Network intrusion detection system using classification techniques in machine learning
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MSc in Data Analytics
Dublin Business School
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Worldwide, the Internet is connected across the country. There are threats of network attacks in this Internet environment. The risk of integrity and con dentiality has also increased with the density of information and global reach. Security breach has become too easy. In these days the improvement of the network security is therefore highlighted. Protection of the Network allow the unintended interference to some form to network and avoid it. It consists of software for network intrusion detection that track the network. NIDS is positioned in the network in a strategic location to track tra c inside the network from source to destination apps. The machine would optimally screen both inbound and outbound tra c, but that would create a congestion that would hinder the system's overall pace. Finally, these methods include machine learning algorithms that render the device exible and deliver reliable performance. Intrusion activities leave evidence in the auditing data, so it is possible to learn and distinguish the pattern of ordinary and malicious activities with machine learning algorithms. Machine training techniques can learn normal, anomalous patterns from training data, and create classi ers for computer system attacks. In the area of intrusion detection for our research works, machine learning methods, such as logistic regression, Naive Bayes, K-Nearest Neighbor and Decision Trees were used. The research provides a predictive computational approach to optimize intrusion detection in the Network Tra c Data along with implementing di erent methodologies for the evaluation of the best accuracy from the Classi cation and Deep - Learning Algorithms. A new intrusion intrusion detection system for smart networks has been developed using a two-stage distinction (Anomaly-Misuse) and a deep methodology for learning. As detection methods to identify tra c disruption that could be attacked, the Decision Tree, logistic regression, KNN and Naive Bayes were used and Deeplearning used an ANN method that would recognize the attacks as they exist. The analysis has used the complete 42 dimensional features of the training data set. The ndings indicate that the high accuracy values are 100% with 0.10 recall levels at stage 1 of the appraisal, and 99.5% with 0.99 at stage 2 of the validation. Experimental ndings indicate that the design of the decision tree contributes to high precision in contrast with other algorithm.