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Novel Class Probability Features for Optimizing Network Attack Detection With Machine Learning
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DOI：10.1109/ACCESS.2023.3313596.png)
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摘要
En 中文
Network attacks refer to malicious activities exploiting computer network vulnerabilities to compromise security, disrupt operations, or gain unauthorized access to sensitive information. Common network attacks include phishing, malware distribution, and brute-force attacks on network devices and user credentials. Such attacks can lead to financial losses due to downtime, recovery costs, and potential legal liabilities. To counter such threats, organizations use Intrusion Detection Systems (IDS) that leverage sophisticated algorithms and machine learning techniques to detect network attacks with enhanced accuracy and efficiency. Our proposed research aims to detect network attacks effectively and timely to prevent harmful losses. We used a benchmark dataset named CICIDS2017 to build advanced artificial intelligence-based machine learning methods. We propose a novel approach called Class Probability Random Forest (CPRF) for network attack detection performance enhancement. We created a novel feature set using the proposed CPRF approach. The CPRF approach predicts the class probabilities from the network attack dataset, which are then used as features for building applied machine learning methods. The comprehensive research results demonstrated that the random forest approach outperformed the state-of-the-art approach with a high-performance accuracy of 99.9%. The performance of each applied technique is validated using a k-fold approach and optimized with hyperparameter tuning. Our novel proposed research has revolutionized network attack detection, effectively preventing unauthorized access, service disruptions, sensitive information theft, and data integrity compromise.
Keyword:
Feature extraction
Random forests
Analytical models
Training
Security
Data models
Cyberattack
Machine learning
Intrusion detection
Network attacks
intrusion detection
machine learning
feature engineering
期刊
IF:
3.6
论文数:
9.8W
被引数:
33.1W
机构
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