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Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection
MohammadNoor Injadat, Fadi Salo, Ali Bou Nassif, Aleksander Essex, Abdallah Shami
TL;DR
Network intrusion detection must address prevalent attacks and changing, high-volume traffic that threaten information security. The paper uses Bayesian Optimization to tune SVM-RBF, Random Forest, and k-NN parameters on ISCX 2012, and reports improved accuracy with low false-alarm rates across the optimized classifiers.
Problem
Network attacks threaten the confidentiality, integrity, and availability of critical information, while intrusion detection faces changing environments and high-volume, high-dimensional traffic.
Method
Bayesian Optimization tunes parameters of SVM-RBF, Random Forest, and k-NN by minimizing their objective functions on the ISCX 2012 dataset.
Results
The optimized classifiers improve reported accuracy and reduce or maintain false-alarm rates, including testing accuracy gains for SVM-RBF and Random Forest.
Takeaways & Limitations
Bayesian Optimization provides an effective parameter-selection framework for the considered machine-learning anomaly-detection classifiers.
Abstract
from arXiv · showhide
Network attacks have been very prevalent as their rate is growing tremendously. Both organization and individuals are now concerned about their confidentiality, integrity and availability of their critical information which are often impacted by network attacks. To that end, several previous machine learning-based intrusion detection methods have been developed to secure network infrastructure from such attacks. In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique to tune the parameters of Support Vector Machine with Gaussian Kernel (SVM-RBF), Random Forest (RF), and k-Nearest Neighbor (k-NN) algorithms. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
I. INTRODUCTION
The paper motivates anomaly-based intrusion detection for changing, high-volume, high-dimensional network environments and proposes Bayesian-optimized machine-learning classifiers evaluated on ISCX 2012.
- I. INTRODUCTION: Anomaly-based detection can identify unknown intrusions by modeling normal behavior, unlike signature-based systems that miss new attack patterns.
- I. INTRODUCTION: High traffic volume, changing environments, high-dimensional datasets, and real-time requirements continue to challenge intrusion detection systems.
- I. INTRODUCTION: Bayesian Optimization tunes SVM-RBF, Random Forest, and k-NN parameters for anomaly detection on the ISCX 2012 intrusion dataset.The proposed models are called BO-SVM, BO-RF, and BO-kNN.
- I. INTRODUCTION: The framework seeks higher classification accuracy and lower false-alarm rates through objective-function minimization and optimal parameter selection.
- I. INTRODUCTION: The study uses UNB ISCX 2012 to validate the optimization process and reports no previous related work applying Bayesian Optimization to this dataset for anomaly detection.
II. RELATED WORK
The related background frames intrusion detection as a classification problem and introduces SVM-based classification, including kernel mapping and objective minimization.
- II. RELATED WORK: Intrusion detection has been approached with data-mining classifiers including SVM, decision trees, k-NN, and Naive Bayes.
- II. RELATED WORK: SVM classifies positive and negative samples by determining a maximum-separation hyperplane, with Gaussian kernels supporting nonlinearly separable data.
- II. RELATED WORK: The SVM formulation determines the weight vector and intercept by minimizing an objective function, with C penalizing incorrectly classified instances.
B. k-Nearest Neighbors (k-NN)
k-NN classifies an instance from the majority class among its k nearest training points, using a selected distance measure.
- B. k-Nearest Neighbors (k-NN): k-NN assigns an instance the majority class among its k nearest neighboring training points.The algorithm evaluates distances, identifies the nearest points, and then applies majority voting.
- B. k-Nearest Neighbors (k-NN): Euclidean and Mahalanobis distance are examples of measures used to identify the nearest neighbors.
C. Random Forests (RF)
Random Forest combines independently sampled decision trees and predicts classes through majority voting, with random feature selection used during tree growth.
- C. Random Forests (RF): Random Forest combines several decision trees and predicts a new data point’s class using the majority of tree votes.
- C. Random Forests (RF): The procedure chooses the number of trees and randomly selects m variables from M variables to find splits at each node.The described setting requires m ≪ M.
- C. Random Forests (RF): Each tree is grown from a bootstrap sample of the training cases and expanded to maximum size without pruning.
D. Bayesian Optimization (BO)
Bayesian optimization minimizes an objective by modeling it with a Gaussian process, updating that model, and selecting evaluations through an acquisition function.
