online anomaly detection
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2021 ◽  
pp. 116225
Author(s):  
Gustavo Frigo Scaranti ◽  
Luiz Fernando Carvalho ◽  
Sylvio Barbon ◽  
Jaime Lloret ◽  
Mario Lemes Proença

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7449
Author(s):  
Min-Zheng Shieh ◽  
Yi-Bing Lin ◽  
Yin-Jui Hsu

An Internet of Things (IoT) application typically involves implementations in both the device domain and the network domain. In this two-domain environment, it is possible that application developers implement the wrong network functions and/or connect some IoT devices that should never be linked, which result in the execution of wrong operations on network functions. To resolve these issues, we propose the VerificationTalk mechanism to prevent inappropriate IoT application deployment. VerificationTalk consists of two subsystems: the BigraphTalk subsystem which verifies IoT device configuration; and AFLtalk which validates the network functions. VerificationTalk provides mechanisms to conduct online anomaly detection by using a runtime monitor and offline by using American Fuzzy Lop (AFL). The runtime monitor is capable of intercepting potentially harmful data targeting IoT devices. When VerificationTalk detects errors, it provides feedback for debugging. VerificationTalk also assists in building secure IoT applications by identifying security loopholes in network applications. By the appropriate design of the IoTtalk execution engine, the testing capacity of AFLtalk is three times that of traditional AFL approaches.


2021 ◽  
pp. 826-836
Author(s):  
Nabil Belacel ◽  
René Richard ◽  
Durga Prasad Rangavajjala ◽  
Rani Adhaduk

Author(s):  
Ahmed Soliman ◽  
Sanguthevar Rajasekaran ◽  
Patrick Toman ◽  
Nalini Ravishanker ◽  
Nathan Lally ◽  
...  

2021 ◽  
Author(s):  
Keying Liu ◽  
Wentao Mao ◽  
Huadong Shi ◽  
Chao Wu ◽  
Jiaxian Chen

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