Prevention of Data Breach by Machine Learning Techniques
- Title
- Prevention of Data Breach by Machine Learning Techniques
- Creator
- Adharsh C.; Vijayalakshmi S.
- Description
- In today's data communication environment, network and system security is vital. Hackers and intruders can gain unauthorized access to networks and online services, resulting in some successful attempts to knock down networks and web services. With the progress of security systems, new threats and countermeasures to these assaults emerge. Intrusion Detection Systems are one of these choices (IDS). An Intrusion Detection System's primary goal is to protect resources from attacks. It analyses and anticipates user behavior before determining if it is an assault or a common occurrence. We use Rough Set Theory (RST) and Gradient Boosting to identify network breaches (using the boost library). When packets are intercepted from the network, RST is used to pre-process the data and reduce the dimensions. A gradient boosting model will be used to learn and evaluate the features chosen by RST. RST-Gradient boost model provides the greatest results and accuracy when compared to other scale-down strategies like regular scaler. 2022 IEEE.
- Source
- 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022, pp. 1819-1823.
- Date
- 2022-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Gradient Boost; Intrusion Detection System; Rough Set Theory
- Coverage
- Adharsh C., Christ (Deemed to Be University), Department of Data Science, Maharashtra, Pune, India; Vijayalakshmi S., Christ (Deemed to Be University), Department of Data Science, Maharashtra, Pune, India
- Rights
- Restricted Access
- Relation
- ISBN: 978-166543789-9
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Adharsh C.; Vijayalakshmi S., “Prevention of Data Breach by Machine Learning Techniques,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/20230.