scholarly journals Identifying IoT Devices Based on Spatial and Temporal Features from Network Traffic

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Feihong Yin ◽  
Li Yang ◽  
Jianfeng Ma ◽  
Yasheng Zhou ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Network Traffic ◽  
Network Flow ◽  
Data Augmentation ◽  
Accurate Identification ◽  
Device Identification ◽  
Data Packets ◽  
Temporal Features ◽  
Communication Traffic ◽  
Iot Devices ◽  
The One

With the rapid growth of the Internet of Things (IoT) devices, security risks have also arisen. The preidentification of IoT devices connected to the network can help administrators to set corresponding security policies according to the functionality and heterogeneity of the devices. However, the existing methods are based on manually extracted features and prior knowledge to identify the IoT devices, which increases the difficulty of the device identification task and reduces the timeliness. In this paper, we present CBBI, a novel IoT device identification approach. On the one hand, CBBI uses a hybrid neural network model Conv-BiLSTM to automatically learn the representative spatial and temporal features from the network traffic, such as the position relationship of the internal organization structure in network communication traffic, the time sequence of the data packets, and the duration of the network flow. On the other hand, CBBI contains the data augmentation module FGAN that solves the problem of data imbalance in deep learning and improves the accuracy of the model. Finally, we used the public dataset and laboratory dataset to evaluate CBBI from multiple dimensions. The evaluation results for different datasets show that our approach achieves the accurate identification of IoT devices.

A Virtual Machine Migration Algorithm Based on Network Flow Balance

2014 ◽  
Vol 1044-1045 ◽  
pp. 1028-1034
Author(s):  
Yu Yang ◽  
Hua Zhou ◽  
Jun Hui Liu ◽  
Yun Feng
Keyword(s):  
Virtual Machine ◽  
Network Traffic ◽  
Network Flow ◽  
Virtual Machine Migration ◽  
Network Resources ◽  
Migration Strategy ◽  
Network Location ◽  
Flow Balance ◽  
The Difference ◽  
Communication Traffic

Current research of virtual machine migration strategy mainly focuses on how to reduce the delay of virtual machine migration process but does not pay much attention to the network flow problem caused by the virtual machine migration. Because of the difference caused by Infrastructure Operator's network location makes a different virtual machine migration strategy, which will result in large differences in network traffic. Infrastructure operator's network resources are scarce resources. Therefore, how to reduce the network flow of virtual machine migration is a problem to be studied. In order to reduce network traffic virtual machine migration, this paper proposes a virtual machine migration algorithm (NFBA) based on network flow balance to obtain the minimum scheduling cost. Experimental results show the migration strategy can effectively reduce the communication traffic between the virtual machine clusters within the system and reduce the burden of network and consider workload balance at the same time.

scholarly journals IoT Botnet Detection Using Various One-Class Classifiers

2020 ◽  
pp. 1-20
Author(s):  
Mehedi Hasan Raj ◽  
A. N. M. Asifur Rahman ◽  
Umma Habiba Akter ◽  
Khayrun Nahar Riya ◽  
Anika Tasneem Nijhum ◽  
...  
Keyword(s):  
Network Flow ◽  
Cost Effective ◽  
Support Vector ◽  
Good Precision ◽  
Accuracy Score ◽  
Botnet Detection ◽  
The People ◽  
Iot Devices ◽  
Set Up ◽  
The One

Nowadays, the Internet of Things (IoT) is a common word for the people because of its increasing number of users. Statistical results show that the users of IoT devices are dramatically increasing, and in the future, it will be to an ever-increasing extent. Because of the increasing number of users, security experts are now concerned about its security. In this research, we would like to improve the security system of IoT devices, particularly in IoT botnet, by applying various machine learning (ML) techniques. In this paper, we have set up an approach to detect botnet of IoT devices using three one-class classifier ML algorithms. The algorithms are: one-class support vector machine (OCSVM), elliptic envelope (EE), and local outlier factor (LOF). Our method is a network flow-based botnet detection technique, and we use the input packet, protocol, source port, destination port, and time as features of our algorithms. After a number of preprocessing steps, we feed the preprocessed data to our algorithms that can achieve a good precision score that is approximately 77–99%. The one-class SVM achieves the best accuracy score, approximately 99% in every dataset, and EE’s accuracy score varies from 91% to 98%; however, the LOF factor achieves lowest accuracy score that is from 77% to 99%. Our algorithms are cost-effective and provide good accuracy in short execution time.

