Validation and Correction of Large Security Policies: A Clustering and Access Log Based Approach

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

doi:10.1109/bigdata.2018.8622610

Cited by 9 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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. In our previous work [28], we introduced the parameter priority-level to avoid the anomaly conflict-demand and reduce the more number of clusters.…”

Section: Resultsmentioning

confidence: 99%

“…MaryemAit El Hadj, Mohammed Erradi, and their team proposed an approach to cluster the security policies and additionally used the information of access log to detect the fraud intruders. They used the KNN algorithm to cluster the security rules and applied the rule-sub-module-reduction technique to minimize the count of rules in each cluster [21]. MaryemAit El Hadj and his team proposed a clustering approach to cluster XACML (eXtensible Access Control Markup Language ) policies.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

Vijayalakshmi¹,

Jayalakshmi²

2021

IJACSA

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Vijayalakshmi¹,

Jayalakshmi²

2021

IJACSA

View full text Add to dashboard Cite

show abstract

“…section 5.1.2) and is hence an important tool for ACP quality assessment. Moreover, the usage of an ACP is an indicator of its relevance, assuming that an ACP that is often invoked is more important than one that is scarcely invoked (Pan et al, 2018;Hadj et al, 2018a).…”

Section: Established Quality Criteriamentioning

confidence: 99%

Optimization of Access Control Policies

Kern¹,

Baumer²,

Groll³

et al. 2022

Journal of Information Security and Applications

View full text Add to dashboard Cite

“…Clustering [44] Triage Malware Analysis Reinforcement Learning/Ranking [47] Risk Assessment Regression [66] Forensics Attribution Clustering [132] Object Recognition…”

Section: Log Analysismentioning

confidence: 99%

Machine Learning (In) Security: A Stream of Problems

Ceschin,

Gomes,

Botacin

et al. 2020

Preprint

View full text Add to dashboard Cite

Machine Learning (ML) has been widely applied to cybersecurity, and is currently considered state-of-the-art for solving many of the field´s open issues. However, it is very difficult to evaluate how good the produced solutions are, since the challenges faced in security may not appear in other areas (at least not in the same way). One of these challenges is the concept drift, that actually creates an arms race between attackers and defenders, given that any attacker may create novel, different threats as time goes by (to overcome defense solutions) and this "evolution´´is not always considered in many works. Due to this type of issue, it is fundamental to know how to correctly build and evaluate a ML-based security solution. In this work, we list, detail, and discuss some of the challenges of applying ML to cybersecurity, including concept drift, concept evolution, delayed labels, and adversarial machine learning. We also show how existing solutions fail and, in some cases, we propose possible solutions to fix them.

show abstract

Validation and Correction of Large Security Policies: A Clustering and Access Log Based Approach

Cited by 9 publications

References 4 publications

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

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

Optimization of Access Control Policies

Machine Learning (In) Security: A Stream of Problems

Contact Info

Product

Resources

About