Testing IoT Security: The Case Study of an IP Camera

Abdalla, Peshraw Ahmed; Varol, Cihan

doi:10.1109/isdfs49300.2020.9116392

Cited by 33 publications

(18 citation statements)

References 12 publications

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“…Te Health Insurance, Portability, and Accounting Act (HIPAA) and Health Information Technology for Economic and Clinical Health Act (HITECH) require all healthcare organizations to ensure the safety of health information. Network attacks against IoT terminal devices, such as webcams [15], small routers [16], bluetooth door locks [17], intelligent thermostats [18], and theft of face recognition data [19], have made people gradually realize that IoT devices in IoHT systems may become a tool for attackers to launch network attacks and destroy the stability of IoHT systems or steal user-sensitive information.…”

Section: Requirements Of Ioht Systems Lightweight Authentication Stra...mentioning

confidence: 99%

A Lightweight Three‐Party Mutual Authentication Protocol for Internet of Health Things Systems

Wang

Zhao

Sun

et al. 2023

Journal of Healthcare Engineering

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In Internet of Health Things (IoHT) systems, there is a two-hop network structure between the authentication server TA, Internet of Things Connector (IotC), and wearable sensor (WS). Attackers can use the sensor layer network (the first hop) between the IotC and WS to steal patient’s health-related information and undermine the security of the system and the privacy of sensitive information. To address this threat, this study proposes a lightweight identity authentication and key agreement protocol for third-party authentication servers TA, IotC, and WS. The results of the formal security proof, BAN logic analysis, and AVISPA tool simulation show that the scheme proposed in this study has an ideal security performance and can meet the security requirements of IoHT. In terms of performance, the proposed scheme could dynamically construct a sensor layer network (the first hop) and offline networking according to the diagnostic needs of doctors. Compared with other related protocols, the proposed scheme can significantly reduce the computing resource requirements of IotC and server TA and the resource requirements of database I/O operation of server TA in the application scenario of concurrent access of multiple WS nodes.

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Section: Requirements Of Ioht Systems Lightweight Authentication Stra...mentioning

confidence: 99%

A Lightweight Three‐Party Mutual Authentication Protocol for Internet of Health Things Systems

Wang

Zhao

Sun

et al. 2023

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…Due to the growth of smart IoT environments, and the widespread adoption of these paradigms by organizations around the globe that incorporate them into their existing infrastructure, the security and integrity of these systems has become a popular topic of study [14], [15], [16], [17]. In this section, we provide research related to the development and utilization of penetration testing techniques in IoT settings, focusing on vulnerability detection.…”

Section: Related Workmentioning

confidence: 99%

“…Abdalla et al [15] presented a penetration testing showcase for the IP camera "Onvif YY HD". The proposed penetration testing process included 3 steps, starting with defining the area of investigation, implementation of process and outcome report presentation.…”

Section: Related Workmentioning

confidence: 99%

“…Although the presented studies [14], [15], [16], [17] provided either a functional implementation or an extendable abstract design for a penetration testing tool tailor-made for IoT settings, the construction of a versatile and efficient deep learning-based penetration testing tool for smart airports that focuses on vulnerability detection, is still an unexplored topic. Additionally, some of the presented work either lacked any specific details of their testing environment or neglected to evaluate their solution through experimentation.…”

Section: Related Workmentioning

confidence: 99%

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A Deep Learning-based Penetration Testing Framework for Vulnerability Identification in Internet of Things Environments

Koroniotis¹,

Moustafa²,

Turnbull³

et al. 2021

Preprint

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The Internet of Things (IoT) paradigm has displayed tremendous growth in recent years, resulting in innovations like Industry 4.0 and smart environments that provide improvements to efficiency, management of assets and facilitate intelligent decision making. However, these benefits are offset by considerable cybersecurity concerns that arise due to inherent vulnerabilities, which hinder IoT-based systems' Confidentiality, Integrity, and Availability. Security vulnerabilities can be detected through the application of penetration testing, and specifically, a subset of the information-gathering stage, known as vulnerability identification. Yet, existing penetration testing solutions can not discover zero-day vulnerabilities from IoT environments, due to the diversity of generated data, hardware constraints, and environmental complexity. Thus, it is imperative to develop effective penetration testing solutions for the detection of vulnerabilities in smart IoT environments. In this paper, we propose a deep learning-based penetration testing framework, namely Long Short-Term Memory Recurrent Neural Network-Enabled Vulnerability Identification (LSTM-EVI). We utilize this framework through a novel cybersecurity-oriented testbed, which is a smart airport-based testbed comprised of both physical and virtual elements. The framework was evaluated using this testbed and on real-time data sources. Our results revealed that the proposed framework achieves about 99% detection accuracy for scanning attacks, outperforming other four peer techniques.

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“…Deep learning and machine learning techniques are used in many fields nowadays [2][3][4][5][6]. This system can be made accessible worldwide by various technologies, including IoT and cloud-based systems [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Deep Transfer Learning Networks for Brain Tumor Detection: The Effect of MRI Patient Image Augmentation Methods

Abdalla

Qadir

Rashid

et al. 2022

IJECS

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The exponential growth of deep learning networks has enabled us to handle difficult tasks, even in the complex field of medicine with small datasets. In the sphere of treatment, they are particularly significant. To identify brain tumors, this research examines how three deep learning networks are affected by conventional data augmentation methods, including MobileNetV2, VGG19, and DenseNet201. The findings showed that before and after utilizing approaches, picture augmentation schemes significantly affected the networks. The accuracy of MobileNetV2, which was originally 85.33%, was then enhanced to 96.88%. The accuracy of VGG19, which was 77.33%, was then enhanced to 95.31%, and DenseNet201, which was originally 82.66%, was then enhanced to 93.75%. The models' accuracy percentage engagement change is 13.53%, 23.25%, and 23.25%, respectively. Finally, the conclusion showed that applying data augmentation approaches improves performance, producing models far better than those trained from scratch.

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Testing IoT Security: The Case Study of an IP Camera

Cited by 33 publications

References 12 publications

A Lightweight Three‐Party Mutual Authentication Protocol for Internet of Health Things Systems

A Lightweight Three‐Party Mutual Authentication Protocol for Internet of Health Things Systems

A Deep Learning-based Penetration Testing Framework for Vulnerability Identification in Internet of Things Environments

Deep Transfer Learning Networks for Brain Tumor Detection: The Effect of MRI Patient Image Augmentation Methods

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