scholarly journals Class-Incremental Learning for Wireless Device Identification in IoT

2021 ◽  
Author(s):  
Yongxin Liu ◽  
Jian Wang ◽  
Jianqiang Li ◽  
Shuteng Niu ◽  
Houbing Song
Keyword(s):  
Incremental Learning ◽  
Historical Data ◽  
Real Data ◽  
Accurate Identification ◽  
Potential Conflict ◽  
Typical Application ◽  
Device Identification ◽  
Iot Devices ◽  
New Perspective ◽  
Channel Separation

<div>Deep Learning (DL) has been utilized pervasively in the Internet of Things (IoT). One typical application of DL in IoT is device identification from wireless signals, namely Non-cryptographic Device Identification (NDI). However, learning components in NDI systems have to evolve to adapt to operational variations, such a paradigm is termed as Incremental Learning (IL). Various IL algorithms have been proposed and many of them require dedicated space to store the increasing amount of historical data, and therefore, they are not suitable for IoT or mobile applications. However, conventional IL schemes can not provide satisfying performance when historical data are not available. In this paper, we address the IL problem in NDI from a new perspective, firstly, we provide a new metric to measure the degree of topological maturity of DNN models from the degree of conflict of class-specific fingerprints. We discover that an important cause for performance degradation in IL enabled NDI is owing to the conflict of devices' fingerprints. Second, we also show that the conventional IL schemes can lead to low topological maturity of DNN models in NDI systems. Thirdly, we propose a new Channel Separation Enabled Incremental Learning (CSIL) scheme without using historical data, in which our strategy can automatically separate devices' fingerprints in different learning stages and avoid potential conflict. Finally, We evaluated the effectiveness of the proposed framework using real data from ADS-B (Automatic Dependent Surveillance-Broadcast), an application of IoT in aviation. The proposed framework has the potential to be applied to accurate identification of IoT devices in a variety of IoT applications and services. Data and code available at IEEE Dataport (DOI: 10.21227/1bxc-ke87) and \url{https://github.com/pcwhy/CSIL}}.<br></div>

scholarly journals A MACHINE LEARNING FRAMEWORK BASED ON VARIOUS NETWORK TRAFFIC CHARACTERISTICS TO IDENTIFY AND CLASSIFY THE DEFAULT BEHAVIOR OF IOT DEVICES ON A NETWORK

2021 ◽  
Vol 6 (5) ◽  
Author(s):  
Pagalla Bhavani Shankar ◽  
Yogi Reddy Maramreddy ◽  
Padala S Venkata Durga Gayatri
Keyword(s):  
Machine Learning ◽  
Network Traffic ◽  
Network Flow ◽  
New Technologies ◽  
Smart Cities ◽  
Smart Environments ◽  
Traffic Characteristics ◽  
Trade Offs ◽  
Iot Devices ◽  
Robust Framework

The Internet of Things (IoT) is being well acquire to the next era of revolutionary generations amongst the new technologies. IoT technology being hailed so hard we had to stop in our society, smart homes, enterprises, and smart cities. Dynamics of smart one’s are increasingly being equipped with a profusion of IoT devices. Due to the tremendous upgradation of knowledge in various aspects impresarios of such smart environments may not even be fully aware of their working nature or principles of IoT devices, assets and functioning properly safe from cyberattacks. In this paper, we addressing this challenge by developing a robust framework for IoT device classification using traffic characteristics obtained at the level of network level. As a part of robust framework, firstly, we have a tendency to instrument a smart environment with 28 completely different IoT devices, spanning cameras, lights, plugs, motion sensors, appliances and health-monitors. We have a tendency to collect and synthesize traffic traces from this framework infrastructure for a period of 6 months, a type of subset of which we release as open data for the community to use. Second, we have to present or gifts the insights into the underlying network traffic characteristics using statistical and applied mathematical attributes such as activity cycles, port numbers, signaling patterns and cipher suites. Third, we have a tendency to develop a multi-stage machine learning based classification algorithm and demonstrate its ability to identify specific IoT devices with over 99% accuracy based on their network flow of activity. Finally, we have a tendency to discuss the trade-offs between cost, speed, and performance involved in deploying the classification network framework in real-time. Our study paves the way for impresarios of smart environments to monitor their IoT devices and assets for presence, functionality, and cyber-security without requiring any specialized devices or protocols.

