WiFi-Enabled User Authentication through Deep Learning in Daily Activities

2021 ◽  
Vol 2 (2) ◽  
pp. 1-25
Author(s):  
Cong Shi ◽  
Jian Liu ◽  
Hongbo Liu ◽  
Yingying Chen
Keyword(s):  
Deep Learning ◽  
User Authentication ◽  
Smart Cities ◽  
Security And Privacy ◽  
Daily Activities ◽  
Indoor Environments ◽  
Home Environments ◽  
Training Cost ◽  
Iot Devices ◽  
Smart Thermostat

User authentication is a critical process in both corporate and home environments due to the ever-growing security and privacy concerns. With the advancement of smart cities and home environments, the concept of user authentication is evolved with a broader implication by not only preventing unauthorized users from accessing confidential information but also providing the opportunities for customized services corresponding to a specific user. Traditional approaches of user authentication either require specialized device installation or inconvenient wearable sensor attachment. This article supports the extended concept of user authentication with a device-free approach by leveraging the prevalent WiFi signals made available by IoT devices, such as smart refrigerator, smart TV, and smart thermostat, and so on. The proposed system utilizes the WiFi signals to capture unique human physiological and behavioral characteristics inherited from their daily activities, including both walking and stationary ones. Particularly, we extract representative features from channel state information (CSI) measurements of WiFi signals, and develop a deep-learning-based user authentication scheme to accurately identify each individual user. To mitigate the signal distortion caused by surrounding people’s movements, our deep learning model exploits a CNN-based architecture that constructively combines features from multiple receiving antennas and derives more reliable feature abstractions. Furthermore, a transfer-learning-based mechanism is developed to reduce the training cost for new users and environments. Extensive experiments in various indoor environments are conducted to demonstrate the effectiveness of the proposed authentication system. In particular, our system can achieve over 94% authentication accuracy with 11 subjects through different activities.

scholarly journals Towards Secure and Privacy-Preserving IoT Enabled Smart Home: Architecture and Experimental Study

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6131
Author(s):  
Mamun Abu-Tair ◽  
Soufiene Djahel ◽  
Philip Perry ◽  
Bryan Scotney ◽  
Unsub Zia ◽  
...  
Keyword(s):  
Experimental Study ◽  
Smart Home ◽  
Privacy Preservation ◽  
Smart Cities ◽  
Security And Privacy ◽  
Test Cases ◽  
Security Threats ◽  
Hardware Implementations ◽  
Cryptographic Algorithms ◽  
Iot Devices

Internet of Things (IoT) technology is increasingly pervasive in all aspects of our life and its usage is anticipated to significantly increase in future Smart Cities to support their myriad of revolutionary applications. This paper introduces a new architecture that can support several IoT-enabled smart home use cases, with a specified level of security and privacy preservation. The security threats that may target such an architecture are highlighted along with the cryptographic algorithms that can prevent them. An experimental study is performed to provide more insights about the suitability of several lightweight cryptographic algorithms for use in securing the constrained IoT devices used in the proposed architecture. The obtained results showed that many modern lightweight symmetric cryptography algorithms, as CLEFIA and TRIVIUM, are optimized for hardware implementations and can consume up to 10 times more energy than the legacy techniques when they are implemented in software. Moreover, the experiments results highlight that CLEFIA significantly outperforms TRIVIUM under all of the investigated test cases, and the latter performs 100 times worse than the legacy cryptographic algorithms tested.

scholarly journals Lightweight MIPv6 with IPSec Support

2014 ◽  
Vol 10 (1) ◽  
pp. 37-77 ◽  
Author(s):  
Antonio J. Jara ◽  
David Fernandez ◽  
Pablo Lopez ◽  
Miguel A. Zamora ◽  
Antonio F. Skarmeta
Keyword(s):  
Smart Cities ◽  
Mobile Ipv6 ◽  
Security And Privacy ◽  
Mobility Support ◽  
Memory Processing ◽  
Constrained Environments ◽  
Iot Devices ◽  
The One ◽  
New Generation ◽  
Constrained Devices

