Designing an Improved Cyberattack Prediction Model Using Context-Aware Behavioral Modeling Analysis

Received: 28 April 2025 | Revised: 14 June 2025 | Accepted: 21 June 2025 | Online: 6 October 2025

Corresponding author: Geeta Patil

Abstract

The increasing sophistication and coordination of cyberattacks require proactive defense mechanisms equipped to predict malicious activity before it happens. Conventional systems for intrusion detection and anomaly detection primarily rely on signature-based or shallow anomaly detection methods, which are poorly suited for detecting temporally evolving stealthy-threats and zero-day attacks. These limitations highlight the need for a holistic, context-aware framework that can not only catch threats much earlier but also attribute, with high-fidelity, the underlying causes of these threats. Integrated Neural Cyberattack Prediction and Threat Attribution using Contextual Deep Learning (INCEPT) is a proposal put forth by the authors to address the above challenges: a modular, multi-pronged deep-learning framework designed to predict cyberattacks in detail from raw network traffic data. INCEPT integrates five novel models. Context-Aware Spatio-Temporal Graph Neural Network (CA-STGNN) learns complex entity interactions across time and space, significantly improving the detection of coordinated attacks. Behavior-based Latent Intent Modeling (BLIM) using Contrastive Predictive Coding (CPC) primarily focuses on deviations of intent for early-stage detection, especially in scenarios involving stealthy malware. Hierarchical Attention Transformer guided by Threat Taxonomy Embeddings (HAT-TTE) enables interpretable, multi-stage classification aligned with the MITRE ATT&CK framework. Federated Ensemble framework for Zero-Day Attack Detection (FedEn-ZAD) enhances generalization and robustness across distributed domains with uncertainty quantification. Multi-Resolution Autoencoder with Causal Attribution (MRA-CAA) identifies root causes of detected anomalies across granular traffic layers. Together, these modules demonstrate up to 20% improvement in detection accuracy, 30-35% reduction in incident response time, and notable gains in analyst interpretability and trust. The result is an architecture that offers a scalable and interpretable anticipatory solution to modern cyber defense tasks.