Applying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection

13Artificial Intelligence for Threat Anomaly Detection Using Graph Databases – A Semantic Outlook

Edwiges G.H. Grata¹, Anand Deshpande², Ricardo T. Lopes³, Asif A. Laghari⁴, Abdullah A. Khan⁵, R. Jenice Aroma⁶, Kumudha Raimond⁷, Shoulin Yin⁸, and Awais Khan Jumani⁹

¹Department of Telecommunications, Federal Fluminense University (UFF), Niterói, RJ, Brazil

²Electronics and Communication Engineering, Angadi Institute of Technology and Management, Belagavi, India

³Federal University of Rio de Janeiro (COPPE/UFRJ), Nuclear Engineering Laboratory (LIN), Rio de Janeiro, RJ, Brazil

⁴Sindh Madresstul Islam University, Karachi, Sindh, Pakistan

⁵Research Lab of Artificial Intelligence and Information Security, Faculty of Computing, Science and Information Technology, Benazir Bhutto Shaheed University, Karachi, Sindh, Pakistan

⁶Department of CSE, Karunya Institute of Technology and Sciences, Karunya University, Coimbatore, India

⁷Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India

⁸Shenyang Normal University, Shenyang, Liaoning Province, China

⁹Department of Computer Science, Sindh Madressa‐tul‐Islam University, Karachi, Sindh, Pakistan

13.1 Introduction

A comprehensive cybersecurity (CS) framework must help implement “cyber‐physical systems” (CPSs) effectively while managing potential security risks to accommodate the Internet of Things (IoT) hardware (HW) diversity [1–3]. Both factors are becoming increasingly crucial in infrastructure, ...