Detecting changes at the sensor level in cyber-physical systems: Methodology and technological implementation

Alippi, Cesare; D'Alto, V.; Falchetto, Mirko; Pau, Danilo; Roveri, Manuel

doi:10.1109/ijcnn.2017.7966066

Cited by 12 publications

(5 citation statements)

References 19 publications

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“…This study discusses machine learning frameworks for DDoS attack detection in SD-IoT networks [43]Toward Software-Defined Networking-Based IoT Frameworks: A Systematic Literature Review, Taxonomy, Open Challenges, and Prospects [44], discusses machine learning frameworks for DDoS attack detection in SD-IoT networks. For IoT-based smart home environments, Alippi et al [26] presented a framework for detecting changes at the level of sensors. The approach constructs a dynamical model of the anticipated sensor-generated signal over time [22].…”

Section: Background and Literature Reviewmentioning

confidence: 99%

IoT Network Anomaly Detection in Smart Homes Using Machine Learning

Sarwar,

Bajwa,

Hussain

et al. 2023

IEEE Access

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In this modern age of technology, the Internet of things has covered all aspects of life including smart situations, smart homes, and smart spaces. Smart homes have a large number of IoT objects that are working continuously without any interruption. Better security and authentication of these smart devices can provide peaceful environments to live in such spaces. It is important to monitor the activities of smart IoT devices to make them work fault free. Such devices are small, consume relatively less power and resources, and are easily attackable by attackers. It is crucial to protect the integrity and characteristics of the smart home environment from external attacks. Machine Learning played a vital role in recognizing such malicious activities and attempts. Several Machine Learning approaches are available to detect the normal and abnormal behavior of IoT device traffic. This study proposed a machine learning-based anomaly detection approach for smart homes using different classifiers. Testing and evaluation are performed using the University of New South Wales (UNSW) BoT IoT dataset. Machine learning models based on four classifiers are built using an IoT devices dataset. For the Test dataset, the Weighted Precision, Recall and F1 score of Random forest, decision tree, and AdaBoost is 1 as compared to ANN which has 0.98, 0.96, and 0.96 respectively Results show that high performance, precision, and robustness can be achieved using the proposed methodology. In this way, smart homes' security and identity of devices can be monitored and anomalies can be detected with high accuracy. Attack categories include binary class, multiclass class, and subclasses. Results show Random Forest algorithm outperforms enough to use this methodology in smart environments.

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Section: Background and Literature Reviewmentioning

confidence: 99%

IoT Network Anomaly Detection in Smart Homes Using Machine Learning

Sarwar,

Bajwa,

Hussain

et al. 2023

IEEE Access

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“…In addition, embedded sensors can capture different types of data to guarantee that the manufacturing execution system is fully aware of the processes [12]. More details of detecting changes at sensor levels can be found in [13].…”

Section: Exception Handling In Manufacturing Systemsmentioning

confidence: 99%

ResourceNet: a collaboration network among decentralised manufacturing resources for autonomous exception‐handling in smart manufacturing

Qian

Sun

Zhang

2020

IET Collaborative Intelligent Manufacturing

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“…We emphasize that, in both cases, the change-detection phase is carried out for sensors for which Ci≠∅, i.e., the sensor must be related to at least one of the other sensors in U according to the dependency graph. When Ci=∅, as in the case of sensors u6 and u10, the analysis based on cross correlation cannot be considered and one could resort on change-detection analysis based on inspection of the residual between the output of a suitably trained prediction model (e.g., linear input–output models or recurrent neural networks) on the acquired data (see, for example, Reference [21]).…”

Section: Dataidsmentioning

confidence: 99%

False Data Detection for Fog and Internet of Things Networks

Fantacci

Nizzi

Pecorella

et al. 2019

Sensors

Self Cite

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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.

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Detecting changes at the sensor level in cyber-physical systems: Methodology and technological implementation

Cited by 12 publications

References 19 publications

IoT Network Anomaly Detection in Smart Homes Using Machine Learning

IoT Network Anomaly Detection in Smart Homes Using Machine Learning

ResourceNet: a collaboration network among decentralised manufacturing resources for autonomous exception‐handling in smart manufacturing

False Data Detection for Fog and Internet of Things Networks

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