Machine learning approach for detection of nonTor traffic

Intrusion detection has attracted a considerable interest from researchers and industry. After many years of research the community still faces the problem of building reliable and efficient intrusion detection systems (IDS) capable of handling large quantities of data with changing patterns in real time situations. The Tor network is popular in providing privacy and security to end user by anonymizing the identity of internet users connecting through a series of tunnels and nodes. This work identifies two problems; classification of Tor traffic and nonTor traffic to expose the activities within Tor traffic that minimizes the protection of users in using the UNB-CIC Tor Network Traffic dataset and classification of the Tor traffic flow in the network. This paper proposes a hybrid classifier; Artificial Neural Network in conjunction with Correlation feature selection algorithm for dimensionality reduction and improved classification performance. The reliability and efficiency of the propose hybrid classifier is compared with Support Vector Machine and naïve Bayes classifiers in detecting nonTor traffic in UNB-CIC Tor Network Traffic dataset. Experimental results show the hybrid classifier, ANN-CFS proved a better classifier in detecting nonTor traffic and classifying the Tor traffic flow in UNB-CIC Tor Network Traffic dataset

PCA-enhanced methodology for the identification of partial discharge locations

Partial discharge (PD) that occurs due to insulation breakdown is a precursor to plant failure. PD emits electromagnetic pulses which radiate through space and can be detected using appropriate sensing devices. This paper proposed an enhanced radiolocation technique to locate PD. This approach depends on sensing the radio frequency spectrum and the extraction of PD location features from PD signals. We hypothesize that the statistical characterization of the received PD signals generates many features that represent distinct PD locations within a substation. It is assumed that the waveform of the received signal is altered due to attenuation and distortion during propagation. A methodology for the identification of PD locations based on extracted signal features has been developed using a fingerprint matching algorithm. First, the original extracted signal features are used as inputs to the algorithm. Secondly, Principal Component Analysis (PCA) is used to improve PD localization accuracy by transforming the original extracted features into s new informative feature subspace (principal components) with reduced dimensionality. The few selected PCs are then used as inputs into the algorithm to develop a new PD localization model. This work has established that PCA can provide robust PC representative features with spatially distinctive patterns, a prerequisite for a good fingerprinting localization model. The results indicate that the location of a discharge can be determined from the selected PCs with improved localization accuracy compared to using the original extracted PD features directly

Low complexity wireless sensor system for partial discharge localisation

This study describes a key element of any modern wireless sensor system: data processing. The authors describe a system consisting of a wireless sensor network and an algorithmic software for condition-based monitoring of electrical plant in a live substation. Specifically, the aim is to monitor for the presence of partial discharge (PD) using a matrix of inexpensive radio sensors with limited processing capability. A low-complexity fingerprinting technique is proposed, given that the sensor nodes to be deployed will be highly constrained in terms of processing power, memory and battery life. Two variants of artificial neural network (ANN) learning models (multilayer perceptron and generalised regression neural network) that use regression as a form of function approximation are developed and their performance compared to K-nearest neighbour and weighted K-nearest neighbour models. The results indicate that the ANN models yield superior performance in terms of robustness against noise and may be particularly suited for PD localisation

Threat analysis of IoT networks using artificial neural network intrusion detection system

The Internet of things (IoT) network is still in its infancy and has attracted much interest in many industrial sectors including medical fields, logistics tracking, smart cities and automobiles. However as a paradigm, it is susceptible to a range of significant intrusion threats. This paper presents a threat analysis of the IoT and uses an Artificial Neural Network (ANN) to combat these threats. A multi-level perceptron, a type of supervised ANN, is trained using an IoT Data set, then is assessed on its ability to thwart Distributed Denial of Service (DDoS/DoS) attacks. This paper focuses on the classification of normal and threat patterns on an IoT Network. The ANN procedure is validated against a simulated IoT network. The experimental results demonstrate 99.4% accuracy and can successfully detect various DDoS/DoS attacks

Improving RF-based partial discharge localization via machine learning ensemble method

Partial discharge (PD) is regarded as a precursor to plant failure and therefore, an effective indication of plant condition. Locating the source of PD before failure is key to efficient maintenance and improving reliability of power systems. This paper presents a low cost, autonomous partial discharge radiolocation mechanism to improve PD localization precision. The proposed radio frequency-based technique uses the wavelet packet transform (WPT) and machine learning ensemble methods to locate PDs. More specifically, the received signals are decomposed by the WPT and analyzed in order to identify localized PD signal patterns in the presence of noise. The regression tree algorithm, bootstrap aggregating method, and regression random forest are used to develop PD localization models based on the WPT-based PD features. The proposed PD localization scheme has been found to successfully locate PD with negligible error. Additionally, the principle of the PD location scheme has been validated using a separate test dataset. Numerical results demonstrate that the WPT-random forest PD localization scheme produced superior performance as a result of its robustness against noise

