Enhancing cyber security: a comprehensive approach to the classification and prediction of significant cyber incidents (SCI) through data mining and variational neural network with fox optimization algorithm

Abstract

The rapid advancement of technology and the proliferation of IoT devices have rendered cyberspace vulnerable, resulting in Significant Cyber Incidents (SCIs). This paper proposes an Enhancing Cyber security: a Comprehensive Approach to the Classification with Prediction of Significant Cyber Incidents through Data Mining with Variation Neural Network with Fox Optimization Algorithm (ECS-SCI-DM-VNN-FOA). The dataset is split into pre-pandemic and post-pandemic SCI subsets, according to the report from the Center for Strategic and International Studies (CSIS). Adaptive Variation Bayesian Filter (AVBF) is utilized to remove the noise from the input data. Then the preprocessed input data is supplied to the Improved Window Adaptive Gray Level Co-Occurrence Matrix (IWAGM) for feature extraction. The proposed approach is implemented in Python and its efficacy is evaluated under some metrics, like accuracy, precision, recall, FI-score, sensitivity, computational time, recall, and RoC. The proposed ECS-SCI-DM-VNN-FOA gives 24.91%, 23.76% and 25.98% high accuracy and 30.45%, 23.67% and 29.32% high precision compared with the existing methods, like classification with prediction of cyber events utilizing data mining with machine learning (CRP-SML), Cyber risk prediction via social media big data analytics along statistical machine learning (PECI-MUIP), Mining user interaction patterns in the dark web to forecast enterprise cyber occurrences (CTPA-CSCS) respectively.

KEYWORDS:

• Adaptive variation bayesian filter
• cyber security
• IWAGM
• significant cyber incidents
• variation neural network

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Bhawna Mallick

Dr. Bhawna Mallick, she is currently working Department of Computer Science & Engineering, SSET, Sharda University, Greater Noida, India. She is with more than 25 years of experience in academics, software development, project management, research, training, technical evangelizing, consultation and administration. Have are pupation as an innovative, dedicated and result- driven professional. She has under taken various projects for consultancy services related with project management practices, ISO practices, CMMIst and ards and practices. She has taught various courses at undergraduate and postgraduate level to engineering students. She has been supervisor for various project son different domains and technologies. Her research work includes both in the area of Data mining and Fuzzy logics where new algorithms generate useful patterns that are of interest for practical applications. Her research interests span most aspects of data mining, soft ware engineering, networking with emphasis on sequential pattern mining, increment AL mining and distributed data mining. She has good understanding of emerging technologies, experience in designing multitier enterprise solutions using J2EE technologies and standards, developing business solutions using Oracle/Developer 2000. She has worked with CMMI companies like Infosys Technologies Ltd and NIIT Technologies Ltd. Worked with clients on projects based on Business Intelligence, Design Thinking, Cyber Security ,Cloud Services, and Data Analytics.