Date of Award
5-2020
Culminating Project Type
Thesis
Degree Name
Information Assurance: M.S.
Department
Information Assurance and Information Systems
College
Herberger School of Business
First Advisor
Akalanka Mailewa Dissanayaka
Second Advisor
Mark Schmidt
Third Advisor
David Robinson
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Keywords and Subject Headings
Autoencoders, Deep Learning, Anomaly Detection, Artificial Intelligence, Machine Learning, Information Assurance, Intrusion Detection System
Abstract
With the recent advances in Internet-of-thing devices (IoT), cloud-based services, and diversity in the network data, there has been a growing need for sophisticated anomaly detection algorithms within the network intrusion detection system (NIDS) that can tackle advanced network threats. Advances in Deep and Machine learning (ML) has been garnering considerable interest among researchers since it has the capacity to provide a solution to advanced threats such as the zero-day attack. An Intrusion Detection System (IDS) is the first line of defense against network-based attacks compared to other traditional technologies, such as firewall systems. This report adds to the existing approaches by proposing a novel strategy to incorporate both supervised and unsupervised learning to Intrusion Detection Systems (IDS). Specifically, the study will utilize deep Autoencoder (DAE) as a dimensionality reduction tool and Support Vector Machine (SVM) as a classifier to perform anomaly-based classification. The study diverts from other similar studies by performing a thorough analysis of using deep autoencoders as a valid non-linear dimensionality tool by comparing it against Principal Component Analysis (PCA) and tuning hyperparameters that optimizes for 'F-1 Micro' score and 'Balanced Accuracy' since we are dealing with a dataset with imbalanced classes. The study employs robust analysis tools such as Precision-Recall Curves, Average-Precision score, Train-Test Times, t-SNE, Grid Search, and L1/L2 regularization. Our model will be trained and tested on a publicly available datasets KDDTrain+ and KDDTest+.
Recommended Citation
Khan, Shehram, "Autoencoder-Based Representation Learning to Predict Anomalies in Computer Networks" (2020). Culminating Projects in Information Assurance. 102.
https://repository.stcloudstate.edu/msia_etds/102
