Cognitive Cyber Crimes in the Era of Artificial Intelligence

24Forensic Insights into Cognitive Cyberattacks: Integrating Artificial Intelligence and Big Data Analytics

Romil Rawat

LabGeoInf – Research LABoratory in GEOmatics and INFormation Systems, Rome, Italy

Abstract

This study presents a novel forensic framework for detecting and analyzing cognitive cyberattacks by leveraging the synergy of artificial intelligence (AI) and Big Data analytics. Utilizing the publicly available CERT Insider Threat Dataset and CICIDS2017, the proposed approach uses a hybrid deep learning technique combining graph neural networks with attention-based long short-term memory models to capture both behavioral patterns and temporal dependencies in large-scale cybersecurity data. Advanced feature engineering using entropy-based feature selection enhances detection sensitivity to subtle cognitive manipulation tactics. Experimental results demonstrate a detection accuracy of 98.7%, with a false-positive rate reduced to 1.3%, outperforming state-of-the-art models by at least 4%. Additionally, the model introduces a novel Cognitive Attack Severity Index (CASI), quantifying attack impact based on psychological and behavioral anomaly scores. This integration of AI-driven forensics with Big Data analytics paves the way for proactive and explainable cybersecurity defenses against evolving cognitive threats.

Keywords: Cognitive cyberattacks, forensic analysis, artificial intelligence, big data analytics, graph neural networks

24.1 Introduction

The rapid evolution ...