Cyberattacks Prediction with Machine Learning

Abstract

A cyber-attack occurs when an organization uses cyberspace to disrupt, disrupted, disable, damage, or manipulate the infrastructure; This also occurs when the integrity of the data is destroyed or controlled information is stolen. The current state of cyberspace reflects uncertainty about the future of the Internet and its rapidly growing user base. New paradigms cause more concerns because the large data collected by the gadget sensors reveals a lot of information that can be used for concentrated attacks.Although the mounds of the extinct approach, models and algorithms have provided the basis for predictions for cyber-bulder, must consider new models and algorithms, which are based on data deficiencies other than work techniques. However, the non-regional information processing architecture can be adapted to learn different data representations of network traffic to classify the type of network attacks. In this, we model cyber horses as a classification problem, network sectors must predict the type of network attacks from datasets given using machine learning techniques. Analysis of dataset of monitored machine learning technology (SMLT) to capture many information, variable identity, UNI-view analysis, BI-Architers and Multi-Spiting Analysis, lack of pricing, etc.

Introduction

Machine learning (ML) is a branch of artificial intelligence that enables computers to learn from data without explicit programming. It includes three main types: supervised learning (with labeled data), unsupervised learning (without labels, focusing on data clustering), and reinforcement learning (learning through feedback from interaction with the environment). ML algorithms use training data to create models that can predict outcomes on new, unseen data.

In cybersecurity, ML is applied to predict and detect cyberattacks, especially Denial of Service (DoS) attacks. Existing detection methods face challenges distinguishing between normal traffic bursts and actual attacks and often require extensive historical data. Recent approaches combine genetic algorithms, clustering methods, and Bayesian analysis to improve prediction accuracy by modeling relationships between network traffic and attack frequency.

The methodology involves using the NSL-KDD dataset, a well-known and improved version of the KDD Cup 99 dataset, containing labeled network traffic for both normal and attack scenarios. Data preprocessing includes encoding categorical features, scaling data with Min-Max normalization, and handling class imbalance with techniques like oversampling or SMOTE.

Several ML models—Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Tree—are trained on 70% of the data and tested on the remaining 30%. Model performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrix metrics to assess their effectiveness in predicting cyberattacks.

Conclusion

Data cleaning and processing, missing price analysis, searching analysis and model construction and evaluation were the first step in the analytical process. The highest accuracy score on public testing sets will be determined by comparing each method with types of all network attacks to detect the best connections for future prediction results. This provides some insight to identify the network attack of each new connection. Using artificial intelligence, a prediction model was presented that has the ability to explore initial detection and human accuracy. This model can be inferred that the field of machine learning technology is effective in generating prediction models that can help network areas and reduce the long diagnosis long process and eradicate any human error. Based on the connection details, the network industry wants to automatically detect packet transfer attacks from the eligibility process (in real time). To display the result of prediction in desktop or web application to automate this process.To maximize the job to be applied in the atmosphere of artificial intelligence.

References

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Copyright

Copyright © 2025 Praveen Kumar Prajapati, Nitesh Kumar, Dr. Sweta Rai , Dr. Sureshwati , Ms. Tanu Kumari. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.