scholarly journals Novel Classification of IoT Devices Based on Traffic Flow Features

2021 ◽  
Vol 33 (6) ◽  
pp. 0-0
Keyword(s):  
Traffic Flow ◽  
Coefficient Of Variation ◽  
Flow Characteristics ◽  
Variation Method ◽  
Continuous Increase ◽  
Flow Features ◽  
Traffic Flow Characteristics ◽  
Iot Devices ◽  
Network Anomaly Detection

The concept of IoT (Internet of Things) assumes a continuous increase in the number of devices, which raises the problem of classifying them for different purposes. Based on their semantic characteristics, meaning, functionality or domain of usage, the system classes have been identified so far. This research purpose is to identify devices classes based on traffic flow characteristics such as the coefficient of variation of the received and sent data ratio. Such specified classes can combine devices based on behavior predictability and can serve as the basis for the creation of network management or network anomaly detection classification models. Four generic classes of IoT devices where defined by using the classification of the coefficient of variation method.

scholarly journals Novel Classification of IoT Devices Based on Traffic Flow Features

2021 ◽  
Vol 33 (6) ◽  
pp. 1-20
Author(s):  
Ivan Cvitić ◽  
Dragan Peraković ◽  
Marko Periša ◽  
Mirjana D. Stojanović
Keyword(s):  
Traffic Flow ◽  
Coefficient Of Variation ◽  
Flow Characteristics ◽  
Variation Method ◽  
Continuous Increase ◽  
Flow Features ◽  
Traffic Flow Characteristics ◽  
Iot Devices ◽  
Network Anomaly Detection

The concept of IoT (Internet of Things) assumes a continuous increase in the number of devices, which raises the problem of classifying them for different purposes. Based on their semantic characteristics, meaning, functionality or domain of usage, the system classes have been identified so far. This research purpose is to identify devices classes based on traffic flow characteristics such as the coefficient of variation of the received and sent data ratio. Such specified classes can combine devices based on behavior predictability and can serve as the basis for the creation of network management or network anomaly detection classification models. Four generic classes of IoT devices where defined by using the classification of the coefficient of variation method.

scholarly journals Semi-Continuous Spatial Statistical Analysis Using AIS Data for Vessel Traffic Flow Characteristics in Fairway

2021 ◽  
Vol 9 (4) ◽  
pp. 378
Author(s):  
Jong Kwan Kim
Keyword(s):  
Statistical Analysis ◽  
Traffic Flow ◽  
Flow Characteristics ◽  
Automatic Identification ◽  
Identification System ◽  
Spatial Statistical Analysis ◽  
Traffic Characteristics ◽  
Traffic Distribution ◽  
Traffic Flow Characteristics ◽  
Marine Accidents

As high vessel traffic in fairways is likely to cause frequent marine accidents, understanding vessel traffic flow characteristics is necessary to prevent marine accidents in fairways. Therefore, this study conducted semi-continuous spatial statistical analysis tests (the normal distribution test, kurtosis test and skewness test) to understand vessel traffic flow characteristics. First, a vessel traffic survey was conducted in a designated area (Busan North Port) for seven days. The data were collected using an automatic identification system and subsequently converted using semi-continuous processing methods. Thereafter, the converted data were used to conduct three methods of spatial statistical analysis. The analysis results revealed the vessel traffic distribution and its characteristics, such as the degree of use and lateral positioning on the fairway based on the size of the vessel. In addition, the generalization of the results of this study along with that of further studies will aid in deriving the traffic characteristics of vessels on the fairway. Moreover, these characteristics will reduce maritime accidents on the fairway, in addition to establishing the foundation for research on autonomous ships.

Traffic Flow Characteristics on a Three-Lane Rural Highway in Finland: Results from Empirical Studies and Simulations

1997 ◽  
Vol 1572 (1) ◽  
pp. 33-42 ◽  
Author(s):  
Åsa Enberg ◽  
Matti Pursula
Keyword(s):  
Traffic Flow ◽  
Flow Rate ◽  
Empirical Studies ◽  
Flow Characteristics ◽  
Field Studies ◽  
Optimum Length ◽  
Before And After ◽  
Traffic Flow Characteristics ◽  
Simulation Results ◽  
Rural Highway

The traffic-flow characteristics on an experimental, 20-km-long three-lane highway section in Finland were studied. The sections of highway that have a separate passing lane consist of three lanes. The central lane is assigned alternately to each direction as a passing lane with a length of 1.05 to 1.70 km. The lengths of the no-overtaking zones between successive passing lanes are 1.5 to 4.0 km. The traffic-flow characteristics on the three-lane highway have been observed by comprehensive before-and-after field studies and complementary simulations. Because it was possible to use passing lanes, the number of overtakings on the three-lane highway was remarkably higher than on the former two-lane highway. The overall average travel speeds were slightly higher, and the speed decreased a little more slowly with increasing flow on the three-lane compared with the two-lane highway. Overall platooning and mean platoon lengths decreased as a result of platoon dispersal on the passing lanes. The speeds used in the passing lanes were clearly higher than in the basic lanes. According to the simulation results, the optimum length for a single passing lane was between 0.5 and 2.5 km depending on flow rate and measure of effectiveness. For the actual three-lane highway conditions, passing lanes 1.0 to 1.5 km long seemed to bring the most benefits.

scholarly journals Traffic flow features as metrics (TFFM): detection of application layer level DDOS attack scope of IOT traffic flows

2018 ◽  
Vol 7 (2.7) ◽  
pp. 203 ◽  
Author(s):  
Kalathiripi Rambabu ◽  
N Venkatram
Keyword(s):  
Traffic Flow ◽  
Traffic Flows ◽  
Application Layer ◽  
Continuous Growth ◽  
Loosely Coupled ◽  
Ddos Attack ◽  
Proposed Model ◽  
Flow Features ◽  
Target Network ◽  
Iot Devices

The phenomenal and continuous growth of diversified IOT (Internet of Things) dependent networks has open for security and connectivity challenges. This is due to the nature of IOT devices, loosely coupled behavior of internetworking, and heterogenic structure of the networks.  These factors are highly vulnerable to traffic flow based DDOS (distributed-denial of services) attacks. The botnets such as “mirae” noticed in recent past exploits the IoT devises and tune them to flood the traffic flow such that the target network exhaust to response to benevolent requests. Hence the contribution of this manuscript proposed a novel learning-based model that learns from the traffic flow features defined to distinguish the DDOS attack prone traffic flows and benevolent traffic flows. The performance analysis was done empirically by using the synthesized traffic flows that are high in volume and source of attacks. The values obtained for statistical metrics are evincing the significance and robustness of the proposed model

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