Security Assessment Of Internet Of Things In Healthcare Environment

Ali, Dia M.; Mahmoud, Ahmed S.

doi:10.1109/iccista.2019.8830663

Cited by 6 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They suggested a reverse engineering-based method to analyze the security challenges in the system, thereby enabling suitable countermeasures. Ali and Mahmoud (2019) [11] mentioned various routing attacks found in healthcare IoT-based networks. Black hole attacks and delay attacks are explored in detail, and the authors also proposed a preventive mechanism that employs both presence and absence of AODV protocol.…”

Section: Surveys On Security Challengesmentioning

confidence: 99%

Strength of Deep Learning-based Solutions to Secure Healthcare IoT: A Critical Review

Mathew¹,

Mani²

2023

TOBEJ

View full text Add to dashboard Cite

Healthcare applications of IoT systems have gained huge popularity across the globe. From personal monitoring to expert clinical diagnosis, healthcare IoT systems have shown their importance to all possible extents. The ease of use and precise results add to the wide acceptance of such systems. However, this has also led to a magnificent increase in the number of attacks aimed at stealing or manipulating data as well as operations of HIoT-based healthcare assistance. Among the various modes of attacks, network-based attacks are found in the majority. In this work, we perform a critical review of these attacks, the existing countermeasures, and their limitations to understand and proclaim the importance of securing healthcare networks in the best possible manner. We also emphasize the necessity of deep learning-based smart solutions for securing healthcare systems, understanding the potential of deep learning in the security aspects being deployed in other genres of IoT applications. A comparative analysis of deep learning and machine learning-based security solutions is performed to examine their performances.

show abstract

Section: Surveys On Security Challengesmentioning

confidence: 99%

Strength of Deep Learning-based Solutions to Secure Healthcare IoT: A Critical Review

Mathew¹,

Mani²

2023

TOBEJ

View full text Add to dashboard Cite

show abstract

“…Internet of Tings (IoT) advancements in healthcare hold great promise for improving the sector's technological, social, and economic future and thereby ensuring a healthy future for all. Tanks to wireless connectivity between devices in the medical feld and the Internet, patients can monitor their health status from afar [40][41][42][43][44][45][46]. Real-time patient monitoring, enhanced diagnostic precision, and more efcient treatment are all made feasible by the IoMT.…”

Section: Related Workmentioning

confidence: 99%

Detection of Middlebox-Based Attacks in Healthcare Internet of Things Using Multiple Machine Learning Models

Abdulwahid

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The huge number of network traffic data, the abundance of available network features, and the diversity of cyber-attack patterns mean that intrusion detection remains difficult even though many earlier efforts have succeeded in building the Internet of Healthcare Things (IoHT). The implementation of an effective algorithm to filter out most of the probable outliers of Round Trip Time (RTT) of packets recorded in the Internet environment is urgently required. Congestion and interference in networks can arise when numerous biosensors in an IoHT system all attempt to communicate at once. Internet of Health Things networks are susceptible to both intra- and internetwork interference. In this research, the Server-Side Includes (SSI) attack is a key issue because it allows for network compromise as part of Internal Attacks. Despite recent advancements, SSI detection remains difficult due to the vast amounts of network traffic data, the abundance of network features, and the diversity of cyber-attack patterns (DDoS, DoS, Satan, spoofing, etc.). With the help of sensors, physiological data may be collected and sent to distant servers, where they can be analyzed in real time by doctors to help them catch diseases in their earliest stages. This is made possible by the Internet of medical things (IoMT). Wireless data transfer, however, leaves it vulnerable to hackers, especially if the data being transferred are particularly private or sensitive. Security measures designed for devices with more storage space and processing power will not work on those with less. However, machine learning for intrusion detection can give a tailored security response to the needs of IoMT systems. For SSI detection, current methods are either inefficient because of the large number of packets that need to be caught and analyzed or unsuccessful because of outlier values in the RTTs obtained from the captured TCP packets. To the same end, “downstream detection” refers to the process of calculating the total length of all connections made after a certain point. As a means of improving the SSI detection algorithm’s throughput in a network environment, packet RTT outliers will be eliminated. Flow records are used as inputs by flow-based NIDS to determine whether or not a given flow is malicious. In order to detect middlebox-based attacks from two Medical Health IoT datasets, this paper proposes a unique architecture of explainable neural networks (XNN). The model’s accuracy in classifying attacks in dataset 1 of the IoHT is 99.7%t, besides achieving 99.4% accuracy in categorising attacks on IoHT dataset 2.

show abstract

Prediction of middle box-based attacks in Internet of Healthcare Things using ranking subsets and convolutional neural network

Bangali,

Rodrigues,

Pandimurugan

et al. 2023

Wireless Netw

View full text Add to dashboard Cite

Security Assessment Of Internet Of Things In Healthcare Environment

Cited by 6 publications

References 6 publications

Strength of Deep Learning-based Solutions to Secure Healthcare IoT: A Critical Review

Strength of Deep Learning-based Solutions to Secure Healthcare IoT: A Critical Review

Detection of Middlebox-Based Attacks in Healthcare Internet of Things Using Multiple Machine Learning Models

Prediction of middle box-based attacks in Internet of Healthcare Things using ranking subsets and convolutional neural network

Contact Info

Product

Resources

About