Combining Statistical and Machine Learning Techniques in IoT Anomaly Detection for Smart Homes

Spanos, Georgios; Giannoutakis, Konstantinos M.; Votis, Konstantinos; Tzovaras, Dimitrios

doi:10.1109/camad.2019.8858490

Cited by 31 publications

(16 citation statements)

References 21 publications

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“…In contrast, distributed methods run directly on sensor nodes equipped with light computation capability. Most of these approaches require historical data samples to be kept [19,20] yes no yes no no yes [21,22] yes no no yes yes no [24][25][26] no yes no yes no yes [27,28] no yes yes no yes no Prior method [18], [23] no yes no yes no yes Proposed method no yes no yes yes yes in the sensor node, which has limited memory storage. In [27,28], a rule-based distributed fuzzy inference system for WSNs was proposed that combines both local and neighboring observations to identify the occurrence of events.…”

Section: B Detection Methodsmentioning

confidence: 99%

“…Anomaly detection in homogeneous WSNs has received much attention in the literature. Most of these methodologies [19][20][21][22][23][24][25] deploy multiple one-type sensors to detect abnormal nodes. In this case, the homogeneous device is analyzed based on the fact that neighboring same-type sensors are often correlated and tend to generate similar measurements.…”

Section: A Sensor Node Typesmentioning

confidence: 99%

“…In [20], the authors proposed a trust evaluation model that can detect the state of a node according to the data trust. In [21], the authors combined both statistical and machine learning techniques to detect anomalies in the network behavior of IoT devices. The solution is based on constructing behavioral device templates.…”

Section: A Sensor Node Typesmentioning

confidence: 99%

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A Spatiotemporal and Multivariate Attribute Correlation Extraction Scheme for Detecting Abnormal Nodes in WSNs

Berjab¹,

Yokota

2021

IEEE Access

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Many heterogeneous sensors exhibit a strong correlation in both space and time that can be used to enhance the abnormal node detection problem in a wireless sensor network (WSN). Corruption in these correlations has been shown effective in detecting false data injection attacks. However, extracting such correlations is considered a challenging task in WSNs. In this paper, we adopt a new crosscorrelation-based method to extract the sensor relationships. It utilizes the observed spatiotemporal (ST) and multivariate-attribute (MVA) sensor correlations to decide whether the sensor is subject to abnormalities or represents actual events. Based on the ST correlations, the cross-correlation is extracted in both space and time by conducting shape-based logical subclustering and two-phase analysis methods. In the first analysis phase, the system uses a variable-size sliding window and a median absolute deviation (MAD) measure. If the collected sensor data streams output a certain percentage of anomalous points, the MAD will flag these points as anomalous measurements, and all the sensor data and the window size will be passed to the second analysis phase. The latter performs both tumbling-window and sliding-window analyses to extract multicriteria cross-correlation measures. Finally, all the extracted sensor time-series features will be fed to the shape-based clustering to generate a sensor similarity-like graph. The latter reflects the similarity degree of the sensor with the other nodes. The nodes with a low similarity degree below the threshold will be identified as candidate abnormal nodes. Based on the observed MVA correlations, a set consisting of a few rules is introduced to check whether the detected candidate abnormal nodes represent actual events. Finally, if abnormal nodes exist, then such nodes are reported. Our experiments using two real-world datasets demonstrate that our proposed approach detects abnormal nodes with an average accuracy of 96.50%, an average precision of 88.69%, and a recall rate of 93.00%.

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Section: B Detection Methodsmentioning

confidence: 99%

Section: A Sensor Node Typesmentioning

confidence: 99%

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A Spatiotemporal and Multivariate Attribute Correlation Extraction Scheme for Detecting Abnormal Nodes in WSNs

Berjab¹,

Yokota

2021

IEEE Access

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“…In some papers, a home network is simulated to implement methods for better learning, and this information is used to try to find a priority sequence to the extent that some activities are omitted for better learning and to discuss the accuracy and effectiveness of the proposed methods, a comparison is made with the methods of learning user behavior by Hidden Markov Models (Yamauchi et al, 2020). In some papers, it has an approach similar to the proposed approach of this article, and it has been similar in terms of statistical studies, and this combination of statistical techniques with machine learning techniques is evident.. Those templates are being calculated by combining machine learning techniques and statistical techniques according to their network behaviors in a smart house, so in these method, statistical measurements sometimes are used after processing to build clusters (Fahad, Rajarajan, 2015, Spanos et al, 2019.…”

