Machine Learning Solutions for the Security of Wireless Sensor Networks: A Review

Raniyah Wazirali Tarik Abu-Ain Rami Ahmad

23 Juni 2022

Energy and security are major challenges in a wireless sensor network, and they work oppositely. As security complexity increases, battery drain will increase. Due to the limited power in wireless sensor networks, options to rely on the security of ordinary protocols embodied in encryption and key management are futile due to the nature of communication between sensors and the ever-changing network topology. Therefore, machine learning algorithms are one of the proposed solutions for providing security services in this type of network by including monitoring and decision intelligence. Machine learning algorithms present additional hurdles in terms of training and the amount of data required for training. This paper provides a convenient reference for wireless sensor network infrastructure and the security challenges it faces. It also discusses the possibility of benefiting from machine learning algorithms by reducing the security costs of wireless sensor networks in several domains; in addition to the challenges and proposed solutions to improving the ability of sensors to identify threats, attacks, risks, and malicious nodes through their ability to learn and self-development using machine learning algorithms. Furthermore, this paper discusses open issues related to adapting machine learning algorithms to the capabilities of sensors in this type of network.

A. Rehman Zahid Mehmood U. Tariq + 3 lainnya

1 Mei 2021

Machine learning techniques are proven valuable for the Internet of things (IoT) due to intelligent and cost-effective computing processes. In recent decades, wireless sensor network (WSN) and machine learning are integrated to give significant improvements for energy-based systems. However, resourceful routes analytic with nominal energy consumption are some demanding challenges. Moreover, WSN operates in an unpredictable space and a lot of network threats can be harmful to smart and secure data gathering. Consequently, security against such threats is another major concern for low-power sensors. Therefore, we aim to present a machine learning-based approach for autonomous IoT Security to achieve optimal energy efficiency and reliable transmissions. First, the proposed protocol optimizes network performance using a model-free Q-learning algorithm and achieves fault-tolerant data transmission. Second, it accomplishes data confidentiality against adversaries using a cryptography-based deterministic algorithm. The proposed protocol demonstrates better conclusions than other existing solutions.

N. Lamghari I. Hafidi Hiba Tabbaa + 1 lainnya

1 Januari 2021

The number of Wireless sensor network (WSN) deployments have been growing so exponentially over the recent years. Due to their small size and cost-effective, WSN are attracting many industries to use them in various applications. Environmental monitoring, security of buildings and precision agriculture are few example among several other fields. However, WSN faces high security threats considering most of them are deployed in unattended nature and hostile environment. In the aim of providing secure data processing in the WSN, many techniques are proposed to protect the data privacy while being transferred from the sensors to the base station. This work is focusing on attack detection which is an essential task to secure the network and the data. Anomaly detection is a key challenge in order to ensure the security and prevent malicious attacks in wireless sensor networks. Various machine learning techniques have been used by researchers these days to detect anomalies using offline learning algorithms. On the other hand online learning classifiers have not been thoroughly addressed in the literature. Our aim is to provide an intrusion detection model compatible with the characteristics of WSN. This model is built based on information gain ratio and the online Passive aggressive classifier. Firstly, the information gain ratio is used to select the relevant features of the sensor data. Secondly, the online Passive aggressive algorithm is trained to detect and classify different type of Deny of Service attacks. The experiment was conducted on a wireless sensor network-detection system (WSN-DS) dataset. The proposed model ID-GOPA results detection rate of 96% determining whether the network is in its normal mode or exposed to any type of attack. The detection accuracy is 86%, 68%, 63%, and 46% for scheduling, grayhole, flooding and blackhole attacks, respectively, in addition to 99% for normal traffic. These results shows that our model based on offline learning can be providing good anomaly detection to the WSN and replace online learning in some cases.

Hosam F. El-Sofany Omar H. Karam S. A. El-Seoud + 1 lainnya

27 Mei 2024

The term “Internet of Things” (IoT) refers to a system of networked computing devices that may work and communicate with one another without direct human intervention. It is one of the most exciting areas of computing nowadays, with its applications in multiple sectors like cities, homes, wearable equipment, critical infrastructure, hospitals, and transportation. The security issues surrounding IoT devices increase as they expand. To address these issues, this study presents a novel model for enhancing the security of IoT systems using machine learning (ML) classifiers. The proposed approach analyzes recent technologies, security, intelligent solutions, and vulnerabilities in ML IoT-based intelligent systems as an essential technology to improve IoT security. The study illustrates the benefits and limitations of applying ML in an IoT environment and provides a security model based on ML that manages autonomously the rising number of security issues related to the IoT domain. The paper proposes an ML-based security model that autonomously handles the growing number of security issues associated with the IoT domain. This research made a significant contribution by developing a cyberattack detection solution for IoT devices using ML. The study used seven ML algorithms to identify the most accurate classifiers for their AI-based reaction agent’s implementation phase, which can identify attack activities and patterns in networks connected to the IoT. The study used seven ML algorithms to identify the most accurate classifiers for their AI-based reaction agent’s implementation phase, which can identify attack activities and patterns in networks connected to the IoT. Compared to previous research, the proposed approach achieved a 99.9% accuracy, a 99.8% detection average, a 99.9 F1 score, and a perfect AUC score of 1. The study highlights that the proposed approach outperforms earlier machine learning-based models in terms of both execution speed and accuracy. The study illustrates that the suggested approach outperforms previous machine learning-based models in both execution time and accuracy.

S. Ameen S. H. Haji

2 Juni 2021

The Internet of Things (IoT) is one of today's most rapidly growing technologies. It is a technology that allows billions of smart devices or objects known as "Things" to collect different types of data about themselves and their surroundings using various sensors. They may then share it with the authorized parties for various purposes, including controlling and monitoring industrial services or increasing business services or functions. However, the Internet of Things currently faces more security threats than ever before. Machine Learning (ML) has observed a critical technological breakthrough, which has opened several new research avenues to solve current and future IoT challenges. However, Machine Learning is a powerful technology to identify threats and suspected activities in intelligent devices and networks. In this paper, various ML algorithms have been compared in terms of attack detection and anomaly detection, following a thorough literature review on Machine Learning methods and the significance of IoT security in the context of various types of potential attacks. Furthermore, possible ML-based IoT protection technologies have been introduced.