Anomaly detection in wireless sensor networks
Wireless Sensor Network can be defined as a network of integrated sensors responsible for environmental sensing, data processing and communication with other sensors and the base station while consuming low power. Today, WSNs are being used in almost every part of life. The cost effective nature of WSNs is beneficial for environmental monitoring, production facilities and security monitoring. At the same time WSNs are vulnerable to security breaches, attacks and information leakage. Anomaly detection techniques are used to detect such activities over the network that do not conform to the normal behavior of the network communication. Supervised Machine learning approach is one way to detect anomalies where a normal model is developed with known responses called labels and this model is tested against new data sets. We experimented Supervised Machine Learning approach for the labelled sensor data set of Humidity and Temperature and the results show that KNN (K Nearest Neighbor) proves to be the best anomaly detection algorithm for this data set.
...
Asiasanat
Metadata
Näytä kaikki kuvailutiedotKokoelmat
- Pro gradu -tutkielmat [28130]
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
-
Analysis and Evaluation of Adaptive RSSI-based Ranging in Outdoor Wireless Sensor Networks
Luomala, Jari; Hakala, Ismo (Elsevier, 2019)Estimating inter-node distances based on received radio signal strength (RSSI) is the foundation of RSSI-based outdoor localization in wireless sensor networks (WSNs). However, the accuracy of RSSI-based ranging depends ... -
Adaptive range-based localization algorithm based on trilateration and reference node selection for outdoor wireless sensor networks
Luomala, Jari; Hakala, Ismo (Elsevier BV, 2022)Locating the nodes of outdoor wireless sensor networks (WSNs) using (tri)lateration with a low-cost ranging technique, such as the received signal strength indicator (RSSI), often results in inaccurate location estimates. ... -
Piecewise anomaly detection using minimal learning machine for hyperspectral images
Raita-Hakola, A.-M.; Pölönen, I. (Copernicus Publications, 2021)Hyperspectral imaging, with its applications, offers promising tools for remote sensing and Earth observation. Recent development has increased the quality of the sensors. At the same time, the prices of the sensors are ... -
Evaluation of Ensemble Machine Learning Methods in Mobile Threat Detection
Kumar, Sanjay; Viinikainen, Ari; Hämäläinen, Timo (Infonomics Society, 2017)The rapid growing trend of mobile devices continues to soar causing massive increase in cyber security threats. Most pervasive threats include ransom-ware, banking malware, premium SMS fraud. The solitary hackers use ... -
A method for anomaly detection in hyperspectral images, using deep convolutional autoencoders
Penttilä, Jeremias (2017)Menetelmä poikkeavuuksien havaitsemiseen hyperspektrikuvista käyttäen syviä konvolutiivisia autoenkoodereita. Poikkeavuuksien havaitseminen kuvista, erityisesti hyperspektraalisista kuvista, on hankalaa. Kun ongelmaan ...
Ellei toisin mainittu, julkisesti saatavilla olevia JYX-metatietoja (poislukien tiivistelmät) saa vapaasti uudelleenkäyttää CC0-lisenssillä.