Network Intrusion Detection using Machine Learning Algorithms
B. Sankara Babu, G.Akshay Reddy, D.Kushal Goud, K Naveen, K.Sai Tharun Reddy
Abstract
The advancement in wireless communication technology has led to various security challenges in networks. To combat these issues, Network Intrusion Detection Systems (NIDS) are employed to identify attacks. To enhance their accuracy in detecting intruders, various machine learning techniques have been previously used with NIDS. This paper presents a new approach that utilizes machine learning techniques to identify intrusions. The findings of our model indicate that it outperforms other methods, such as Naive Bayes, in terms of accuracy. Our method resulted in a performance time of 1.26 minutes, an accuracy rate of 97.38%, and an error rate of 0.25%.
Topics & Concepts
Computer scienceIntrusion detection systemMachine learningNaive Bayes classifierArtificial intelligenceAlgorithmNetwork securityWord error rateData miningSupport vector machineComputer securityNetwork Security and Intrusion DetectionAnomaly Detection Techniques and ApplicationsInternet Traffic Analysis and Secure E-voting