Mobility management is a desired feature for the emerging Internet of Things (IoT). Mobility aware solutions increase the connectivity and enhance adaptability to changes of the location and infrastructure. IoT is enabling a new generation of dynamic ecosystems in environments such as smart cities and hospitals. Dynamic ecosystems require ubiquitous access to Internet, seamless handover, flexible roaming policies, and an interoperable mobility protocol with existing Internet infrastructure. These features are challenges for IoT devices, which are usually constrained devices with low memory, processing, communication and energy capabilities. This work presents an analysis of the requirements and desirable features for the mobility support in the IoT, and proposes an efficient solution for constrained environments based on Mobile IPv6 and IPSec. Compatibility with IPv6-existing protocols has been considered a major requirement in order to offer scalable and inter-domain solutions that were not limited to specific application domains in order to enable a new generation of application and services over Internet-enabled dynamic ecosystems, and security support based on IPSec has been also considered, since dynamic ecosystems present several challenges in terms of security and privacy. This work has, on the one hand, analysed suitability of Mobile IPv6 and IPSec for constrained devices, and on the other hand, analysed, designed, developed and evaluated a lightweight version of Mobile IPv6 and IPSec. The proposed solution of lightweight Mobile IPv6 with IPSec is aware of the requirements of the IoT and presents the best solution for dynamic ecosystems in terms of efficiency and security adapted to IoT-devices capabilities. This presents concerns in terms of higher overhead and memory requirements. But, it is proofed and concluded that even when higher memory is required and major overhead is presented, the integration of Mobile IPv6 and IPSec for constrained devices is feasible.

scholarly journals The Future of Emerging IoT Paradigms: Architectures and Technologies

2019 ◽  
Author(s):  
Abdul Salam ◽  
Anh Duy Hoang ◽  
Atluri Meghna ◽  
Dylan R. Martin ◽  
Gabriel Guzman ◽  
...  
Keyword(s):  
Vehicular Networks ◽  
New Technologies ◽  
Smart Cities ◽  
Wireless Systems ◽  
Power Grids ◽  
Control Power ◽  
The Future ◽  
Iot Devices ◽  
Smart Thermostat ◽  
Communication Methods

With Internet of Things (IoT) gaining presence throughout different industries a lot of new technologies have been introduced to support this undertaking. Implications on one such technology, wireless systems allowed for the use of different communication methods to achieve the goal of transferring data reliably, with more cost efficiency and over longer distances. Anywhere from a single house with only a few IoT devices such as a smart light bulb or a smart thermostat connected to the network, all the way to a complex system that can control power grids throughout countries, IoT has been becoming a necessity in everyday lives. This paper presents an overview of the devices, systems and wireless technologies used in different IoT architectures (Healthcare, Vehicular Networks, Mining, Learning, Energy, Smart Cities, Behaviors and Decision Making), their upbringings and challenges to this date and some foreseen in the future.

scholarly journals Gait-Based Implicit Authentication Using Edge Computing and Deep Learning for Mobile Devices

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4592
Author(s):  
Xin Zeng ◽  
Xiaomei Zhang ◽  
Shuqun Yang ◽  
Zhicai Shi ◽  
Chihung Chi
Keyword(s):  
Deep Learning ◽  
Mobile Devices ◽  
User Authentication ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Edge Computing ◽  
Behavior Modeling ◽  
Security And Privacy ◽  
Positive Rate ◽  
Edge Based

Implicit authentication mechanisms are expected to prevent security and privacy threats for mobile devices using behavior modeling. However, recently, researchers have demonstrated that the performance of behavioral biometrics is insufficiently accurate. Furthermore, the unique characteristics of mobile devices, such as limited storage and energy, make it subject to constrained capacity of data collection and processing. In this paper, we propose an implicit authentication architecture based on edge computing, coined Edge computing-based mobile Device Implicit Authentication (EDIA), which exploits edge-based gait biometric identification using a deep learning model to authenticate users. The gait data captured by a device’s accelerometer and gyroscope sensors is utilized as the input of our optimized model, which consists of a CNN and a LSTM in tandem. Especially, we deal with extracting the features of gait signal in a two-dimensional domain through converting the original signal into an image, and then input it into our network. In addition, to reduce computation overhead of mobile devices, the model for implicit authentication is generated on the cloud server, and the user authentication process also takes place on the edge devices. We evaluate the performance of EDIA under different scenarios where the results show that i) we achieve a true positive rate of 97.77% and also a 2% false positive rate; and ii) EDIA still reaches high accuracy with limited dataset size.

scholarly journals Deep Learning-Based Security Behaviour Analysis in IoT Environments: A Survey