Radio Location of Partial Discharge Sources: A Support Vector Regression Approach

Partial discharge (PD) can provide a useful forewarning of asset failure in electricity substations. A significant proportion of assets are susceptible to PD due to incipient weakness in their dielectrics. This paper examines a low cost approach for uninterrupted monitoring of PD using a network of inexpensive radio sensors to sample the spatial patterns of PD received signal strength. Machine learning techniques are proposed for localisation of PD sources. Specifically, two models based on Support Vector Machines (SVMs) are developed: Support Vector Regression (SVR) and Least-Squares Support Vector Regression (LSSVR). These models construct an explicit regression surface in a high dimensional feature space for function estimation. Their performance is compared to that of artificial neural network (ANN) models. The results show that both SVR and LSSVR methods are superior to ANNs in accuracy. LSSVR approach is particularly recommended as practical alternative for PD source localisation due to it low complexity

Machine learning enhanced radio location of partial discharge

Partial Discharge (PD) is a well-known indicator of plant failure in electricity facilities. A considerable proportion of assets including transformers, switch gears, and power lines are susceptible to PD due to incipient weakness of their dielectric components. These discharges may cause further degradation of the insulation, which in turn may lead to subsequent catastrophic failure. The damage that results from PD activity is worth millions of pounds and endangers the lives of personnel. PD emits electrical pulses in the form of Radio Frequency (RF) signals which propagate as a travelling wave in the vicinity of the discharge site and can be detected using dedicated sensors. This has motivated the use of an enhanced radio-based technique to detect its occurrence at early stage. Early detection of PD helps utility operators to initiate an emergency maintenance outside the scheduled times when it is most cost-effective and before the equipment loses performance or suffers catastrophic failure, hence improving asset management. Therefore this thesis presents an investigation of an enhanced machine learning approach to continuous PD localisation using a network of radio sensors. The approach being investigated relies on location dependent parameters which will be extracted from PD measurements. This thesis demonstrates RF-based fingerprinting technique for locating PD sources using Received Signal Strength (RSS). Furthermore, Signal Strength Ratios (SSR) between pairs of sensor nodes are used as robust fingerprints given that the energy emitted by each PD event may be different due to progressive nature of PD severity as deterioration continues and the fact that different types of PD occur in nature. Sophisticated machine learning techniques are investigated and used to develop PD localisation models. This work also investigates the plausibility of using other PD received signal parameters for locating PD sources. It has been found that the statistical characterisation of the received RF signals produces manifold PD features beside RSS. The developed localisation approach based on the analysis of these statistical features assumes that PDs generate unique RF spatial patterns due to the complexities and non-linearities of RF propagation. This approach exploits two distinct frequency bands which hold different PD information. PD location features are extracted from the main PD signal and the two sub-band signals. These features are then used to infer PD location. Moreover, due to the increased dimensionality of data that may result from PD feature generation, feature selection algorithm; Correlation Based Feature Selection (CFS) is employed for feature selection and dimensionality reduction. The use of statistical PD features improves localisation accuracy. This study further presents a novel method for RF-based PD localisation. The technique uses Wavelet Packet Transform (WPT) and machine learning ensemble methods to locate PDs. More specifically, the received signals are decomposed by the Maximal Overlap Discrete Wavelet Packet Transform (MODWPT) version of wavelet packet and analysed in order to identify localised PD signal patterns. The Regression Tree algorithm, Bootstrap Aggregating method and Regression Random Forest (RRF) are used to develop PD localisation models based on the wavelet PD features. The proposed PD localisation scheme has been found to successfully locate PD with negligible error. Additionally, the principle of the developed PD localisation system has been validated using a separate test dataset. This approach is based on purely practical reasons, given the enormity of separate experiments to be carried out. The data required is collated over an extended time period. The results of the investigation presented in this thesis show that an autonomous andefficient substation-wide RF-based continuous PD localisation system is possible.Partial Discharge (PD) is a well-known indicator of plant failure in electricity facilities. A considerable proportion of assets including transformers, switch gears, and power lines are susceptible to PD due to incipient weakness of their dielectric components. These discharges may cause further degradation of the insulation, which in turn may lead to subsequent catastrophic failure. The damage that results from PD activity is worth millions of pounds and endangers the lives of personnel. PD emits electrical pulses in the form of Radio Frequency (RF) signals which propagate as a travelling wave in the vicinity of the discharge site and can be detected using dedicated sensors. This has motivated the use of an enhanced radio-based technique to detect its occurrence at early stage. Early detection of PD helps utility operators to initiate an emergency maintenance outside the scheduled times when it is most cost-effective and before the equipment loses performance or suffers catastrophic failure, hence improving asset management. Therefore this thesis presents an investigation of an enhanced machine learning approach to continuous PD localisation using a network of radio sensors. The approach being investigated relies on location dependent parameters which will be extracted from PD measurements. This thesis demonstrates RF-based fingerprinting technique for locating PD sources using Received Signal Strength (RSS). Furthermore, Signal Strength Ratios (SSR) between pairs of sensor nodes are used as robust fingerprints given that the energy emitted by each PD event may be different due to progressive nature of PD severity as deterioration continues and the fact that different types of PD occur in nature. Sophisticated machine learning techniques are investigated and used to develop PD localisation models. This work also investigates the plausibility of using other PD received signal parameters for locating PD sources. It has been found that the statistical characterisation of the received RF signals produces manifold PD features beside RSS. The developed localisation approach based on the analysis of these statistical features assumes that PDs generate unique RF spatial patterns due to the complexities and non-linearities of RF propagation. This approach exploits two distinct frequency bands which hold different PD information. PD location features are extracted from the main PD signal and the two sub-band signals. These features are then used to infer PD location. Moreover, due to the increased dimensionality of data that may result from PD feature generation, feature selection algorithm; Correlation Based Feature Selection (CFS) is employed for feature selection and dimensionality reduction. The use of statistical PD features improves localisation accuracy. This study further presents a novel method for RF-based PD localisation. The technique uses Wavelet Packet Transform (WPT) and machine learning ensemble methods to locate PDs. More specifically, the received signals are decomposed by the Maximal Overlap Discrete Wavelet Packet Transform (MODWPT) version of wavelet packet and analysed in order to identify localised PD signal patterns. The Regression Tree algorithm, Bootstrap Aggregating method and Regression Random Forest (RRF) are used to develop PD localisation models based on the wavelet PD features. The proposed PD localisation scheme has been found to successfully locate PD with negligible error. Additionally, the principle of the developed PD localisation system has been validated using a separate test dataset. This approach is based on purely practical reasons, given the enormity of separate experiments to be carried out. The data required is collated over an extended time period. The results of the investigation presented in this thesis show that an autonomous andefficient substation-wide RF-based continuous PD localisation system is possible

