INTRUSION DETECTION SYSTEM BASED ON MACHINE LEARNING

Abstract

Intrusion Detection System is a software application to detect network intrusion. Network Intrusion Detection System(NIDS) needs to accurately identify malicious network attacks. Network intrusion using various machine learning algorithms to helps to detect network intrusion. In imbalanced network traffic, malicious cyber attacks can often hide in large amounts of normal data. It exhibits a high degree of stealth and obfuscation in cyberspace, making it difficult for Network Intrusion Detection System(NIDS) to ensure the accuracy and timeliness of detection. It proposes a novel Difficult Set Sampling Technique(DSSTE) algorithm to tackle

the class imbalance problem. First, use the Edited Nearest Neighbor(ENN) algorithm to divide the imbalanced training set into the difficult set and the easy set.

Next, use the KMeans algorithm to compress the majority samples in the difficult set to reduce the majority. Zoom in and out the minority samples’ continuous

attributes in the difficult set synthesize new samples to increase the minority number. Finally, the easy set, the compressed set of majority in the difficult, and the

minority in the difficult set are combined with its augmentation samples to make up a new training set.The algorithm reduces the imbalance of the original training set and provides targeted data augment for the minority class that needs to learn.

It enables the classifier to learn the differences in the training stage better and im- prove classification performance. The classic intrusion dataset CSE-CIC-IDS2018

is used to verify the proposed method. The proposed method use Logistic Regression Model, Decision Tree Classifier Model , K Nearest Neighbors Classifier

Model and Naive Bayes model. Compare this algorithm to identify which perform better in intrusion detection.