Unsupervised anomaly detection for unlabelled wireless sensor networks data

With the advances in sensor technology, sensor nodes, the tiny yet powerful device are used to collect data from the various domain. As the sensor nodes communicate continuously from the target areas to base station, hundreds of thousands of data are collected to be used for the decision making. Unfortunately, the big amount of unlabeled data collected and stored at the base station. In most cases, data are not reliable due to several reasons. Therefore, this paper will use the unsupervised one-class SVM (OCSVM) to build the anomaly detection schemes for better decision making. Unsupervised OCSVM is preferable to be used in WSNs domain due to the one class of data training is used to build normal reference model. Furthermore, the dimension reduction is used to minimize the resources usage due to resource constraint incurred in WSNs domain. Therefore one of the OCSVM variants namely Centered Hyper-ellipsoidal Support Vector Machine (CESVM) is used as classifier while Candid-Covariance Free Incremental Principal Component Analysis (CCIPCA) algorithm is served as dimension reduction for proposed anomaly detection scheme. Environmental dataset collected from available WSNs data is used to evaluate the performance measures of the proposed scheme. As the results, the proposed scheme shows comparable results for all datasets in term of detection rate, detection accuracy and false alarm rate as compared with other related methods

Distributed CESVM-DR anomaly detection for wireless sensor network

Nowadays, the advancement of the sensor technology, has introduced the smart living community where the sensor is communicating with each other or to other entities. This has introduced the new term called internet-of-things (IoT). The data collected from sensor nodes will be analyzed at the endpoint called based station or sink for decision making. Unfortunately, accurate data is not usually accurate and reliable which will affect the decision making at the base station. There are many reasons constituted to the inaccurate and unreliable data like the malicious attack, harsh environment as well as the sensor node failure itself. In a worse case scenario, the node failure will also lead to the dysfunctional of the entire network. Therefore, in this paper, an unsupervised one-class SVM (OCSVM) is used to build the anomaly detection schemes in recourse constraint Wireless Sensor Networks (WSNs). Distributed network topology will be used to minimize the data communication in the network which can prolong the network lifetime. Meanwhile, the dimension reduction has been providing the lightweight of the anomaly detection schemes. In this paper Distributed Centered Hyperellipsoidal Support Vector Machine (DCESVM-DR) anomaly detection schemes is proposed to provide the efficiency and effectiveness of the anomaly detection schemes

Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine

Wireless Sensors Networks have been the focus of significant attention from research and development due to their applications of collecting data from various fields such as smart cities, power grids, transportation systems, medical sectors, military, and rural areas. Accurate and reliable measurements for insightful data analysis and decision-making are the ultimate goals of sensor networks for critical domains. However, the raw data collected by WSNs usually are not reliable and inaccurate due to the imperfect nature of WSNs. Identifying misbehaviours or anomalies in the network is important for providing reliable and secure functioning of the network. However, due to resource constraints, a lightweight detection scheme is a major design challenge in sensor networks. This paper aims at designing and developing a lightweight anomaly detection scheme to improve efficiency in terms of reducing the computational complexity and communication and improving memory utilization overhead while maintaining high accuracy. To achieve this aim, oneclass learning and dimension reduction concepts were used in the design. The One-Class Support Vector Machine (OCSVM) with hyper-ellipsoid variance was used for anomaly detection due to its advantage in classifying unlabelled and multivariate data. Various One-Class Support Vector Machine formulations have been investigated and Centred-Ellipsoid has been adopted in this study due to its effectiveness. Centred-Ellipsoid is the most effective kernel among studies formulations. To decrease the computational complexity and improve memory utilization, the dimensions of the data were reduced using the Candid Covariance-Free Incremental Principal Component Analysis (CCIPCA) algorithm. Extensive experiments were conducted to evaluate the proposed lightweight anomaly detection scheme. Results in terms of detection accuracy, memory utilization, computational complexity, and communication overhead show that the proposed scheme is effective and efficient compared few existing schemes evaluated. The proposed anomaly detection scheme achieved the accuracy higher than 98%, with O(nd) memory utilization and no communication overhead

Islamic web pages filtering and categorization

The Internet creates the world without boundaries where people can get lots of information just by surfing the Internet. But still some of the information is not genuine and correct. Because of that, some of the practitioners of deviant teachings can take this opportunity to attract followers just using the Internet especially to distort beliefs of Muslim in Malaysia. Web filtering can be used as protection against inappropriate and prevention of misuse of the network, hence, it can be used to filter the content of suspicious websites and alleviate the dissemination of such website. Currently, process for blocking the deviate teaching website is done manually and in addition there are limited web filtering product offered to filter religion content and very limited for Malay language. This project is aim to classify deviant teachings Website into three categories which is deviate, suspicious and clean. Pre-processing, feature selection and classification are process involved in Web filtering process. In pre-processing three processes are involved: HTML parsing, stemming and stopping to produce the deviant teaching keyword. Three existing term weighting scheme namely TF, TFIDF and Modified Entropy are used as feature selection process in filtering deviant teaching website while Support Vector Machine (SVM) will be used for classification process. Classification is validated by accuracy, precision, recall and F1. 300 Web pages were collected from Internet based on three categories: deviant teaching, suspicious and clean Web pages. As a result, M.Entropy shows the most suitable term weighting scheme to use in Islamic web pages filtering rather than TFIDF and Entropy

Selection of soil features for detection of ganoderma using rough set theory

Ganoderma boninense (G. boninense) is one of the critical palm oil diseases that have caused major loss in palm oil production, especially in Malaysia. Current detection methods are based on molecular and non-molecular approaches. Unfortunately, both are expensive and time consuming. Meanwhile, wireless sensor networks (WSNs) have been successfully used in precision agriculture and have a potential to be deployed in palm oil plantation. The success of using WSN to detect anomalous events in other domain reaffirms that WSN could be used to detect the presence of G. boninense, since WSN has some resource constraints such as energy and memory. This paper focuses on feature selection to ensure only significant and relevant data that will be collected and transmitted by the sensor nodes. Sixteen soil features have been collected from the palm oil plantation. This research used rough set technique to do feature selection. Few algorithms were compared in terms of their classification accuracy, and we found that genetic algorithm gave the best combination of feature subset to signify the presence of Ganoderma in soil

Inspiring wireless sensor network from brain connectome

Wireless Sensor Networks (WSN) are usually large - scale self - organized networks that can dynamically change with no pre - established infrastructure or a topology. In order to inference information from it, data collected by different sensors should be aggreg ated, known as Data Fusion (FC). This can happen in a centralized mode by broadcasting all data to a FC or in a distributed way. The centralized approach needs high communication bandwidth and transmission power, which is usually lacking due to limited cap abilities of sensor nodes. In distributed processing, instead of transmitting all the data to a FC in order to accomplish the final goal of the network, each sensor should rely only on local information received by itself and the sen sors in its vicinity. O n the other hand, relying only on the information received by a single sensor (or a small group of them) might not necessarily lead to the overall precision required by the network. Thus, appropriate information sharing and collaborative processing algorit hms should also be put in place to make sure of reliable inferencing. Distributed processing makes large - scale sensor networking possible by striking a proper trade - off between performance and resource utilization. The proposed methodology in this research is to use the idea of sparse structures which the best example of it, is human brain network of neurons known as connectome. Many studies demonstrate inferencing reliability (performance) and energy efficiency (resource utilization) of connectome. In this research a review of the possibility of using brain connectome in wireless sensor network design has been presented