scholarly journals Class-Incremental Learning for Wireless Device Identification in IoT

2021 ◽  
Author(s):  
Yongxin Liu ◽  
Jian Wang ◽  
Jianqiang Li ◽  
Shuteng Niu ◽  
Houbing Song
Keyword(s):  
Incremental Learning ◽  
Historical Data ◽  
Real Data ◽  
Accurate Identification ◽  
Potential Conflict ◽  
Typical Application ◽  
Device Identification ◽  
Iot Devices ◽  
New Perspective ◽  
Channel Separation

<div>Deep Learning (DL) has been utilized pervasively in the Internet of Things (IoT). One typical application of DL in IoT is device identification from wireless signals, namely Non-cryptographic Device Identification (NDI). However, learning components in NDI systems have to evolve to adapt to operational variations, such a paradigm is termed as Incremental Learning (IL). Various IL algorithms have been proposed and many of them require dedicated space to store the increasing amount of historical data, and therefore, they are not suitable for IoT or mobile applications. However, conventional IL schemes can not provide satisfying performance when historical data are not available. In this paper, we address the IL problem in NDI from a new perspective, firstly, we provide a new metric to measure the degree of topological maturity of DNN models from the degree of conflict of class-specific fingerprints. We discover that an important cause for performance degradation in IL enabled NDI is owing to the conflict of devices' fingerprints. Second, we also show that the conventional IL schemes can lead to low topological maturity of DNN models in NDI systems. Thirdly, we propose a new Channel Separation Enabled Incremental Learning (CSIL) scheme without using historical data, in which our strategy can automatically separate devices' fingerprints in different learning stages and avoid potential conflict. Finally, We evaluated the effectiveness of the proposed framework using real data from ADS-B (Automatic Dependent Surveillance-Broadcast), an application of IoT in aviation. The proposed framework has the potential to be applied to accurate identification of IoT devices in a variety of IoT applications and services. Data and code available at IEEE Dataport (DOI: 10.21227/1bxc-ke87) and \url{https://github.com/pcwhy/CSIL}}.<br></div>

scholarly journals Spectrum Based Power Management for Congested IoT Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2681
Author(s):  
Kedir Mamo Besher ◽  
Juan Ivan Nieto-Hipolito ◽  
Raymundo Buenrostro-Mariscal ◽  
Mohammed Zamshed Ali
Keyword(s):  
Power Management ◽  
Network Performance ◽  
Channel Allocation ◽  
Battery Life ◽  
Packet Routing ◽  
Data Packets ◽  
Increasing Demand ◽  
Iot Devices ◽  
Set Up

With constantly increasing demand in connected society Internet of Things (IoT) network is frequently becoming congested. IoT sensor devices lose more power while transmitting data through congested IoT networks. Currently, in most scenarios, the distributed IoT devices in use have no effective spectrum based power management, and have no guarantee of a long term battery life while transmitting data through congested IoT networks. This puts user information at risk, which could lead to loss of important information in communication. In this paper, we studied the extra power consumed due to retransmission of IoT data packet and bad communication channel management in a congested IoT network. We propose a spectrum based power management solution that scans channel conditions when needed and utilizes the lowest congested channel for IoT packet routing. It also effectively measured power consumed in idle, connected, paging and synchronization status of a standard IoT device in a congested IoT network. In our proposed solution, a Freescale Freedom Development Board (FREDEVPLA) is used for managing channel related parameters. While supervising the congestion level and coordinating channel allocation at the FREDEVPLA level, our system configures MAC and Physical layer of IoT devices such that it provides the outstanding power utilization based on the operating network in connected mode compared to the basic IoT standard. A model has been set up and tested using freescale launchpads. Test data show that battery life of IoT devices using proposed spectrum based power management increases by at least 30% more than non-spectrum based power management methods embedded within IoT devices itself. Finally, we compared our results with the basic IoT standard, IEEE802.15.4. Furthermore, the proposed system saves lot of memory for IoT devices, improves overall IoT network performance, and above all, decrease the risk of losing data packets in communication. The detail analysis in this paper also opens up multiple avenues for further research in future use of channel scanning by FREDEVPLA board.

