Machine Learning-Based Intrusion Detection System (IDS) for Classifying Types of Attacks on Computer Networks

Fiska, Ryci Rahmatil
Wahyat, Wahyat
Hermawan, Dedi
Laurenz, Via
Fateha, Izatul

Proceeding Applied Business and Engineering Conference
Vol. 12 (2024): 12th Applied Business and Engineering Conference

Server security on a computer network is very important, maintaining the security of a computer network inorder to maintain information, data and maintain infrastructure so that it can work and function properly and provideaccess rights to registered users, this research, aims to build an IDS (Intrusion Detection System) on the network andServer using Raspberry Pi with SNORT which is useful for monitoring Server activity when an attempted attack occurs.With the increasing complexity of network attacks carried out by attackers, intelligent and adaptive approaches areneeded to detect and overcome these threats. Traditional methods such as rule-based or signatures are often not effectiveenough in the face of evolving attacks. The large amount of network traffic data makes it difficult to manually analyzeand detect attacks. Naive Bayes has a very important role in the classification and detection of network attacks, bothconsidered malicious and highly malicious, By using Naive Bayes, network security systems can become more proactiveand adaptive to attacks. This technology not only helps in detecting familiar attacks but also enables identification andresponse to new or unknown attack techniques. Through proper classification, the system can provide better protectionand reduce the impact of attacks.