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Developing Secure Framework for Cyber-Attack Detection: A Machine Learning Approach

Dahiya, Mahima
http://orcid.org/0009-0002-7073-5865
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1692286936834599

Abstract Details

2023, MS, University of Cincinnati, Engineering and Applied Science: Computer Science.
Due to the rapid advancements in wireless and telecommunications systems, security in cyberspace has significantly impacted different crucial infrastructures. For developing a novel cyber security defense and protection, in addition to data on the present state of security, the system should also collect historical data. Moreover, it gives adaptive security management and control. For improving the level of safety of key system components, a Data Mining Intrusion detection system (DataMIDS) framework utilizing a selection of features based on Functional Perturbation and attack detection based on BNM-tGAN approach was developed. The developed framework was trained and put to the test using data collection in order to recognize different attacks. Initially, the data was inconsistent and incomplete due to poor scaling, missing values, overlapped, and imbalanced data. The issue of inconsistent or unstructured data was addressed in order to enhance decision-making for identifying attacks. The work deals with the missing values alongside utilizing the established Absolute Median Deviation-based Robust Scaler (AMD-RS) to address scaling performance. The pertinent feature selection of the data was carried out using the '3' FS techniques i.e., ICS-FSO wrapper, HpTT-DT embedded, and XavND-Relief filter method. The data mining-based methodology places an emphasis on feature engineering as well as feature selection and offers shallow feature learning. The data were divided while being trained using the suggested BNM-tGAN. The experiment results demonstrated that the proposed approaches were more accurate in identifying attacks across various datasets. The proposed techniques attained a low false detection rate and computation time in contrast with present techniques. In comparison with other approaches, it continues to be rather resistant to malicious attacks.
FNU NITIN, Ph.D. (Committee Chair)
Yizong Cheng, Ph.D. (Committee Member)
Raj Bhatnagar, Ph.D. (Committee Member)
60 p.
Computer Science
Cyber security; Data Mining Intrusion Detection System; Marginal Likelihood Fisher Information Matrix; Functional Perturbation; Absolute Median Deviation based Robust Scalar; Hyperparameter Tuned Threshold based Decision Tree

Recommended Citations

Citations

  • Dahiya, M. (2023). Developing Secure Framework for Cyber-Attack Detection: A Machine Learning Approach [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1692286936834599

    APA Style (7th edition)

  • Dahiya, Mahima. Developing Secure Framework for Cyber-Attack Detection: A Machine Learning Approach. 2023. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1692286936834599.

    MLA Style (8th edition)

  • Dahiya, Mahima. "Developing Secure Framework for Cyber-Attack Detection: A Machine Learning Approach." Master's thesis, University of Cincinnati, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1692286936834599

    Chicago Manual of Style (17th edition)

ucin1692286936834599
148
© 2023, some rights reserved.Creative Commons License Developing Secure Framework for Cyber-Attack Detection: A Machine Learning Approach by Mahima Dahiya is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.