Cyber Attack Prediction using Traditional Methods, Machine Learning and Gen AI: A Conceptual Framework

Komal P. Kanojia; Urmila U. Ghare; Sakshi G. Pawar; Sharayu S. Shewale; Pooja P. Satpute

Research Article

Cyber Attack Prediction using Traditional Methods, Machine Learning and Gen AI: A Conceptual Framework

by Komal P. Kanojia, Urmila U. Ghare, Sakshi G. Pawar, Sharayu S. Shewale, Pooja P. Satpute

International Journal of Computer Applications

Foundation of Computer Science (FCS), NY, USA

Volume 187 - Issue 148

Published: October 2026

Authors: Komal P. Kanojia, Urmila U. Ghare, Sakshi G. Pawar, Sharayu S. Shewale, Pooja P. Satpute

10.5120/ijca1df7519bc86c

PDF

Komal P. Kanojia, Urmila U. Ghare, Sakshi G. Pawar, Sharayu S. Shewale, Pooja P. Satpute . Cyber Attack Prediction using Traditional Methods, Machine Learning and Gen AI: A Conceptual Framework. International Journal of Computer Applications. 187, 148 (October 2026), 22-28. DOI=10.5120/ijca1df7519bc86c

                        @article{ 10.5120/ijca1df7519bc86c,
                        author  = { Komal P. Kanojia,Urmila U. Ghare,Sakshi G. Pawar,Sharayu S. Shewale,Pooja P. Satpute },
                        title   = { Cyber Attack Prediction using Traditional Methods, Machine Learning and Gen AI: A Conceptual Framework },
                        journal = { International Journal of Computer Applications },
                        year    = { 2026 },
                        volume  = { 187 },
                        number  = { 148 },
                        pages   = { 22-28 },
                        doi     = { 10.5120/ijca1df7519bc86c },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }

                        %0 Journal Article
                        %D 2026
                        %A Komal P. Kanojia
                        %A Urmila U. Ghare
                        %A Sakshi G. Pawar
                        %A Sharayu S. Shewale
                        %A Pooja P. Satpute
                        %T Cyber Attack Prediction using Traditional Methods, Machine Learning and Gen AI: A Conceptual Framework%T 
                        %J International Journal of Computer Applications
                        %V 187
                        %N 148
                        %P 22-28
                        %R 10.5120/ijca1df7519bc86c
                        %I Foundation of Computer Science (FCS), NY, USA

Abstract

The advancements in cyberattacks pose threats and challenges for individuals and organizations in protecting sensitive information and maintaining secure network infrastructures. Traditional security methods like firewalls, signature-based intrusion detection systems (IDSs) are effective against known threats but struggle to detect sophisticated and unseen attacks. Machine learning techniques can improve cyberattack prediction by detecting patterns in network traffic. However, many ML models provide limited explanations, making it difficult for security analysts to understand and respond to predictions effectively. Generative AI offers effective approaches to improve cybersecurity by generating human-readable explanations, summarizing threats, and recommending mitigation strategies. This paper proposes a framework that integrates traditional cybersecurity methods, machine learning, and Generative AI to support intelligent cyberattack prediction. The prediction component of the proposed framework is evaluated on the NSL-KDD benchmark using five machine learning models, a rule-based baseline, and a hybrid detector. The results show a substantial gap between cross-validation and official test performance, while the hybrid detector improves the F1-score compared with Random Forest alone. The explainability, risk interpretation, and Generative AI components are proposed as parts of the conceptual framework and remain to be experimentally evaluated.

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Index Terms

Computer Science

Information Sciences

No index terms available.

Keywords

Cybersecurity cyberattack prediction machine learning generative artificial intelligence Threat intelligence risk assessment decision support system