K.Shivaganesh, A.Jaya Sree, P.Rashmitha, V.Sanjay
Random forest, Support Vector Machine , KDD99 , Dynamic Learning Rate, Machine Learning , Intruder Detection System
Intrusion Detection Systems (IDSs) are critical in ensuring the security of computer networks. The existing IDSs use various machine learning algorithms such as SVM and Random Forest to detect network intrusions. However, in real-world scenarios, the network traffic can be highly dynamic, and the intrusion patterns can change over time, making it challenging to detect new types of intrusions accurately. It is difficult to guarantee the success of any intrusion detection system due to the nonlinearity and number of features in the network traffic data stream. Several intrusion detection techniques with various degrees of accuracy have been developed to address this issue. As a result, choosing an efficient and reliable IDS method is an important subject in information security. For classification purposes in this work, two models were created, one based on Support Vector Machines (SVM), and the other on Random Forests (RF). According to the experimental data, both classifiers are efficient, with SVM having a marginally greater accuracy but taking longer to run. On the other hand, when given the right modelling parameters, RF generates similar accuracy far more quickly. By improving its accuracy, these classifiers can improve an IDS system. To choose the best intrusion detector for this dataset, we used the KDD'99 dataset. Our statistical research uncovered important problems that have a negative impact on the performance of the systems under examination and lead to inadequate assessments of techniques to anomaly identification. The KDD'99 dataset's enormous amount of redundant records is its main problem. With the help of this study's conclusions, SVM and RF can be used more effectively to improve performance and reduce the false-negative rate.
Article Details
Unique Paper ID: 159358

Publication Volume & Issue: Volume 9, Issue 11

Page(s): 1103 - 1107
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