DDoS Reflection Attack Based on IoT: A Case Study

Šimon, Marek; Huraj, Ladislav; Horák, Tibor

doi:10.1007/978-3-319-91192-2_5

Cited by 14 publications

(9 citation statements)

References 15 publications

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“…Distributed Reflection Denial of Service Attack (DRDoS): DRDoS is one form of DDoS attack. In this attack, an attacker preserves anonymity through an IP address by using a third party, called a reflector, which needs not be compromised [ 176 ]. Numerous victim machines that unintentionally take part in a DDoS attack on the attacker’s target are typically used in DRDoS attacks.…”

Section: Devices’ Vulnerabilities and Requirementsmentioning

confidence: 99%

Internet of Things: Security and Solutions Survey

Sadhu

Yanambaka

Abdelgawad

2022

Sensors

109

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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.

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Section: Devices’ Vulnerabilities and Requirementsmentioning

confidence: 99%

Internet of Things: Security and Solutions Survey

Sadhu

Yanambaka

Abdelgawad

2022

Sensors

109

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“…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%

“…To increase security levels, these companies developed an authentication approach which gives access to only a few staff members with clearance. Other practices that can increase security include implementing security protocols through tracking and monitoring, network segmentation, monitoring of inventories of devices, and encryption [ 82 , 83 , 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

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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.

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“…The centralized scanning mechanism of JenX enables attackers to offer botnet-for-hire and DDoSfor-hire services [21]. Other attacks were explored by [18] and [22], who inquired into potential attacks by employing IoT as a reflector of DDoS attacks, which are very difficult to trace. Adding to our understanding of cyberattacks, Soltan et al [23] examined a possible attack in which a botnet utilizes highwattage IoT devices to manipulate demand and thus disrupt power grid operations.…”

Section: A Iot Botnetsmentioning

confidence: 99%

A Multi-Class Neural Network Model for Rapid Detection of IoT Botnet Attacks

Alzahrani¹

2020

IJACSA

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The tremendous number of Internet of Things (IoT) devices and their widespread use have made our lives considerably more manageable and safer. At the same time, however, the vulnerability of these innovations means that our day-today existence is surrounded by insecure devices, thereby facilitating ways for cybercriminals to launch various attacks by large-scale robot networks (botnets) through IoT. In consideration of these issues, we propose a neural network-based model to detect IoT botnet attacks. Furthermore, the model provides multi-classification, which is necessary for taking appropriate countermeasures to understand and stop the attacks. In addition, it is independent and does not require specific equipment or software to fetch the required features. According to the conducted experiments, the proposed model is accurate and achieves 99.99%, 99.04% as F1 score for two benchmark datasets in addition to fulfilling IoT constraints regarding complexity and speed. It is less complicated in terms of computations, and it provides real-time detection that outperformed the state-of-theart, achieving a detection time ratio of 1:5 and a ratio of 1:8.

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DDoS Reflection Attack Based on IoT: A Case Study

Cited by 14 publications

References 15 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

A Multi-Class Neural Network Model for Rapid Detection of IoT Botnet Attacks

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