A Model-Driven-Engineering Approach for Detecting Privilege Escalation in IoT Systems
May 23, 2022 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Atheer Abu Zaid, Manar H. Alalfi, Ali Miri
arXiv ID
2205.11406
Category
cs.SE: Software Engineering
Cross-listed
cs.CR
Citations
1
Venue
arXiv.org
Last Checked
5 months ago
Abstract
Software vulnerabilities in access control models can represent a serious threat in a system. In fact, OWASP lists broken access control as number 5 in severity among the top 10 vulnerabilities. In this paper, we study the permission model of an emerging Smart-Home platform, SmartThings, and explore an approach that detects privilege escalation in its permission model. Our approach is based on Model Driven Engineering (MDE) in addition to static analysis. This approach allows for better coverage of privilege escalation detection than static analysis alone, and takes advantage of analyzing free-form text that carries extra permissions details. Our experimental results demonstrate a very high accuracy for detecting over-privilege vulnerabilities in IoT applications
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted