Review of Security Issues in Industrial Networks

Cheminod, Manuel; Durante, Luca; Valenzano, Adriano

doi:10.1109/tii.2012.2198666

Cited by 285 publications

(130 citation statements)

References 74 publications

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“…Many industrial applications may be mission critical and may have certification or standardisation requirements that make security crucial. The peculiarities of industrial networks restrict the use of classical approaches to security [80]. The cognition and reconfigurability of CRs central to their functioning introduce a new class of security concerns distinct to those evident in conventional wireless networks [81].…”

Section: Securitymentioning

confidence: 99%

Using Cognitive Radio for Interference-Resistant Industrial Wireless Sensor Networks: An Overview

Chiwewe

Mbuya

Hancke

2015

IEEE Trans. Ind. Inf.

137

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Abstract-Industrial wireless sensor networks have to contend with environments that are usually harsh and time varying. Industrial wireless technology, such as WirelessHART and ISA 100.11a, also operates in a frequency spectrum utilised by many other wireless technologies, and with wireless applications rapidly growing it is possible that multiple heterogeneous wireless systems would need to operate in overlapping spatiotemporal regions. Interference such as noise or other wireless devices affects connectivity and reduces communication link quality. This negatively affects reliability and latency, which are core requirements of industrial communication. Building wireless networks that are resistant to noise in industrial environments and can coexist with competing wireless devices in an increasingly crowded frequency spectrum is challenging. To meet these challenges, we need to consider the benefits that approaches finding success in other application areas can offer industrial communication. Cognitive radio methods offer a potential solution to improve resistance of industrial wireless sensor networks to interference. Integrating cognitive radio principles into the lower layers of industrial wireless sensor networks can enable devices to detect and avoid interference and potentially opens the possibility of utilising free radio spectrum for additional communication channels. This improves resistance to noise and increases redundancy in terms of channels per network node or adding additional nodes. In this paper, we summarise cognitive radio methods relevant to industrial applications, covering cognitive radio architecture, spectrum access and interference management, spectrum sensing, dynamic spectrum access, game theory and cognitive radio network security.Index Terms-Cognitive radio, industrial wireless sensor networks, Internet of Things, spectrum management, spectrum sensing.

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Section: Securitymentioning

confidence: 99%

Using Cognitive Radio for Interference-Resistant Industrial Wireless Sensor Networks: An Overview

Chiwewe

Mbuya

Hancke

2015

IEEE Trans. Ind. Inf.

137

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“…Vendors would typically offer turn key solutions, which would be incompatible with competitors' systems [42]. Security was not a main concern in the design of these systems, instead major concerns regarded real-time processing, jitter limitation and eventnotification [34]. Despite the lack of security features, SCADA vendors and operators believed they could rely on two forms of protection.…”

Section: Evolution and Vulnerabilitiesmentioning

confidence: 99%

“…A summary of the differences between the environments is shown in Table 4.1 (adapted from [34]). For a detailed description of the differences between SCADA and traditional IT environments, the interested reader is referred to [137].…”

Section: Differences With Traditional Itmentioning

confidence: 99%

“…For example, take the two time series representing the number of transmitted packets per second over the course of a week displayed in Figure 1 generated through an automated process, like the polling mechanism used by SCADA servers to retrieve information from the field devices. Furthermore, SCADA components have a lifetime of tens of years [137] and changes in software are rare, if at all [34]. This should make SCADA networks even more stable, potentially reducing the high false-alarm rates common to anomaly detection methods developed for traditional IT system.…”

Section: Introductionmentioning

confidence: 99%

“…First, SCADA networks are expected to present a nearly fixed number of nodes, in the sense that new nodes are not expected to join or leave the network frequently. Many SCADA systems are designed to work continuously, with virtually no changes, for tens of years [34]. In contrast, traditional IT components have a typical lifetime of 3 to 5 years [137].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Anomaly detection in SCADA systems : a network based approach

Barbosa¹

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Bipolar fully recurrent deep structured neural learning based attack detection for securing industrial sensor networks

Alzubi

2020

Trans Emerging Tel Tech

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Attack detection is a significant problem to be resolved to attain security in industrial sensor network. Few research works have been designed for performing attack discovery process with the help of different classification algorithms. However, classification performance was lowered to accurately find attack nodes and thereby getting higher security level (SL). In order to overcome such limitations, bipolar fully recurrent deep structured neural learning based attack detection (BFRDSNL‐AD) technique is proposed in this article. The BFRDSNL‐AD technique comprises of three main layers, namely, input, hidden, and output layer to accurately carry‐out attack detection process. The input layer in BFRDSNL‐AD Technique takes a number of sensor nodes as input. The designed BFRDSNL‐AD technique utilized numbers of hidden layers in order to deeply examine each sensor node in industrial sensor network. The result of the hidden layer is fed‐back into the network along with the inputs in order to discover the temporal dynamic behavior of sensor nodes. In BFRDSNL‐AD technique, output layer applies bipolar activation function to determine the association between identified node features to give attack detection result. If the output layer result is 1, then sensor node is considered as normal. Otherwise, sensor node is considered as malicious attack in industrial sensor network. Thus, the BFRDSNL‐AD technique increases the attack detection performance with higher accuracy and minimal time. From that, BFRDSNL‐AD technique gets enhanced SL in industrial sensor network. The simulation of BFRDSNL‐AD technique is conducted using metrics such as security, data delivery rate, data loss rate, and delay with respect to a different number of sensor nodes and data packets.

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Review of Security Issues in Industrial Networks

Cited by 285 publications

References 74 publications

Using Cognitive Radio for Interference-Resistant Industrial Wireless Sensor Networks: An Overview

Using Cognitive Radio for Interference-Resistant Industrial Wireless Sensor Networks: An Overview

Anomaly detection in SCADA systems : a network based approach

Bipolar fully recurrent deep structured neural learning based attack detection for securing industrial sensor networks

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