Section: Related Workmentioning

confidence: 96%

Activity Recognition for Ambient Sensing Data and Rule Based Anomaly Detection

Ardebili

Eken

Küçük

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. After a brief look at the smart home, we conclude that to have a smart home, and it is necessary to have an intelligent management center. In this article, We have tried to make it possible for the smart home management center to be able to detect the presence of an abnormal state in the behavior of someone who lives in the house. In the proposed method, the daily algorithm examines the rate of changes of a person and provides a number which is henceforth called NNC (Number of normal changes) based on the person’s behavioral changes. We achieve the NNC number using a machine learning algorithm and performing a series of several simple statistical and mathematical calculations. NNC is a number that shows abnormal changes in residents’ behaviors in a smart home, i.e., this number is a small number for a regular person with constant planning and for a person who may not have any fixed principles and regular in personal life is a big number.To increase our accuracy in calculating NNC, we review all common machine learning algorithms and after tests we choose the decision tree because of its higher accuracy and speed and finally, NNC number is obtained by combining the Decision Tree algorithm with statistical and mathematical methods. In this method, we present a set of states and information obtained from the sensors along with the activities performed by the occupant of the house over a period of several days to the proposed algorithm. and the method ahead generates the main NNC number for those days for anyone living in a smart home. To generate this main NNC, we calculate each person’s daily NNC. That means we have daily NNCs for each person (based on his/her behaviors on that day) and the main NNC is the average of these daily NNC. We chose ARAS dataset (Human Activity Datasets in Multiple Homes with Multiple Residents) to implement our method and after tests and replications on the ARAS dataset, and to find anomalies in each person’s behavior in a day, we compare the main (average) NNC with that person’s daily NNC on that day. Finally, we can say, if the main NNC changes more than 30%, there is a possibility of an abnormality. and if the NNC changes more than 60% percent, we can say that an abnormal state or an uncommon event happened that day, and a declaration of an abnormal state will be issued to the resident of the house.

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“…IoT-specific anomaly detection is a challenging area, because the solutions must be lightweight and capable of handling the heterogeneous range of IoT devices. Spanos et al (2019) proposed a smart-home anomaly detection method which combines statistical and machine learning techniques according the network behaviour of the device. During training, features are extracted from the network packet data, these features are then standardised and passed into a clustering algorithm.…”

Section: Anomaly Detectionmentioning

confidence: 99%

IoT reliability: a review leading to 5 key research directions

Moore¹,

Nugent²,

Zhang³

et al. 2020

CCF Trans. Pervasive Comp. Interact.

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The Internet of Things (IoT) is rapidly changing the way in which we engage with technology on a daily basis. The IoT paradigm enables low-resource devices to intercommunicate in a fully flexible and pervasive manner, and the data from these devices is used for decision-making in critical applications such as; traffic infrastructure, health-care and home security, to name but a few. Due to the scarce resources available in these IoT devices, being able to quantify the reliability of them is a critical function. This report presents a detailed evolution of the area of reliability measurement, followed by an in-depth review of the state-of-the-art for quantification of reliability in the IoT, revealing the many challenges associated with this task. From this in-depth review, a set of key research directions for IoT reliability is determined. Despite the critical nature of the research area, at this current moment, this study is the first detailed review available in the area of assessing IoT reliability.

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Combining Statistical and Machine Learning Techniques in IoT Anomaly Detection for Smart Homes

Cited by 31 publications

References 21 publications

A Spatiotemporal and Multivariate Attribute Correlation Extraction Scheme for Detecting Abnormal Nodes in WSNs

A Spatiotemporal and Multivariate Attribute Correlation Extraction Scheme for Detecting Abnormal Nodes in WSNs

Activity Recognition for Ambient Sensing Data and Rule Based Anomaly Detection

IoT reliability: a review leading to 5 key research directions

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