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yawei Yue ◽  
Shancang Li ◽  
Phil Legg ◽  
Fuzhong Li
Keyword(s):  
Deep Learning ◽  
Smart Home ◽  
Ad Hoc ◽  
Security Analysis ◽  
Home Healthcare ◽  
Security And Privacy ◽  
Iot Security ◽  
Privacy Concerns ◽  
Iot Devices ◽  
Primary Focus

Internet of Things (IoT) applications have been used in a wide variety of domains ranging from smart home, healthcare, smart energy, and Industrial 4.0. While IoT brings a number of benefits including convenience and efficiency, it also introduces a number of emerging threats. The number of IoT devices that may be connected, along with the ad hoc nature of such systems, often exacerbates the situation. Security and privacy have emerged as significant challenges for managing IoT. Recent work has demonstrated that deep learning algorithms are very efficient for conducting security analysis of IoT systems and have many advantages compared with the other methods. This paper aims to provide a thorough survey related to deep learning applications in IoT for security and privacy concerns. Our primary focus is on deep learning enhanced IoT security. First, from the view of system architecture and the methodologies used, we investigate applications of deep learning in IoT security. Second, from the security perspective of IoT systems, we analyse the suitability of deep learning to improve security. Finally, we evaluate the performance of deep learning in IoT system security.

scholarly journals Deep Learning Approaches for Continuous Authentication Based on Activity Patterns Using Mobile Sensing

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7519
Author(s):  
Sakorn Mekruksavanich ◽  
Anuchit Jitpattanakul
Keyword(s):  
Deep Learning ◽  
Credit Card ◽  
User Authentication ◽  
Binary Classification ◽  
Activity Patterns ◽  
Personal Data ◽  
Security And Privacy ◽  
Sensor Data ◽  
Learning Approaches ◽  
Continuous Authentication

Smartphones as ubiquitous gadgets are rapidly becoming more intelligent and context-aware as sensing, networking, and processing capabilities advance. These devices provide users with a comprehensive platform to undertake activities such as socializing, communicating, sending and receiving e-mails, and storing and accessing personal data at any time and from any location. Nowadays, smartphones are used to store a multitude of private and sensitive data including bank account information, personal identifiers, account passwords and credit card information. Many users remain permanently signed in and, as a result, their mobile devices are vulnerable to security and privacy risks through assaults by criminals. Passcodes, PINs, pattern locks, facial verification, and fingerprint scans are all susceptible to various assaults including smudge attacks, side-channel attacks, and shoulder-surfing attacks. To solve these issues, this research introduces a new continuous authentication framework called DeepAuthen, which identifies smartphone users based on their physical activity patterns as measured by the accelerometer, gyroscope, and magnetometer sensors on their smartphone. We conducted a series of tests on user authentication using several deep learning classifiers, including our proposed deep learning network termed DeepConvLSTM on the three benchmark datasets UCI-HAR, WISDM-HARB and HMOG. Results demonstrated that combining various motion sensor data obtained the highest accuracy and energy efficiency ratio (EER) values for binary classification. We also conducted a thorough examination of the continuous authentication outcomes, and the results supported the efficacy of our framework.

scholarly journals Analyzing and Evaluating Smart Cities for IoT Based on Use Cases Using the Analytic Network Process

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Chao Huang ◽  
Shah Nazir
Keyword(s):  
Smart City ◽  
Smart Cities ◽  
Information Leakage ◽  
Analytic Network Process ◽  
Use Cases ◽  
Security And Privacy ◽  
World Population ◽  
Network Process ◽  
Iot Devices ◽  
And Control

With the passage of time, the world population is growing. Proper utilization of resources and other devices is tremendously playing an important role to easily examine, manage, and control the resources of the Internet of Things (IoT) in the smart city. Research in the field of IoT has revolutionized the services mostly in smart cities. In the smart city, the applications of IoT are utilized without human involvement. Diverse IoT devices are connected with each other and communicate for different tasks. With the existence of a huge number of IoT devices in the forthcoming years, the chances of privacy breach and information leakage are increasing. Billions of devices connected on IoT producing huge volume of data bound to cloud for processing, management, and storage. Sending of whole data to the cloud might create risk of security and privacy. Various needs of the smart city should be considered for both urgent and effective solutions to support requirements of the growing population. On the other side of rising technology, the IoT evolution has massively produced diverse research directions for the smart city. Keeping in view the use cases of the smart city, the proposed study presents the analytic network process (ANP) for evaluating smart cities. The approach of ANP works well in the situation of complexity, and vagueness exists among the available alternatives. The experimental results of the planned approach show that the approach is effective for evaluating the smart cities for IoT based on the use cases.

