Publication

Abstract : Cyberthreats including hacking, data breaches, and malware assaults have significantly increased as a result of digitalization and the Internet of Things’ (IoT) extensive use. As a consequence, the significance of cybersecurity measures has increased in order to shield crucial information technology assets and shield people and organisations from monetary losses. In order to improve cybersecurity’s capacity to recognise and react to cyberattacks, this research makes a contribution by investigating intelligent computing methodologies that make use of technologies like data analytics, machine learning (ML) and artificial intelligence (AI). The integration of intelligent computing techniques with current security architecture, proactive defence strategies, and ethical cybersecurity practises are highlighted. The study uses the UNSW-NB15 dataset and a mix of feature selection methods based on correlation and k-means clustering, followed by support vector machine (SVM) classification, to show the efficacy of the suggested strategy. According to the findings, the recommended technique has good accuracy, sensitivity, specificity, precision, and F1-score, making it a reliable option for successfully addressing dynamic cyberthreats. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.