| Title | A Framework to Detect the Malicious Insider Threat in Cloud Environment using Supervised Learning Methods |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Padmavathi, G., Shanmugapriya, D., Asha, S. |
| Conference Name | 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom) |
| Keywords | anomaly detection, behavioral model, Computational modeling, Data integration, Deep Learning, Human Behavior, human factors, insider threat, Insider Threat Detection, insider threats, machine learning, Measurement, Metrics, OCSVM, Organizations, Policy Based Governance, policy-based governance, pubcrawl, resilience, Resiliency, supervised learning, Support vector machines |
| Abstract | A malicious insider threat is more vulnerable to an organization. It is necessary to detect the malicious insider because of its huge impact to an organization. The occurrence of a malicious insider threat is less but quite destructive. So, the major focus of this paper is to detect the malicious insider threat in an organization. The traditional insider threat detection algorithm is not suitable for real time insider threat detection. A supervised learning-based anomaly detection technique is used to classify, predict and detect the malicious and non-malicious activity based on highest level of anomaly score. In this paper, a framework is proposed to detect the malicious insider threat using supervised learning-based anomaly detection. It is used to detect the malicious insider threat activity using One-Class Support Vector Machine (OCSVM). The experimental results shows that the proposed framework using OCSVM performs well and detects the malicious insider who obtain huge anomaly score than a normal user. |
| DOI | 10.23919/INDIACom54597.2022.9763205 |
| Citation Key | padmavathi_framework_2022 |
A Framework to Detect the Malicious Insider Threat in Cloud Environment using Supervised Learning Methods