- D. Bayesian Optimization (BO): Bayesian optimization combines a Gaussian process model, Bayesian updates after objective evaluations, and an acquisition function.The method targets minimization of a scalar objective function f(x).
- D. Bayesian Optimization (BO): The optimization procedure can produce different outputs for the same input when the objective function is stochastic rather than deterministic.
- D. Bayesian Optimization (BO): The acquisition process identifies xbest as the location of the lowest posterior mean, whose value is denoted µQ(xbest).
A. Dataset Description
The experiments use the ISCX 2012 dataset, which contains network traffic covering normal and intrusion activity across seven days.
- A. Dataset Description: Nearly 1.5 million network traffic packets and 20 features comprise the ISCX 2012 dataset.
- A. Dataset Description: The dataset covers seven days of network activity containing both normal and intrusion traffic.
- A. Dataset Description: A random subset was extracted from the original ISCX 2012 dataset for the experiments.
B. Experimental setup and Data Pre-processing
The experimental data were converted into a 14-feature dataset by removing payloads and replacing start and end times with duration, while categorical values were mapped numerically.
- B. Experimental setup and Data Pre-processing: The selected data were transformed into a new dataset consisting of 14 features.
- B. Experimental setup and Data Pre-processing: Payload features were eliminated because most packet contents were empty, and start and end times were replaced by a duration feature.
- B. Experimental setup and Data Pre-processing: Nonnumeric feature values were transformed into numeric values using MATLAB’s categorical data-mapping technique.
C. Prediction Performance Measures
Prediction models are evaluated quantitatively using accuracy and precision, with the measures defined from true and false positive and negative outcomes.
- C. Prediction Performance Measures: The evaluation uses quantitative measurements to compare prediction models.
- C. Prediction Performance Measures: Accuracy is calculated as (TP + TN) / (TP + TN + FP + FN).
- C. Prediction Performance Measures: TP, TN, FP, and FN denote true positive, true negative, false positive, and false negative rates, respectively.
D. Results Discussion
Bayesian Optimization tuned SVM-RBF, k-NN, and RF parameters by minimizing classifier objective functions, with optimized configurations improving reported anomaly-detection performance on ISCX 2012.
- Optimization process: The optimization minimized a cost function J(P) over classifier parameter sets P, targeting high classification accuracy and minimum false-alarm rate.The tuned parameters included SVM C and γ, RF tree depth and ensemble method, and the k-NN neighbor and distance settings.
- SVM-RBF: 99.95% training and 99.84% testing accuracy were achieved after optimizing SVM-RBF, versus 99.58% and 99.59% without optimization.The optimized SVM used C = 433.32 and γ = 1.0586, while FAR decreased by 0.01 in training and 0.007 in testing.
- Optimization process: BO found unique global minima for SVM-RBF, k-NN, and RF objective functions, with each objective reaching its minimum within at most 30 iterations.For k-NN, the optimum was k = 1 with Mahalanobis distance; for RF, it used 1004 tree splits and AdaBoost.
- Random Forest: 99.98% training and 99.92% testing accuracy were achieved after optimizing RF, versus 99.97% and 99.88% without optimization.The selected RF configuration used 1004 tree splits and AdaBoost; testing FAR decreased by 0.001 while training FAR remained steady.
- Additional classifier: Naïve Bayes was excluded after producing 87.23% training and 87.65% testing accuracy because the dataset features were not fully independent.The classifier was used only at the initial experiment stage.
- Comparison with prior work: Prior ISCX 2012 experiments reported 95% overall accuracy and 99.8% training versus 99.0% testing accuracy, which the paper states its results outperform.These comparisons are attributed to prior studies cited as and.
V. CONCLUSIONS
The paper applies Bayesian Optimization to tune SVM-RBF, RF, and k-NN parameters for anomaly detection, improving classification performance on the ISCX 2012 dataset. Overall, BO-optimized k-NN achieved the best performance across accuracy, precision, recall, and false alarm rate.
- Bayesian Optimization improved the three conventional classifiers by finding global minima of their objective functions, leading to better classification results.
- The proposed framework combines Bayesian Optimization with SVM-RBF, Random Forest, and k-NN to enhance anomaly detection performance.
- BO-optimized k-NN achieved the optimum performance on ISCX 2012 across accuracy, precision, recall, and false alarm rate.