DUCE: Distributed Usage Control Enforcement for Private Data Sharing in Internet of Things

2021 ◽  
pp. 278-290
Author(s):  
Na Shi ◽  
Bo Tang ◽  
Ravi Sandhu ◽  
Qi Li
Keyword(s):  
Internet Of Things ◽  
Data Sharing ◽  
Usage Control ◽  
Private Data

Attribute-based Private Data Sharing with Script-driven Programmable Ciphertext and Decentralized Key Management in Blockchain Internet of Things

2021 ◽  
pp. 1-1
Author(s):  
Hongjian Yin ◽  
E Chen ◽  
Yan Zhu ◽  
Chengwei Zhao ◽  
Rongquan Feng ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Data Sharing ◽  
Key Management ◽  
Private Data

Securing Private Data Sharing in Multi-Party Analytics

2016 ◽  
Author(s):  
Gowtham Bellala
Keyword(s):  
Data Sharing ◽  
Private Data

scholarly journals Next Generation of SDN in Cloud-Fog for 5G and Beyond-Enabled Applications: Opportunities and Challenges

Network ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 28-49
Author(s):  
Ehsan Ahvar ◽  
Shohreh Ahvar ◽  
Syed Mohsan Raza ◽  
Jose Manuel Sanchez Vilchez ◽  
Gyu Myoung Lee
Keyword(s):  
Internet Of Things ◽  
Data Sharing ◽  
Mobile Networks ◽  
The Other ◽  
The Internet ◽  
Hybrid Cloud ◽  
Next Generation ◽  
Delay Constraints ◽  
Mission Critical ◽  
Iot Devices

In recent years, the number of objects connected to the internet have significantly increased. Increasing the number of connected devices to the internet is transforming today’s Internet of Things (IoT) into massive IoT of the future. It is predicted that, in a few years, a high communication and computation capacity will be required to meet the demands of massive IoT devices and applications requiring data sharing and processing. 5G and beyond mobile networks are expected to fulfill a part of these requirements by providing a data rate of up to terabits per second. It will be a key enabler to support massive IoT and emerging mission critical applications with strict delay constraints. On the other hand, the next generation of software-defined networking (SDN) with emerging cloudrelated technologies (e.g., fog and edge computing) can play an important role in supporting and implementing the above-mentioned applications. This paper sets out the potential opportunities and important challenges that must be addressed in considering options for using SDN in hybrid cloud-fog systems to support 5G and beyond-enabled applications.

Sustainable Security for the Internet of Things Using Artificial Intelligence Architectures

2021 ◽  
Vol 21 (3) ◽  
pp. 1-22
Author(s):  
Celestine Iwendi ◽  
Saif Ur Rehman ◽  
Abdul Rehman Javed ◽  
Suleman Khan ◽  
Gautam Srivastava
Keyword(s):  
Internet Of Things ◽  
Detection System ◽  
Experimental Testing ◽  
Cyber Attacks ◽  
Internet Technology ◽  
The Internet ◽  
Business Operations ◽  
Private Data ◽  
The Internet Of Things ◽  
Better Than

In this digital age, human dependency on technology in various fields has been increasing tremendously. Torrential amounts of different electronic products are being manufactured daily for everyday use. With this advancement in the world of Internet technology, cybersecurity of software and hardware systems are now prerequisites for major business’ operations. Every technology on the market has multiple vulnerabilities that are exploited by hackers and cyber-criminals daily to manipulate data sometimes for malicious purposes. In any system, the Intrusion Detection System (IDS) is a fundamental component for ensuring the security of devices from digital attacks. Recognition of new developing digital threats is getting harder for existing IDS. Furthermore, advanced frameworks are required for IDS to function both efficiently and effectively. The commonly observed cyber-attacks in the business domain include minor attacks used for stealing private data. This article presents a deep learning methodology for detecting cyber-attacks on the Internet of Things using a Long Short Term Networks classifier. Our extensive experimental testing show an Accuracy of 99.09%, F1-score of 99.46%, and Recall of 99.51%, respectively. A detailed metric representing our results in tabular form was used to compare how our model was better than other state-of-the-art models in detecting cyber-attacks with proficiency.

scholarly journals A simple architecture for secure and private data sharing solutions

2014 ◽  
Author(s):  
Antonio Famulari ◽  
Francesco Longo ◽  
Giuseppe Campobello ◽  
Thomas Bonald ◽  
Marco Scarpa
Keyword(s):  
Data Sharing ◽  
Private Data

Blockchain Assisted Secure Data Sharing Model for Internet of Things Based Smart Industries

2021 ◽  
pp. 1-11
Author(s):  
Gunasekaran Manogaran ◽  
Mamoun Alazab ◽  
P. Mohamed Shakeel ◽  
Ching-Hsien Hsu
Keyword(s):  
Internet Of Things ◽  
Data Sharing ◽  
Secure Data

EdgeShare: A Blockchain-based Edge Data-sharing Framework for Industrial Internet of Things

2021 ◽  
Author(s):  
Lei Yang ◽  
Wanrong Zou ◽  
Jianggan Wang ◽  
Zi Tang
Keyword(s):  
Internet Of Things ◽  
Data Sharing ◽  
Industrial Internet Of Things ◽  
Industrial Internet

scholarly journals Safe and Private Data Sharing with Turtle: Friends Team-Up and Beat the System

2006 ◽  
pp. 213-220 ◽  
Author(s):  
Bogdan C. Popescu ◽  
Bruno Crispo ◽  
Andrew S. Tanenbaum
Keyword(s):  
Data Sharing ◽  
Private Data

scholarly journals Detection of the Hardcoded Login Information from Socket and String Compare Symbols

2021 ◽  
Vol 5 (1) ◽  
pp. 28-39
Author(s):  
Minami Yoda ◽  
Shuji Sakuraba ◽  
Yuichi Sei ◽  
Yasuyuki Tahara ◽  
Akihiko Ohsuga
Keyword(s):  
Internet Of Things ◽  
Static Analysis ◽  
Real World ◽  
Symbolic Execution ◽  
The Internet ◽  
User Input ◽  
Network Function ◽  
Private Data ◽  
String Search ◽  
Iot Devices

Internet of Things (IoT) for smart homes enhances convenience; however, it also introduces the risk of the leakage of private data. TOP10 IoT of OWASP 2018 shows that the first vulnerability is ”Weak, easy to predict, or embedded passwords.” This problem poses a risk because a user can not fix, change, or detect a password if it is embedded in firmware because only the developer of the firmware can control an update. In this study, we propose a lightweight method to detect the hardcoded username and password in IoT devices using a static analysis called Socket Search and String Search to protect from first vulnerability from 2018 OWASP TOP 10 for the IoT device. The hardcoded login information can be obtained by comparing the user input with strcmp or strncmp. Previous studies analyzed the symbols of strcmp or strncmp to detect the hardcoded login information. However, those studies required a lot of time because of the usage of complicated algorithms such as symbolic execution. To develop a lightweight algorithm, we focus on a network function, such as the socket symbol in firmware, because the IoT device is compromised when it is invaded by someone via the Internet. We propose two methods to detect the hardcoded login information: string search and socket search. In string search, the algorithm finds a function that uses the strcmp or strncmp symbol. In socket search, the algorithm finds a function that is referenced by the socket symbol. In this experiment, we measured the ability of our proposed method by searching six firmware in the real world that has a backdoor. We ran three methods: string search, socket search, and whole search to compare the two methods. As a result, all methods found login information from five of six firmware and one unexpected password. Our method reduces the analysis time. The whole search generally takes 38 mins to complete, but our methods finish the search in 4-6 min.

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