A Review on Microstrip Patch Antenna Performance Improvement Techniques on Various Applications
Volume-3 | Issue-3
A Review on Finding Efficient Approach to Detect Customer Emotion Analysis using Deep Learning Analysis
Volume-3 | Issue-2
A Comparative Analysis of Prediction of Student Results Using Decision Trees and Random Forest
Volume-4 | Issue-3
Study of Security Mechanisms to Create a Secure Cloud in a Virtual Environment with the Support of Cloud Service Providers
Volume-2 | Issue-3
Construction of Black Box to Detect the Location of Road Mishap in Remote Area in the IoT Domain
Volume-3 | Issue-2
Fault Diagnosis in Hybrid Renewable Energy Sources with Machine Learning Approach
Volume-3 | Issue-3
Secure and Optimized Cloud-Based Cyber-Physical Systems with Memory-Aware Scheduling Scheme
Volume-2 | Issue-3
Stochastic Geometry and Performance Analysis of Large Scale Wireless Networks
Volume-3 | Issue-3
Computer Vision on IOT Based Patient Preference Management System
Volume-2 | Issue-2
Fake News Detection using Data Mining Techniques
Volume-3 | Issue-4
A Review on Microstrip Patch Antenna Performance Improvement Techniques on Various Applications
Volume-3 | Issue-3
Fake News Detection using Data Mining Techniques
Volume-3 | Issue-4
A Comparative Analysis of Prediction of Student Results Using Decision Trees and Random Forest
Volume-4 | Issue-3
Speedy Detection Module for Abandoned Belongings in Airport Using Improved Image Processing Technique
Volume-3 | Issue-4
Deployment of Artificial Intelligence with Bootstrapped Meta-Learning in Cyber Security
Volume-4 | Issue-3
Design an Early Detection and Classification for Diabetic Retinopathy by Deep Feature Extraction based Convolution Neural Network
Volume-3 | Issue-2
Design of an Intelligent Approach on Capsule Networks to Detect Forged Images
Volume-3 | Issue-3
Future Challenges of the Internet of Things in the Health Care Domain - An Overview
Volume-3 | Issue-4
Construction of Black Box to Detect the Location of Road Mishap in Remote Area in the IoT Domain
Volume-3 | Issue-2
A Review on Finding Efficient Approach to Detect Customer Emotion Analysis using Deep Learning Analysis
Volume-3 | Issue-2
Volume - 6 | Issue - 1 | march 2024
Published
17 April, 2024
Cyber incident classification and prioritization are crucial tasks in cybersecurity, enabling rapid response and resource allocation to mitigate potential threats effectively. This study presents a robust machine learning model designed for accurate classification and prioritization of cyber incidents, aiming to enhance cyber defense mechanisms. The proposed model integrates diverse machine learning algorithms, including Random Forest, Support Vector Machines, and Gradient Boosting, leveraging their complementary strengths to improve predictive performance and robustness. Extensive experimentation on real-world cyber threat datasets demonstrates the efficacy of the model, achieving high accuracy and reliability in identifying and prioritizing diverse types of cyber incidents. The model's performance is assessed using standard evaluation metrics such as accuracy, precision, recall, and F1-score, highlighting its ability to effectively distinguish between different classes of cyber threats and prioritize incidents based on their severity and potential impact on organizational assets. It was found that the model's interpretability is enhanced through feature importance analysis, providing insights into the key factors influencing cyber incident classification and prioritization decisions. The proposed machine learning model offers a promising approach to bolstering cyber defense capabilities, enabling organizations to proactively respond to cyber threats and safeguard their digital assets.
KeywordsCyber Threat Security Classification Attack Machine Learning Ensemble
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