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A Novel CNN and GA-Based Algorithm for Intrusion Detection in IoT Devices


Ibrahim Darwish, Samih Montser, Mohamed R,Saadi


Vol. 23  No. 9  pp. 55-64


The Internet of Things (IoT) is the combination of the internet and various sensing devices. IoT security has increasingly attracted extensive attention. However, significant losses appears due to malicious attacks. Therefore, intrusion detection, which detects malicious attacks and their behaviors in IoT devices plays a crucial role in IoT security. The intrusion detection system, namely IDS should be executed efficiently by conducting classification and efficient feature extraction techniques. To effectively perform Intrusion detection in IoT applications, a novel method based on a Conventional Neural Network (CNN) for classification and an improved Genetic Algorithm (GA) for extraction is proposed and implemented. Existing issues like failing to detect the few attacks from smaller samples are focused, and hence the proposed novel CNN is applied to detect almost all attacks from small to large samples. For that purpose, the feature selection is essential. Thus, the genetic algorithm is improved to identify the best fitness values to perform accurate feature selection. To evaluate the performance, the NSL-KDDCUP dataset is used, and two datasets such as KDDTEST21 and KDDTEST+ are chosen. The performance and results are compared and analyzed with other existing models. The experimental results show that the proposed algorithm has superior intrusion detection rates to existing models, where the accuracy and true positive rate improve and the false positive rate decrease. In addition, the proposed algorithm indicates better performance on KDDTEST+ than KDDTEST21 because there are few attacks from minor samples in KDDTEST+. Therefore, the results demonstrate that the novel proposed CNN with the improved GA can identify almost every intrusion.


IoT, Intrusion Detection, CNN, Genetic Algorithm, NSL-KDD CUP dataset.