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Conference proceeding
Enhancing Network Security Through Proactive Anomaly Detection: A Comparative Study of Auto-Encoder Models and K-Nearest Neighbours Algorithm
2023 3rd Intelligent Cybersecurity Conference (ICSC), pp.119-126
Intelligent Cybersecurity Conference (ICSC), 3rd (San Antonio, TX, USA, 10/23/2023–10/25/2023)
10/23/2023
Abstract
In our interconnected digital landscape, safeguarding network security is paramount. This research juxtaposes two anomaly detection methods: an Auto-encoder model using Ten-sorFlow's Keras and the K-Nearest Neighbours (KNN) algorithm. Beyond assessing model performance, this study underscores the practical relevance of these techniques in real-world security contexts. The KNN results reveal 202,325 True Positives, 4,442 True Negatives, 960 (0.045%) False Positives (Type-I error), and 2,274 (1.08%) False Negatives (Type-II error), while the Auto-encoder model achieves 130,260 True Positives, 1,791 True Negatives, 5,208 (3.7%) False Positive (Type-I error), and 2,742 (1.96%) False Negatives (Type-II error). Crucially, this research emphasizes that timely anomaly detection is the linchpin in thwarting potential security breaches, with anomaly prevention serving as a proactive defense strategy. By harnessing machine learning and data-driven methodologies, this work contributes to fortifying network security. These findings provide security prac-titioners with valuable insights into the pivotal role of anomaly detection in intrusion prevention. Furthermore, this study paves the way for future advancements in network security, solidifying the position of proactive anomaly detection in cybersecurity.
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Details
- Title
- Enhancing Network Security Through Proactive Anomaly Detection
- Publication Details
- 2023 3rd Intelligent Cybersecurity Conference (ICSC), pp.119-126
- Resource Type
- Conference proceeding
- Conference
- Intelligent Cybersecurity Conference (ICSC), 3rd (San Antonio, TX, USA, 10/23/2023–10/25/2023)
- Publisher
- IEEE
- Copyright
- © 2023, IEEE
- Identifiers
- 99380490096506600
- Academic Unit
- Hal Marcus College of Science and Engineering
- Language
- English