Authors - Chethana R.M. and Dr S.P. Manikandan Abstract - The rapid evolution of cyber threats has intensified risks to organisational security, necessitating intelligent, data-driven approaches to threat assessment and mitigation. This study presents a comprehensive analysis of the evolving cyber threat landscape and its impact on organizational security using a dataset of 1,200 cybersecurity incidents reported across major sectors in India from 2019 to 2024. The dataset includes diverse incident categories such as phishing, ransomware, data breaches, online fraud, identity theft, and hacking, along with associated financial losses, geographic distribution, and affected organizational domains. To investigate threat patterns and predict incident behavior, three machine learning models, Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR) were employed for classification and regression tasks. Experimental results reveal significant challenges posed by class imbalance and feature complexity, leading to relatively low classification accuracies, with Random Forest marginally outperforming other models. Regression analysis for predicting financial losses also demonstrated limited explanatory power, indicating the influence of latent factors beyond the available attributes. Despite these constraints, the study identifies important sector-specific vulnerability patterns, highlighting significant financial impacts across healthcare, financial services, and government. The findings emphasize that conventional machine learning models alone may be insufficient for capturing the highly dynamic and nonlinear nature of cyber threats, underscoring the need for advanced threat intelligence frameworks, richer datasets, and adaptive security analytics. This research contributes empirical insights into cyber risk modeling and offers practical implications for policymakers and organizations seeking evidence-based cybersecurity strategies.
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