Browsing by Author "SAMBA, Steve"

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    Combining Meta-Heuristic Technique and Neural Networks to Detect Internet of Things Network Attacks
    (Adventist University of Africa, 2023-03) SAMBA, Steve
    The Internet of Things (IoT) refers to ordinary objects equipped with wearable sensors and batteries that can communicate over the internet and perform predefined actions. These devices are evading our everyday lives in many ways. It is now possible to sense temperature, and heart rate with a smartphone, while cloud applications can monitor security systems or smart home equipment. Consequently, IoT networks have simplified life. However, the growing popularity of Internet of Things devices poses security concerns that need attention. For instance, attackers may target IoT networks for several reasons, including a quest for personal, medical, or financial information and espionage. In certain circumstances, these attacks can have severe repercussions for people's lives. Others may face extortion, damaged reputation, impersonation, fraud, or financial damages. Detection is critical for defending IoT networks and avoiding the negative repercussions of cyberattacks. Detection consists of identifying assaults before they occur. Numerous writers have examined the security of IoT networks and proposed promising solutions based on machine learning. This research investigated how efficient could combining Neural Networks and Metaheuristic technique be in detecting IoT network attacks. To address that concern this study proposed a novel method that integrates neural networks for attack classification and Particle Swarm Optimization, a metaheuristic strategy for feature selection and hyperparameter tuning. The outcomes of the suggested strategy using two different IoT data sets, namely the BaIoT and the CICIDS 2017 datasets yielded accuracy scores of 98% and 99.95% for multiclass classification. The binary categorization was nearly flawless. Furthermore, this study revealed the potential of CNN, MLP and FFNN when dealing with classification problems for IoT environments. The study also highlighted interesting future venues for improving IoT network security, such as deployment, training models with higher quality datasets, or even tweaking more parameters.