An Optimized Feature Selection and Classification Framework for Detecting DDoS Attacks in IoT Networks - AL-Husseini , Haider; Hosseini, Mohammad Mehdi; Alazzawi , Murtadha A; Yousofi , Ahmad (Management Strategies and Engineering Sciences, 2025)
This paper presents the solution of an intrusion detection system to bolster the security of Network infrastructure IoT systems. The proposed method starts with a preprocessing stage of data cleaning, Min-Max normalization, label splitting, conversion of text into numbers, and data partitioning. Important to note, the Artificial Bee Colony Algorithm (ABC) and Neighborhood Component Analysis (NCA) work together in this approach. The initial NCA parameter will be optimized using BCO, whereas the feature selection's effectiveness is evaluated with the Cross Entropy Loss cost function. The final steps include designing and training an ensemble AdaBoost model to the targeted features to maximize accuracy of intrusion detection. Our method has been tested on the NSL-KDD dataset and reports 120 percent training accuracy and 99.74 percent accuracy for test data. With attention to detail, this proposal improves the process of IoT threat detection, unmanned network defense security systems, and poses an efficient method for the advanced dynamic environments, optimizing threading detection, and maximizing military grade precision in modern network security.