scholarly journals Hardening Machine Learning Denial of Service (DoS) Defences Against Adversarial Attacks in IoT Smart Home Networks

2021 ◽  
pp. 102352
Author(s):  
Eirini Anthi ◽  
Lowri Williams ◽  
Amir Javed ◽  
Pete Burnap
Keyword(s):  
Machine Learning ◽  
Smart Home ◽  
Denial Of Service ◽  
Home Networks

A Machine Learning Approach for Blockchain-Based Smart Home Networks Security

IEEE Network ◽  
2020 ◽  
pp. 1-7
Author(s):  
Muhammad Adnan Khan ◽  
Sagheer Abbas ◽  
Abdur Rehman ◽  
Yousaf Saeed ◽  
Asim Zeb ◽  
...  
Keyword(s):  
Machine Learning ◽  
Smart Home ◽  
Learning Approach ◽  
Home Networks ◽  
Machine Learning Approach

A flexible authentication scheme for smart home networks using app interactions and machine learning

2020 ◽  
Vol 39 (5) ◽  
pp. 6009-6020
Author(s):  
Yosef Ashibani ◽  
Qusay H. Mahmoud
Keyword(s):  
Machine Learning ◽  
Smart Home ◽  
User Authentication ◽  
Authentication Scheme ◽  
Home Networks ◽  
Flexible Machine ◽  
Continuous Authentication ◽  
Continuous User ◽  
Access Patterns ◽  
Application Access

Smartphones have now become ubiquitous for accessing and controlling home appliances in smart homes, a popular application of the Internet of Things. User authentication on smartphones is mostly achieved at initial access. However, without applying a continuous authentication process, the network will be susceptible to unauthorized users. This issue emphasizes the importance of offering a continuous authentication scheme to identify the current user of the device. This can be achieved by extracting information during smartphone usage, including application access patterns. In this paper, we present a flexible machine learning user authentication scheme for smart home networks based on smartphone usage. Considering that users may run their smartphone applications differently during different day time intervals as well as different days of the week, new features are extracted by considering this information. The scheme is evaluated on a real-world dataset for continuous user authentication. The results show that the presented scheme authenticates users with high accuracy.

scholarly journals A Machine-learning-enabled Context-driven Control Mechanism for Software-defined Smart Home Networks

2019 ◽  
Vol 31 (6) ◽  
pp. 2103
Author(s):  
Ru Huang ◽  
Xiaoli Chu ◽  
Jie Zhang ◽  
Yu Hen Hu ◽  
Huaicheng Yan
Keyword(s):  
Machine Learning ◽  
Smart Home ◽  
Control Mechanism ◽  
Home Networks

scholarly journals Feature-Selection and Mutual-Clustering Approaches to Improve DoS Detection and Maintain WSNs’ Lifetime

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4821
Author(s):  
Rami Ahmad ◽  
Raniyah Wazirali ◽  
Qusay Bsoul ◽  
Tarik Abu-Ain ◽  
Waleed Abu-Ain
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Open Field ◽  
Network Lifetime ◽  
Detection Efficiency ◽  
Denial Of Service ◽  
Harmony Search ◽  
Machine Learning Algorithms ◽  
Transport Layer ◽  
Feature Selection Techniques

