Network port scanning represents a critical reconnaissance phase preceding advanced cyber attacks and poses a significant threat to modern network infrastructures. Conventional signature-based detection systems and static threshold mechanisms are often ineffective in detecting stealthy and low-rate scanning activities in dynamic network environments. This paper proposes a hybrid intrusion detection approach that combines adaptive entropy-based filtering with a Random Forest classifier. The proposed method employs a sliding window mechanism to dynamically adjust detection thresholds based on statistical properties of network traffic, thereby improving adaptability under varying load conditions. Experimental evaluation conducted on the CIC-IDS2017 dataset demonstrates that the proposed model achieves a classification accuracy of 98.7% with a False Positive Rate (FPR) of 1.8%, outperforming both standalone entropy-based and machine learning-based approaches. In addition, the model maintains an average processing latency of 2.3 ms per packet, confirming its suitability for real-time deployment in high-speed network environments. The results indicate that the proposed hybrid approach effectively improves detection performance while maintaining low computational overhead, making it a practical solution for modern intrusion detection systems. However, the proposed approach has certain limitations, including reliance on traffic metadata and reduced effectiveness in encrypted environments.

Accurate and objective assessment of students’ knowledge remains a challenging problem due to the inherent uncertainty and subjectivity of traditional evaluation systems. Conventional grading approaches often fail to account for task complexity, discrimination power, and variability in student responses, which leads to inconsistent and biased results. This study proposes a multi-stage fuzzy logic–based decision-making model for knowledge assessment. The model integrates several key evaluation indicators, including task difficulty, discrimination index, response value, and response weight, within a unified fuzzy inference framework. A structured multi-factor evaluation mechanism is developed, where fuzzy membership functions and rule-based inference are used to transform qualitative judgments into quantitative assessment measures. Furthermore, a defuzzification process based on the Center of Gravity (COG) method is applied to obtain final scores, and a correction mechanism is introduced to refine evaluation outcomes. A comparative analysis was conducted using assessment data from 100 students across 5 tasks evaluated on a [0–10] scale. The results suggest that the proposed approach provides a more differentiated and consistent interpretation of student performance than the traditional assessment method. The proposed model provides a reliable and interpretable framework for evaluating students’ knowledge and supports the development of adaptive and intelligent educational assessment systems.