Classification of Security Risks in the IoT Environment

Cvitić, Ivan; Vujić, Miroslav; Husnjak, Siniša

doi:10.2507/26th.daaam.proceedings.102

Cited by 29 publications

(18 citation statements)

References 25 publications

(30 reference statements)

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“…Cvitic et al [168] identify various limitations and propose partial solutions. They propose a lightweight encryption function using Identity Based Encryption, called VLFSR.…”

Section: Rfidmentioning

confidence: 99%

Security in IoMT Communications: A Survey

Koutras

Stergiopoulos

Dasaklis

et al. 2020

Sensors

105

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The Internet of Medical Things (IoMT) couples IoT technologies with healthcare services in order to support real-time, remote patient monitoring and treatment. However, the interconnectivity of critical medical devices with other systems in various network layers creates new opportunities for remote adversaries. Since most of the communication protocols have not been specifically designed for the needs of connected medical devices, there is a need to classify the available IoT communication technologies in terms of security. In this paper we classify IoT communication protocols, with respect to their application in IoMT. Then we describe the main characteristics of IoT communication protocols used at the perception, network and application layer of medical devices. We examine the inherent security characteristics and limitations of IoMT-specific communication protocols. Based on realistic attacks we identify available mitigation controls that may be applied to secure IoMT communications, as well as existing research and implementation gaps.

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“…Cvitic et al [168] identify various limitations and propose partial solutions. They propose a lightweight encryption function using Identity Based Encryption, called VLFSR.…”

Section: Rfidmentioning

confidence: 99%

Security in IoMT Communications: A Survey

Koutras

Stergiopoulos

Dasaklis

et al. 2020

Sensors

105

View full text Add to dashboard Cite

show abstract

“…Additionally, the threats and security problems inherited from the traditional network [37]. As shown in Figure 2, the attacks classified on the IoT three layers: perception, network, and application-layer [38]. Artificial intelligence has attracted attention in recent years, especially in the field of IoT security.…”

Section: Iot Security Overviewmentioning

confidence: 99%

DeepDCA: Novel Network-Based Detection of IoT Attacks Using Artificial Immune System

et al. 2020

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Recently Internet of Things (IoT) attains tremendous popularity, although this promising technology leads to a variety of security obstacles. The conventional solutions do not suit the new dilemmas brought by the IoT ecosystem. Conversely, Artificial Immune Systems (AIS) is intelligent and adaptive systems mimic the human immune system which holds desirable properties for such a dynamic environment and provides an opportunity to improve IoT security. In this work, we develop a novel hybrid Deep Learning and Dendritic Cell Algorithm (DeepDCA) in the context of an Intrusion Detection System (IDS). The framework adopts Dendritic Cell Algorithm (DCA) and Self Normalizing Neural Network (SNN). The aim of this research is to classify IoT intrusion and minimize the false alarm generation. Also, automate and smooth the signal extraction phase which improves the classification performance. The proposed IDS selects the convenient set of features from the IoT-Bot dataset, performs signal categorization using the SNN then use the DCA for classification. The experimentation results show that DeepDCA performed well in detecting the IoT attacks with a high detection rate demonstrating over 98.73% accuracy and low false-positive rate. Also, we compared these results with State-of-the-art techniques, which showed that our model is capable of performing better classification tasks than SVM, NB, KNN, and MLP. We plan to carry out further experiments to verify the framework using a more challenging dataset and make further comparisons with other signal extraction approaches. Also, involve in real-time (online) attack detection.

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“…The reasons lie in a large number of users and data that are processed and stored in the observed layer as well as the known vulnerabilities of virtualisation whose unauthorized usage has the potential to cause damage of great dimensions to a large number of users at a time. The classification of risk to the user information system in the traffic environment is based on the increased implementation of the IoT concept in certain environment according to which the level of risk Medium has been assigned (Cvitić et al 2016).…”

Section: Classification Of the Security Risk Of The Proposed Iot Archmentioning

confidence: 99%

Beacon Technology for Real-Time Informing the Traffic Network Users About the Environment

et al. 2019

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Informing the users about their environment is of extreme importance for their full and independent functioning in the traffic system. Today’s development of technology provides the user the access to information about their environment by using the smartphone device at any moment if there is a defined applicative solution. For this, it is necessary to define the user’s environment according to the Ambient Assisted Living (AAL) concept, which understands adequate technology of gathering, processing and distribution of information. This paper presents the proposal of the solution for informing the traffic network users about the environment for the defined group of users based on the beacon technology. The mentioned solution is based on the results of two separate studies about the needs of users who move along a part of the traffic network. The aim of the proposed solution is to provide the user with precise and real-time information and to raise the level of safety during movement.

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Classification of Security Risks in the IoT Environment

Cited by 29 publications

References 25 publications

Security in IoMT Communications: A Survey

Security in IoMT Communications: A Survey

DeepDCA: Novel Network-Based Detection of IoT Attacks Using Artificial Immune System

Beacon Technology for Real-Time Informing the Traffic Network Users About the Environment

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