Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection (1005.4496v1)

Abstract: In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The proposed algorithm also addresses some difficulties of data mining such as handling continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviours, several data miningbased intrusion detection techniques have been applied to network-based traffic data and host-based data in the last decades. However, there remain various issues needed to be examined towards current intrusion detection systems (IDS). We tested the performance of our proposed algorithm with existing learning algorithms by employing on the KDD99 benchmark intrusion detection dataset. The experimental results prove that the proposed algorithm achieved high detection rates (DR) and significant reduce false positives (FP) for different types of network intrusions using limited computational resources.

• The paper introduces a hybrid learning algorithm that synergizes the probabilistic strengths of Naive Bayes with the decision-making power of Decision Trees to boost intrusion detection efficiency.
• It effectively manages noise, continuous attributes, and missing values by optimizing the dataset through the removal of redundant examples.
• Rigorous testing on the KDD99 dataset demonstrates over 99% detection rates and significantly reduced false positives compared to standalone methods.

Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection

The paper presents a novel hybrid learning algorithm that effectively integrates Naive Bayes and Decision Tree techniques for adaptive network intrusion detection. This approach aims to improve intrusion detection by balancing detection rates and minimizing false positives across various network attack types. The algorithm addresses challenges inherent in the field, such as handling noise, continuous attributes, and missing attribute values, which often complicate the development of effective Intrusion Detection Systems (IDS).

Core Contributions

1. Hybrid Learning Algorithm: The algorithm synergizes the strengths of Naive Bayes for probabilistic learning and Decision Trees for information-based decision-making, allowing it to manage continuous attributes effectively while reducing training data noise.
2. Dataset Optimization: The model efficiently removes redundant and contradictory examples from the dataset, enhancing both learning efficiency and model clarity.
3. Robust Evaluation: The algorithm is rigorously tested against the KDD99 dataset, a benchmark for intrusion detection research. The results show superior performance in true positive detection rates and a significant reduction in false positives compared to standalone Naive Bayes and Decision Tree approaches.

Experimental Results

The hybrid algorithm exhibits high detection rates (>99%) across all attack types and notably low false positives, especially in complex classes like Remote to User (R2L) and User to Root (U2R). The model's performance using the full set of 41 attributes, as well as reduced attribute sets, demonstrates its robustness and adaptability. The detection rate remains consistently above 99% even when the dataset is reduced to 12 or 17 attributes, highlighting the algorithm's efficiency in attribute selection and its potential for practical deployment where computational resources may be limited.

Implications and Future Directions

The proposed hybrid approach represents a significant advance in the adaptive intrusion detection landscape. By effectively combining probabilistic reasoning with decision-based classification, this method addresses the critical trade-off between detection accuracy and computational efficiency—a perpetual challenge in IDS development. The successful reduction of false positives is particularly noteworthy, as this has been a persistent issue in anomaly-based IDS methods.

Future research could focus on optimizing the false positive rates further, particularly for R2L attacks, which remain slightly higher compared to other attack types. Additionally, real-world testing and integration into existing IDS frameworks would provide valuable insights into the algorithm's operational efficacy and scalability.

The integration of such adaptive models into real-time network monitoring systems could significantly bolster cybersecurity measures by offering rapid and accurate detection of complex and evolving attack vectors. The inclusion of advanced machine learning techniques and continuous model training and updating mechanisms could further enhance detection accuracy and responsiveness to new attack patterns.

In conclusion, this research paper makes a substantial contribution to the field of network security by presenting a hybrid learning algorithm that significantly augments detection capabilities while maintaining resource efficiency, thereby offering a viable path toward more resilient and adaptive intrusion detection systems.