Traffic Data Classification for Security in IoT-Based Road Signaling System

2018 ◽  
pp. 589-599 ◽  
Author(s):  
Srijanee Mookherji ◽  
Suresh Sankaranarayanan
Keyword(s):  
Data Classification ◽  
Traffic Data ◽  
Signaling System

Traffic Data Classification for Police Activity

2018 ◽  
pp. 169-178
Author(s):  
Stefano Guarino ◽  
Fabio Leuzzi ◽  
Flavio Lombardi ◽  
Enrico Mastrostefano
Keyword(s):  
Data Classification ◽  
Traffic Data ◽  
Police Activity

Online Learning for Network Traffic Data Classification

2021 ◽  
Author(s):  
Wei-Chen Hsi ◽  
Chung-Hao Wu ◽  
Henry Horng-Shing Lu
Keyword(s):  
Online Learning ◽  
Network Traffic ◽  
Data Classification ◽  
Traffic Data

SVM-Based Traffic Data Classification for Secured IoT-Based Road Signaling System

2019 ◽  
Vol 15 (1) ◽  
pp. 22-50 ◽  
Author(s):  
Suresh Sankaranarayanan ◽  
Srijanee Mookherji
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Security Protocols ◽  
Heterogeneous Data ◽  
Machine Learning Algorithm ◽  
Traffic Data ◽  
Signaling System ◽  
Data Set ◽  
Signaling Systems ◽  
Systems Research

The traffic controlling systems at present are microcontroller-based, which is semi-automatic in nature where time is the only parameter that is considered. With the introduction of IoT in traffic signaling systems, research is being done considering density as a parameter for automating the traffic signaling system and regulate traffic dynamically. Security is a concern when sensitive data of great volume is being transmitted wirelessly. Security protocols that have been implemented for IoT networks can protect the system against attacks and are purely based on standard cryptosystem. They cannot handle heterogeneous data type. To prevent the issues on security protocols, the authors have implemented SVM machine learning algorithm for analyzing the traffic data pattern and detect anomalies. The SVM implementation has been done for the UK traffic data set between 2011-2016 for three cities. The implementation been carried out in Raspberry Pi3 processor functioning as an edge router and SVM machine learning algorithm using Python Scikit Libraries.

SVM-Based Traffic Data Classification for Secured IoT-Based Road Signaling System

2021 ◽  
pp. 1003-1030
Author(s):  
Suresh Sankaranarayanan ◽  
Srijanee Mookherji
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Security Protocols ◽  
Heterogeneous Data ◽  
Machine Learning Algorithm ◽  
Traffic Data ◽  
Signaling System ◽  
Sensitive Data ◽  
Signaling Systems ◽  
Edge Router

The traffic controlling systems at present are microcontroller-based, which is semi-automatic in nature where time is the only parameter that is considered. With the introduction of IoT in traffic signaling systems, research is being done considering density as a parameter for automating the traffic signaling system and regulate traffic dynamically. Security is a concern when sensitive data of great volume is being transmitted wirelessly. Security protocols that have been implemented for IoT networks can protect the system against attacks and are purely based on standard cryptosystem. They cannot handle heterogeneous data type. To prevent the issues on security protocols, the authors have implemented SVM machine learning algorithm for analyzing the traffic data pattern and detect anomalies. The SVM implementation has been done for the UK traffic data set between 2011-2016 for three cities. The implementation been carried out in Raspberry Pi3 processor functioning as an edge router and SVM machine learning algorithm using Python Scikit Libraries.

scholarly journals Methods for Traffic Data Classification with regard to Potential Safety Hazards

2021 ◽  
Vol 54 (7) ◽  
pp. 250-255
Author(s):  
Gunda Obereigner ◽  
Pavlo Tkachenko ◽  
Luigi del Re
Keyword(s):  
Data Classification ◽  
Traffic Data ◽  
Safety Hazards ◽  
Potential Safety

Privacy: Users of Pharmaceutical Websites

2002 ◽  
Vol 30 (3) ◽  
pp. 466-474
Keyword(s):  
Personal Information ◽  
District Court ◽  
Traffic Data ◽  
Web Traffic ◽  
Electronic Communications ◽  
Private Action

In In re Pharmatrak, Inc. Privacy Litigation, website users brought suit claiming that major pharmaceutical corporations and a web monitoring company violated three federal statutes protecting electronic communications and data by collecting web traffic data and personal information about website users. On August 13,2002, the District Court of Massachusetts dismissed these allegations, holding that the defendants were parties to the communications and thus exempted under the statutory language.The court also found that plaintiffs had not suffered an amount of damages required to sustain private action.

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