Situational Awareness Using Distributed Data Fusion with Evidence Discounting

Pietro, Antonio Di; Panzieri, Stefano; Gasparri, Andrea

doi:10.1007/978-3-319-26567-4_17

Cited by 6 publications

(2 citation statements)

References 23 publications

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“…Moreover, a disaster can, temporarily or permanently, damage also data centres hosting the core services or cloud services. As disasters often affect people, communication networks are viewed as an essential part of the critical infrastructure [16] allowing us to communicate and providing emergency communications between citizens and authorities. Therefore, in a post-disaster period, we can often observe a substantial increase in the network traffic demand volume, which is hard to serve in a network already affected by a disaster event and contributes to network saturation and service unavailability.…”

Section: Introductionmentioning

confidence: 99%

Reinforced Secure Gossiping Against DoS Attacks in Post-Disaster Scenarios

2020

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Section: Introductionmentioning

confidence: 99%

Reinforced Secure Gossiping Against DoS Attacks in Post-Disaster Scenarios

2020

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“…A considerable amount of research has focused on the development of intrusion/anomaly detection systems. Different strategies have been suggested by embracing diverse techniques such as classification [19], [20], multivariate statistical analysis, principal component analysis [21], [22], and data fusion [23]- [25].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection in Aging Industrial Internet of Things

2019

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The Industrial Internet of Things (IIoT) have been designed to perform a more agile and efficient automation, control, and orchestration of future industrial systems while improving the energy efficiency in smart factories. Unfortunately, while the benefits of the IIoT are undeniable, their pervasive adoption as key enablers for future industries also paved the way for new security risks. In fact, the damaging effects of exploiting vulnerable IIoT have been repeatedly demonstrated and publicly reported. The Mirai botnet, various reports on hackable and invasive devices, alongside the infamous Stuxnet malware, constitute significant proof on the undisputed and disruptive effect of the malware-targeting IIoT systems. As a response, a plethora of solutions has been developed to address the issue of securing IIoT systems in specific sectors. Nevertheless, we believe that the gradual decay of the IIoT's physical dimension (e.g., the physical process), also called aging, is a natural component of the IIoT's life cycle, which has not received sufficient attention from the scientific community. This paper develops a methodology for detecting abnormal behavior in the context of aging IIoT. The approach leverages multivariate statistical analysis [e.g., principle component analysis (PCA)], alongside the Hotelling's T 2 statistics, and the univariate cumulative sum in order to detect abnormal process events. An innovative feature of the developed approach is the detection of stealth attacks attempting to influence the dataset in each age. The extensive experimental results on a continuous stirred-tank reactor (CSTR) model demonstrate its applicability to the IIoT and its superior performance to the recently reported techniques. INDEX TERMS Anomaly detection systems, Industrial Internet of Things, aging processes, multivariate statistical analysis.

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