| Title | MLIDS: A Machine Learning Approach for Intrusion Detection for Real Time Network Dataset |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Rokade, Monika D., Sharma, Yogesh Kumar |
| Conference Name | 2021 International Conference on Emerging Smart Computing and Informatics (ESCI) |
| Keywords | artificial neural network, Classification algorithms, composability, cryptography, cyber physical systems, Deep Learning, Intrusion detection, intrusion detection system, KDDCUP99, machine learning algorithms, naive Bayes, Network security, pubcrawl, Real-time Systems, resilience, Resiliency, security, Support vector machines, SVM, virtual machine security |
| Abstract | Computer network and virtual machine security is very essential in today's era. Various architectures have been proposed for network security or prevent malicious access of internal or external users. Various existing systems have already developed to detect malicious activity on victim machines; sometimes any external user creates some malicious behavior and gets unauthorized access of victim machines to such a behavior system considered as malicious activities or Intruder. Numerous machine learning and soft computing techniques design to detect the activities in real-time network log audit data. KKDDCUP99 and NLSKDD most utilized data set to detect the Intruder on benchmark data set. In this paper, we proposed the identification of intruders using machine learning algorithms. Two different techniques have been proposed like a signature with detection and anomaly-based detection. In the experimental analysis, demonstrates SVM, Naive Bayes and ANN algorithm with various data sets and demonstrate system performance on the real-time network environment. |
| DOI | 10.1109/ESCI50559.2021.9396829 |
| Citation Key | rokade_mlids_2021 |
MLIDS: A Machine Learning Approach for Intrusion Detection for Real Time Network Dataset