Clustering-based Approach for Anomaly Detection in XACML Policies

Hadj, Maryem Ait El; Ayache, Meryeme; Benkaouz, Yahya; Khoumsi, Ahmed; Erradi, Mohammed

doi:10.5220/0006471205480553

Cited by 12 publications

(2 citation statements)

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“…In some previous researches, the number of clusters is very high. Also, only minimum rules are similar in a cluster, and this leads to complexities in detecting and removing anomalies [22]. Unlike the rule-sub-modulereduction method [21], we merge the cluster at the time of the creation of the cluster itself, which increases the performance of the approach.…”

Section: Resultsmentioning

confidence: 99%

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Improving Performance of ABAC Security Policies Validation using a Novel Clustering Approach

Vijayalakshmi¹,

Jayalakshmi²

2021

IJACSA

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Cloud computing offers several services, such as storage, software, networking, and other computing services. Cloud storage is a boon for big data and big data owners. Although big data owners can easily avail cloud storage without spending much on infrastructure and software to manage their data, security is a big issue, and protecting the outsourced big data is challenging and ongoing research. Cloud service providers use the attribute-based access control model to detect malicious intruders and address the security requirements of today's new computing technologies. Anomalies in security policies are removed to improve the efficiency of the access control model. This paper implements a novel clustering approach to cluster security policies. Our proposed approach uses a rule-specific cluster merging technique that compares the rule with the clusters where the probability of similarity is high. Hence this technique reduces the cost, time, and complexity of clustering. Rather than verifying all rules, detecting and removing anomalies in every cluster of rules improve the performance of the intrusion detection system. Our novel clustering approach is useful for the researchers and practitioners in the ABAC policy validation.

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Section: Resultsmentioning

confidence: 99%

“…There must be non-empty clusters only. The above approach may produce a maximum number of clusters and clusters with a maximum number of rules [22]. In contrary to the above research, we proposed and implemented a novel approach that our approach uses the basic technique of hierarchical clustering algorithm [24].…”

Section: Related Workmentioning

confidence: 99%