Evaluation of Machine Learning Algorithms for Intrusion Detection System (1801.02330v1)

Abstract: Intrusion detection system (IDS) is one of the implemented solutions against harmful attacks. Furthermore, attackers always keep changing their tools and techniques. However, implementing an accepted IDS system is also a challenging task. In this paper, several experiments have been performed and evaluated to assess various machine learning classifiers based on KDD intrusion dataset. It succeeded to compute several performance metrics in order to evaluate the selected classifiers. The focus was on false negative and false positive performance metrics in order to enhance the detection rate of the intrusion detection system. The implemented experiments demonstrated that the decision table classifier achieved the lowest value of false negative while the random forest classifier has achieved the highest average accuracy rate.

• The paper demonstrates that Random Forest outperforms other classifiers with a 93.77% accuracy, highlighting its effectiveness in intrusion detection.
• The analysis reveals that Decision Table minimizes false negatives to 0.002 despite a higher false positive rate, while Bayes Network achieves an impressive ROC of 0.999.
• The study underscores the potential of hybrid models that leverage diverse machine learning approaches to counter rapidly evolving network threats.

Evaluation of Machine Learning Algorithms for Intrusion Detection System

The paper "Evaluation of Machine Learning Algorithms for Intrusion Detection System" presents an empirical assessment of multiple machine learning classifiers for use in intrusion detection systems (IDS), a critical component of network security. The KDD dataset, a well-established benchmark for evaluating IDS performance, serves as the basis for the experiments conducted. This paper involves extensive analysis using several classifiers: J48, Random Forest, Random Tree, Decision Table, Multi-layer Perceptron (MLP), Naive Bayes, and Bayes Network, focusing on metrics such as false negative rate, false positive rate, accuracy, and precision.

Experimental Setup

The authors leverage the KDD dataset, a well-recognized benchmark consisting of 4,898,431 instances across 41 attributes, which include different types of network attacks—namely DOS, R2L, U2R, and PROBE. Recognizing the dataset's imbalance, the researchers extract 148,753 instances for training, maintaining proportional attack categories found in the original data (79% DOS, 19% normal, and 2% others). A further 60,000 instances are selected in a fully randomized manner for testing.

Classifier Performance and Comparative Analysis

The paper reveals several notable findings:

• Random Forest emerges as the classifier with the highest average accuracy rate of 93.77%, outperforming others in root mean square error (RMSE) and false positive rate parameters.
• Decision Table achieves the lowest false negative rate (0.002), highlighting its proficiency in minimizing undetected attacks, yet it suffers from a higher false positive rate (0.073), indicating a significant portion of normal traffic misclassified as intrusions.
• MLP requires the longest training time at 176 minutes, illustrating the computational cost of neural network approaches.
• Bayes Network achieves the highest ROC value (0.999), indicative of excellent discrimination capacity between attack and normal traffic.

Performance Metrics

The evaluation employs confusion matrices and several key performance metrics:

• True Positive (TP) and True Negative (TN): Indicate correct classification of attack and normal packets, respectively.
• False Positive (FP) and False Negative (FN): Illustrate misclassification rates. Minimizing these values is critical to maintaining network resource availability and confidentiality.
• Precision and ROC: Offer insights into the classifier’s reliability and the area under the ROC further assesses its overall accuracy.

Implications and Future Outlook

The paper effectively demonstrates that no singular machine learning algorithm proves wholly sufficient for handling every type of attack. Although Random Forest stands out in terms of overall accuracy, the importance of minimizing false negatives is underscored, especially in preserving network security. This suggests future development in hybrid models or ensemble methods that might combine the strengths of various algorithms.

Interestingly, this paper reinforces that while traditional single-method approaches provide foundational insights, the dynamic nature of network threats demands progressive adaptation and combination of techniques to achieve robust intrusion detection. Moreover, the use of comprehensive and diverse datasets such as KDD is emphasized for a real-world application that aligns with ever-evolving cybersecurity threats.

By offering a comparative analysis, this research contributes valuable insights into the design and implementation of intrusion detection systems and underscores the persistent requirement for innovation in IDS methodologies in response to continuous advances in attack strategies.