scholarly journals Internet of Things (IoT) Authentication and Access Control by Hybrid Deep Learning Method - A Study

2021 ◽  
Vol 2 (4) ◽  
pp. 236-245
Author(s):  
Joy Iong Zong Chen ◽  
Kong-Long Lai
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Control Device ◽  
Security And Privacy ◽  
The Real ◽  
Intelligent Technique ◽  
History Of ◽  
Iot Devices ◽  
Future Direction

In the history of device computing, Internet of Things (IoT) is one of the fastest growing field that facing many security challenges. The effective efforts should have been made to address the security and privacy issues in IoT networks. The IoT devices are basically resource control device which provide routine attract impression for cyber attackers. The IoT participation nodes are increasing rapidly with more resource constrained that creating more challenging conditions in the real time. The existing methods provide an ineffective response to the tasks for effective IoT device. Also, it is an insufficient to involve the complete security and safety spectrum of the IoT networks. Because of the existing algorithms are not enriched to secure IoT bionetwork in the real time environment. The existing system is not enough to detect the proxy to the authorized person in the embedding devices. Also, those methods are believed in single model domain. Therefore, the effectiveness is dropping for further multimodal domain such as combination of behavioral and physiological features. The embedding intelligent technique will be securitizing for the IoT devices and networks by deep learning (DL) techniques. The DL method is addressing different security and safety problems arise in real time environment. This paper is highlighting hybrid DL techniques with Reinforcement Learning (RL) for the better performance during attack and compared with existing one. Also, here we discussed about DL combined with RL of several techniques and identify the higher accuracy algorithm for security solutions. Finally, we discuss the future direction of decision making of DL based IoT security system.

A Comprehensive Study on Internet of Things Security

2019 ◽  
pp. 72-86 ◽  
Author(s):  
Manikandakumar Muthusamy ◽  
Karthikeyan Periasamy
Keyword(s):  
Internet Of Things ◽  
Smart Cities ◽  
Denial Of Service ◽  
Critical Issue ◽  
Security And Privacy ◽  
The Internet ◽  
Privacy Concerns ◽  
Major Shift ◽  
Iot Devices ◽  
High Level

Internet of things is a growing technology with many business opportunities and risks. It is strongly believed that IoT will cause a major shift in people's lives similar to how the internet transformed the way people communicate and share information. IoT is becoming popular in the various domains such as smart health, smart cities, smart transport, and smart retail. The security and privacy concerns of IoT are crucial as it connects a large number of devices. Security is a more critical issue that certainly needs to be resolved with a high level of attention, as with an increasing number of users, there would be a need to manage their requests and check authenticity on the cloud-based pattern. Recently, a series of massive distributed denial-of-service attacks have occurred in IoT organizations. Such malicious attacks have highlighted the threats resulting from not enough security in IoT devices together with their overwhelming effects on the internet. This chapter provides an overview of the security attacks with regard to IoT technologies, protocols, and applications.

The Internet of Things

2017 ◽  
pp. 167-187 ◽  
Author(s):  
Edward T. Chen
Keyword(s):  
Internet Of Things ◽  
Smart Cities ◽  
Security And Privacy ◽  
The Internet ◽  
New Wave ◽  
Smart Health ◽  
Smart Living ◽  
Iot Devices ◽  
The Internet Of Things

The Internet of Things (IoT) has the potential to increase quality of life, heighten performance of systems and processes, and save valuable time for businesses and people. Common objects and devices are being linked with Internet connectivity and have capabilities for data analytics that affect day-to-day experiences of both individuals and businesses. The notions of Smart Health, Smart Cities, and Smart Living come into play as the Internet of Things plays a role in today's world. This chapter presents IoT devices and application examples as well as descriptions of the benefits and limitations alongside an assessment of each respective technology's potential for success in the future. Security and privacy are important factors that need to be addressed within the different domains. This chapter addresses these potentials, issues, and challenges for managers to be prepared for the new wave brought forth by the IoT.

Sign in / Sign up

Export Citation Format

Share Document