Mitigation against DDoS Attacks on an IoT-Based Production Line Using Machine Learning

The overwhelming acceptance and growing need for Internet of Things (IoT) products in each aspect of everyday living is creating a promising prospect for the involvement of humans, data, and procedures. The vast areas create opportunities from home to industry to make an automated lifecycle. Human life is involved in enormous applications such as intelligent transportation, intelligent healthcare, smart grid, smart city, etc. A thriving surface is created that can affect society, the economy, the environment, politics, and health through diverse security threats. Generally, IoT devices are susceptible to security breaches, and the development of industrial systems could pose devastating security vulnerabilities. To build a reliable security shield, the challenges encountered must be embraced. Therefore, this survey paper is primarily aimed to assist researchers by classifying attacks/vulnerabilities based on objects. The method of attacks and relevant countermeasures are provided for each kind of attack in this work. Case studies of the most important applications of the IoT are highlighted concerning security solutions. The survey of security solutions is not limited to traditional secret key-based cryptographic solutions, moreover physical unclonable functions (PUF)-based solutions and blockchain are illustrated. The pros and cons of each security solution are also discussed here. Furthermore, challenges and recommendations are presented in this work.

show abstract

“…Firewalls, intrusion detection systems, machine learning, etc. can be used to mitigate DRDoS attacks [ 179 ].…”

Section: Devices’ Vulnerabilities and Requirementsmentioning

confidence: 99%

Internet of Things: Security and Solutions Survey

Sadhu

Yanambaka

Abdelgawad

2022

Sensors

126

View full text Add to dashboard Cite

show abstract

“…Despite the fact that IoT offers several benefits in healthcare settings, one of the major challenges limiting its application are security threats, such as data theft, device hijacking, system attacks (e.g., Distributed Denial of Service or DDoS), data ownership disputes, etc. The interconnection between IoT-based devices with the internet makes them prone to cyber-attacks or hacks [ 82 , 83 ]. Moreover, the use of IoT-based devices requires the use of data storage, such as the cloud, as well as the transfer of a patient’s data through the internet, which raises issues regarding privacy and security [ 84 ].…”

Section: Computer-aided Diagnosis (Cad) and Internet Of Medical Thing...mentioning

confidence: 99%

Current Technologies for Detection of COVID-19: Biosensors, Artificial Intelligence and Internet of Medical Things (IoMT): Review

Irkham

Ibrahim

Nwekwo

et al. 2022

Sensors

View full text Add to dashboard Cite

Despite the fact that COVID-19 is no longer a global pandemic due to development and integration of different technologies for the diagnosis and treatment of the disease, technological advancement in the field of molecular biology, electronics, computer science, artificial intelligence, Internet of Things, nanotechnology, etc. has led to the development of molecular approaches and computer aided diagnosis for the detection of COVID-19. This study provides a holistic approach on COVID-19 detection based on (1) molecular diagnosis which includes RT-PCR, antigen–antibody, and CRISPR-based biosensors and (2) computer aided detection based on AI-driven models which include deep learning and transfer learning approach. The review also provide comparison between these two emerging technologies and open research issues for the development of smart-IoMT-enabled platforms for the detection of COVID-19.

show abstract

“…A logistic regression (LR) algorithm can estimate the probability of a particular instance belonging to a specific class, and, for that reason, is frequently employed in classification problems regarding intrusion detection and spam filtering [21]. Furthermore, the study in [22] designed and implemented a security solution based on a LR algorithm and showed that it is possible to secure an IoT-based production line against DDoS attacks by using ML algorithms and commonly available tools for network traffic analysis and evaluation.…”

Section: Logistic Regression (Lr)mentioning

confidence: 99%

“…[ 15,21,22] Support Vector Machine Better performance in datasets with few classes and many instances per class. Scalable.…”

Section: Difficult To Perform Classification In Case Of Non-linearly Separablementioning

confidence: 99%

An Anomaly-Based Intrusion Detection System for Internet of Medical Things Networks

et al. 2021

View full text Add to dashboard Cite

Over the past few years, the healthcare sector is being transformed due to the rise of the Internet of Things (IoT) and the introduction of the Internet of Medical Things (IoMT) technology, whose purpose is the improvement of the patient’s quality of life. Nevertheless, the heterogenous and resource-constrained characteristics of IoMT networks make them vulnerable to a wide range of threats. Thus, novel security mechanisms, such as accurate and efficient anomaly-based intrusion detection systems (AIDSs), considering the inherent limitations of the IoMT networks, need to be developed before IoMT networks reach their full potential in the market. Towards this direction, in this paper, we propose an efficient and effective anomaly-based intrusion detection system (AIDS) for IoMT networks. The proposed AIDS aims to leverage host-based and network-based techniques to reliably collect log files from the IoMT devices and the gateway, as well as traffic from the IoMT edge network, while taking into consideration the computational cost. The proposed AIDS is to rely on machine learning (ML) techniques, considering the computation overhead, in order to detect abnormalities in the collected data and thus identify malicious incidents in the IoMT network. A set of six popular ML algorithms was tested and evaluated for anomaly detection in the proposed AIDS, and the evaluation results showed which of them are the most suitable.

show abstract

Mitigation against DDoS Attacks on an IoT-Based Production Line Using Machine Learning

Cited by 16 publications

References 22 publications

Internet of Things: Security and Solutions Survey

Internet of Things: Security and Solutions Survey

Current Technologies for Detection of COVID-19: Biosensors, Artificial Intelligence and Internet of Medical Things (IoMT): Review

An Anomaly-Based Intrusion Detection System for Internet of Medical Things Networks

Contact Info

Product

Resources

About