الفهرس | Only 14 pages are availabe for public view |
Abstract The internet of things (IoT) is a collection of common physical things which can communicate and synthesize data utilizing network infrastructure by connecting to the Internet. The IoT has the potential to capture sensitive data and should be able to withstand attacks and privacy concerns. In the IoT, these digital sensors or devices are known as "things". IoT networks are growing increasingly vulnerable to security breaches as their popularity grows. One among the most popular severe dangers to IoT security is cyber security attacks such as distributed denial of service (DDoS) and denial of service (DoS). These attacks take several forms and target various resources on a wide range of IoT devices. These cyberattacks frequently target a large number of devices in an IoT network. Many academics are increasingly interested in enhancing the security of IoT systems. Based on malicious detection systems, machine learning (ML) approaches were employed to provide a high level of security capabilities. This work proposed a novel malicious detection system based on machine learning (ML) methods to detect attacks in IoT and mitigate malicious occurrences. Furthermore, NSL-KDD or KDD-CUP99 datasets are used in the great majority of current studies, these datasets are not updated with new attacks. As a consequence, this study used the ToN_IoT dataset, which was created from a large-scale, heterogeneous IoT network. The ToN_IoT dataset reflects data from each layer of the IoT system such as (cloud, fog, and edge layers). The proposed model is a distributed malicious model which based on a multi-layer of the IoT system. Various ML methods were assessed in each specific sector of the ToN_IoT dataset. The proposed model is the first suggested model that is based on the collected data from the same IoT system from all layers and devices/sensors. The Chi 2 technique was used to pick features in a network dataset. It reduced the number of features to 20, which resulted in a faster training time, lower model complexity, and the best overall performance throughout the dataset. Another feature selection tool employed in the windows dataset was the correlation matrix, which was used to extract the most relevant features from the whole dataset. To balance the classes, the SMOTE method was used. It is enhanced overall performance by lowering the dominant class bias, reducing overfitting, and reducing dominant class bias. Using Chi 2 , SMOTE, and the correlation matrix as preprocessing tools, a satisfactory assessment measure will be produced. In this work, numerous machine learning methods were put to the test in both binary and multi-class classification problems. According to the findings, the XGBoost approach beats other ML methods in the whole dataset for all malicious detection nodes. Keywords: Malicious detection, Intrusion detection system (IDS), Internet of Things (IoT), ToN_IoT dataset, machine learning (ML), XGBoost classifier. |