An Optimal Artificial Intelligence (AI) technique for cybersecurity threat detection in IoT Networks

An exponential growth rate has been seen in cyberattacks targeting fully integrated servers, apps, and communications networks. The Things Network (IoT). Inefficient operation of sensitive devices harms end users, increasing the risk of identity theft and cyberattacks, increasing costs, and decreasing revenue as problems with the Internet of Things network remain undetected for long periods. Robust cybersecurity solutions are necessary to safeguard digital infrastructures against the growing frequency of cyberattacks and the fast growth of the Internet of Things. This research looks at the function of Artificial Intelligence (AI) in improving cybersecurity measures, specifically emphasising the comparison of signature-based and anomaly-based IDS. ML and DL techniques, including DNN, SVM, and Random Forest classifiers, are used in this work to classify cybersecurity risks and detect potential threats using the dataset UNSW-NB15. According to our data, the Random Forest model outperforms the competition, with a 98.6% accuracy rate and 99% precision, F1 score and recall. The research emphasises the efficacy of AI-powered systems in real-time threat identification, emphasising its usefulness in advancing cybersecurity measures. By tackling the issues provided by conventional security measures and employing modern ML and DL approaches, this study gives significant insights for organisations trying to improve their cybersecurity policies in an increasingly complex threat scenario.