Wireless Sensor Networks (WSNs) continue to face two major challenges: energy and security. As a consequence, one of the WSN-related security tasks is to protect them from Denial of Service (DoS) and Distributed DoS (DDoS) attacks. Machine learning-based systems are the only viable option for these types of attacks, as traditional packet deep scan systems depend on open field inspection in transport layer security packets and the open field encryption trend. Moreover, network data traffic will become more complex due to increases in the amount of data transmitted between WSN nodes as a result of increasing usage in the future. Therefore, there is a need to use feature selection techniques with machine learning in order to determine which data in the DoS detection process are most important. This paper examined techniques for improving DoS anomalies detection along with power reservation in WSNs to balance them. A new clustering technique was introduced, called the CH_Rotations algorithm, to improve anomaly detection efficiency over a WSN’s lifetime. Furthermore, the use of feature selection techniques with machine learning algorithms in examining WSN node traffic and the effect of these techniques on the lifetime of WSNs was evaluated. The evaluation results showed that the Water Cycle (WC) feature selection displayed the best average performance accuracy of 2%, 5%, 3%, and 3% greater than Particle Swarm Optimization (PSO), Simulated Annealing (SA), Harmony Search (HS), and Genetic Algorithm (GA), respectively. Moreover, the WC with Decision Tree (DT) classifier showed 100% accuracy with only one feature. In addition, the CH_Rotations algorithm improved network lifetime by 30% compared to the standard LEACH protocol. Network lifetime using the WC + DT technique was reduced by 5% compared to other WC + DT-free scenarios.

HomeTec Software for Security Aspects of Smart Home Devices Based on IoT

2021 ◽  
pp. 5-16
Author(s):  
Parth Rustagi ◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Smart Home ◽  
Denial Of Service ◽  
Security Protocols ◽  
The Internet ◽  
Security Threats ◽  
Deep Dive ◽  
Cost Cutting ◽  
Full Control ◽  
Iot Devices ◽  
Service Data

As useful as it gets to connect devices to the internet to make life easier and more comfortable, it also opens the gates to various cyber threats. The connection of Smart Home devices to the internet makes them vulnerable to malicious hackers that infiltrate the system. Hackers can penetrate these systems and have full control over devices. This can lead to denial of service, data leakage, invasion of privacy, etc. Thus security is a major aspect of Smart home devices. However, many companies manufacturing these Smart Home devices have little to no security protocols in their devices. In the process of making the IoT devices cheaper, various cost-cutting is done on the security protocols in IoT devices. In some way, many manufactures of the devices don’t even consider this as a factor to build upon. This leaves the devices vulnerable to attacks. Various authorities have worked upon to standardize the security aspects for the IoT and listed out guidelines for manufactures to follow, but many fail to abide by them. This paper introduces and talks about the various threats, various Security threats to Smart Home devices. It takes a deep dive into the solutions for the discussed threats. It also discusses their prevention. Lastly, it discusses various preventive measures and good practices to be incorporated to protect devices from any future attacks.

scholarly journals Internet of Things-Based Intelligent Smart Home Control System

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Olutosin Taiwo ◽  
Absalom E. Ezugwu
Keyword(s):  
Machine Learning ◽  
Mobile Application ◽  
Smart Home ◽  
Human Life ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Automation System ◽  
Home Automation ◽  
Area Of Interest ◽  
Home Automation System

The smart home is now an established area of interest and research that contributes to comfort in modern homes. With the Internet being an essential part of broad communication in modern life, IoT has allowed homes to go beyond building to interactive abodes. In many spheres of human life, the IoT has grown exponentially, including monitoring ecological factors, controlling the home and its appliances, and storing data generated by devices in the house in the cloud. Smart home includes multiple components, technologies, and devices that generate valuable data for predicting home and environment activities. This work presents the design and development of a ubiquitous, cloud-based intelligent home automation system. The system controls, monitors, and oversees the security of a home and its environment via an Android mobile application. One module controls and monitors electrical appliances and environmental factors, while another module oversees the home’s security by detecting motion and capturing images. Our work uses a camera to capture images of objects triggered by their motion being detected. To avoid false alarms, we used the concept of machine learning to differentiate between images of regular home occupants and those of an intruder. The support vector machine algorithm is proposed in this study to classify the features of the image captured and determine if it is that of a regular home occupant or an intruder before sending an alarm to the user. The design of the mobile application allows a graphical display of the activities in the house. Our work proves that machine learning algorithms can improve home automation system functionality and enhance home security. The work’s prototype was implemented using an ESP8266 board, an ESP32-CAM board, a 5 V four-channel relay module, and sensors.

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