False Data Detection for Fog and Internet of Things Networks

Abstract

The Internet of Things (IoT) context brings new security issues due to billions of smart end-devices both interconnected in wireless networks and connected to the Internet by using different technologies. In this paper, we propose an attack-detection method, named Data Intrusion Detection System (DataIDS), based on real-time data analysis. As end devices are mainly resource constrained, Fog Computing (FC) is introduced to implement the DataIDS. FC increases storage, computation capabilities, and processing capabilities, allowing it to detect promptly an attack with respect to security solutions on the Cloud. This paper also considers an attack tree to model threats and vulnerabilities of Fog/IoT scenarios with heterogeneous devices and suggests countermeasure costs. We verify the performance of the proposed DataIDS, implementing a testbed with several devices that measure different physical quantities and by using standard data-gathering protocols.

A Heterogeneous Layer-Based Trustworthiness Model for Long Backhaul NVIS Challenging Networks and an IoT Telemetry Service for Antarctica

“…For instance, the authors of [ 34 ] presented a distributed Bayesian algorithm to detect faulty nodes, while the authors of [ 35 ] used a fog computing architecture to detect, filter, and correct abnormal sensed data. In addition, the authors of [ 36 ] presented a data intrusion detection system to trigger false data from malicious attacks; Network Trustworthiness: It can be defined as the probability that a packet will reach its destination unaltered despite the adversities (e.g., link failure, link saturation, malicious attacks), and it is a crucial factor of low-power and lossy networks (LLNs) [ 37 ]. Improving network trustworthiness and performance is a challenge that has been addressed from different perspectives such as transmission coding [ 38 , 39 , 40 , 41 ], load balancing and redundancy protocols [ 42 ], transport protocols [ 43 ], dynamic routing and topology control protocols [ 44 , 45 ], cybersecurity mechanisms [ 46 ], and delay tolerant network (DTN) architectures and protocols [ 47 ].…”

“…For instance, the authors of [ 34 ] presented a distributed Bayesian algorithm to detect faulty nodes, while the authors of [ 35 ] used a fog computing architecture to detect, filter, and correct abnormal sensed data. In addition, the authors of [ 36 ] presented a data intrusion detection system to trigger false data from malicious attacks;…”

“…When a potential abnormal value is detected, it is compared with the values sensed from the node’s neighbors, computing the group value similarity (G). Since this breaks our model’s assumption, this value similarity could be computed with the historical values from the sensor itself as in [ 36 ]. If the similarity is lower than a threshold t sim , then the abnormal data are confirmed and corrected (e.g., interpolation with previous and posterior correct values sensed by the own node).…”

A Survey of Context-Aware Access Control Mechanisms for Cloud and Fog Networks: Taxonomy and Open Research Issues

“…In recent years, different fog computing models have been introduced to reduce the latency and processing overheads involved in managing and accessing data resources from the cloud sources towards the edge of the end-devices (e.g., [44][45][46][47]). These fog nodes usually provide intermediary computation and networking services between the end-users and the traditional cloud data servers.…”

Security, Privacy, and Trustworthiness of Sensor Networks and Internet of Things

“…Paper [ 7 ], entitled “False Data Detection for Fog and Internet of Things Networks”, by Fantacci et al addresses the problem of attack detection in Fog/IoT environments. The authors propose an attack-detection method, named Data Intrusion Detection System (DataIDS), that is based on real-time analysis of physical (sensed) data.…”