms and is more accurate than the existing state-of-the-art techniques such as RF, XGBoost, and embedded learning of ANN with BOA, HHO, and BWO algorithms in network intrusion detection.
<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt; layout-grid-mode: char;" align="left"><span class="text"><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;">This paper presents a successful lab simulation experience to teach signal modulation and demodulation concepts in communication and computer networks to computer science and computer engineering students. Two sections of the same college course with a total of 80 subjects participated in this study. After receiving the same lecture at the same time, the subjects in each course were randomly split into two treatment groups. One group completed two laboratory experiments using the computerized simulation program, while the other completed the same two laboratory experiments using the traditional physical laboratory equipments. Upon the completion of the laboratory assignments, the performance instrument was individually administered to each student.</span></span></p><span class="text"><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;"><p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt; layout-grid-mode: char;" align="left"><span class="text"><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;">The groups were compared on understanding the concepts, remembering the concepts, and displaying a positive attitude toward the treatment tools. Scores on a validated Concepts Test were collected once after the treatment and another time after three weeks in order to gain some insight on students’ knowledge retention. The validated Attitude Survey and qualitative study was administered at the completion of the treatment. The findings of this research indicate that conceptual simulation programs could be feasible substitute for hands-on exercises.</span></span></p></span></span>
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