scholarly journals Packet-level and IEEE 802.11 MAC frame-level Network Traffic Traces Data of the D-Link IoT devices

Data in Brief ◽  
2021 ◽  
pp. 107208
Author(s):  
Rajarshi Roy Chowdhury ◽  
Sandhya Aneja ◽  
Nagender Aneja ◽  
Pg Emeroylariffion Abas
Keyword(s):  
Ieee 802.11 ◽  
Network Traffic ◽  
Ieee 802.11 Mac ◽  
Iot Devices ◽  
802.11 Mac

scholarly journals A Hybrid Optimized LSTM Models for Human Activity Recognition with IOT Devices

2021 ◽  
pp. 182-189
Author(s):  
S. Arokiaraj ◽  
Dr. N. Viswanathan
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Short Term Memory ◽  
The Other ◽  
Learning Models ◽  
Computational Overhead ◽  
Temporal Features ◽  
Human Movements ◽  
Proposed Model ◽  
Iot Devices

With the advent of Internet of things(IoT),HA (HA) recognition has contributed the more application in health care in terms of diagnosis and Clinical process. These devices must be aware of human movements to provide better aid in the clinical applications as well as user’s daily activity.Also , In addition to machine and deep learning algorithms, HA recognition systems has significantly improved in terms of high accurate recognition. However, the most of the existing models designed needs improvisation in terms of accuracy and computational overhead. In this research paper, we proposed a BAT optimized Long Short term Memory (BAT-LSTM) for an effective recognition of human activities using real time IoT systems. The data are collected by implanting the Internet of things) devices invasively. Then, proposed BAT-LSTM is deployed to extract the temporal features which are then used for classification to HA. Nearly 10,0000 dataset were collected and used for evaluating the proposed model. For the validation of proposed framework, accuracy, precision, recall, specificity and F1-score parameters are chosen and comparison is done with the other state-of-art deep learning models. The finding shows the proposed model outperforms the other learning models and finds its suitability for the HA recognition.

Classifying IoT Devices in Smart Environments Using Network Traffic Characteristics

2019 ◽  
Vol 18 (8) ◽  
pp. 1745-1759 ◽  
Author(s):  
Arunan Sivanathan ◽  
Hassan Habibi Gharakheili ◽  
Franco Loi ◽  
Adam Radford ◽  
Chamith Wijenayake ◽  
...  
Keyword(s):  
Network Traffic ◽  
Smart Environments ◽  
Traffic Characteristics ◽  
Iot Devices

Vehicle to Cloud

2016 ◽  
pp. 182-201
Author(s):  
Alper Ozpinar ◽  
Serhan Yarkan
Keyword(s):  
Big Data ◽  
Environmental Sustainability ◽  
Traffic Management ◽  
Human Beings ◽  
Standard Equipment ◽  
Political Views ◽  
Iot Devices ◽  
The One ◽  
Social Applications ◽  
Use Of Internet

The population of humanity has become more than seven billion. Daily used devices, machines, and equipment, are also increasing quicker than the human population. The number of mobile devices in use like phones, tablets and IoT devices already passed the two billion barrier and even more than one billion as vehicles are also on the roads. Combining these two will make the one of the biggest Big Data Environment about the daily life of human beings after the use of internet and social applications. For the newly manufactured vehicles, internet operated entertainment and information Systems are becoming a standard equipment delivering such an information to the manufacturers but most of the current vehicles do not have a system like that. This chapter explains the combined version of IoT and vehicles to create a V2C vehicle to cloud system that will create the big data for environmental sustainability, energy and traffic management by different technical and political views and aspects.

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