RF-based location of partial discharge sources using received signal features

Partial discharges (PDs) are symptomatic of some localised defects in the insulation system of electrical equipment. PD activity emits electrical pulses in the form of radio frequency (RF) signals which can be captured using appropriate sensors. The analysis of the measured RF signals facilitates localisation of PD. This study investigates the plausibility of using purely RF received signal features of PD pulses to locate PD at low cost. A localisation approach based on the analysis of these features has been developed, with the assumption that PDs generate unique RF spatial patterns due to the complexities and nonlinearities of RF propagation. In this approach, two distinct frequency bands which hold different PD information are exploited. PD location features are extracted from the main PD signal and the two sub-band signals. Correlation-based feature selection (CFS) is employed for feature selection and dimensionality reduction. Experimental results show that PD location can be inferred from the features of the PD pulses. The application of CFS to PD data reduces the memory/computational demand and improves localisation accuracy

LoRaWAN-implemented node localisation in a sandstorm environment based on received signal strength indicator

Long Range Wireless Area Network (LoRaWAN) provides desirable solutions for Internet of Things (IoT) applications that require hundreds or thousands of actively connected devices (nodes) to monitor the environment or processes. In most cases, the location information of the devices arguably plays a critical role and is desirable. In this regard, the physical characteristics of the communication channel can be leveraged to provide a feasible and affordable node localisation solution. This paper presents an evaluation of the performance of LoRaWAN Received Signal Strength Indicator (RSSI)-based node localisation in a sandstorm environment. We employ machine learning algorithms - Support Vector Regression (SVR) and Gaussian Process Regression (GPR), which turns the high variance of RSSI due to frequency hopping feature of LoRaWAN to advantage; creating unique signatures representing different locations. In this work, the RSSI features are used as input location fingerprints into the machine learning models. The proposed method reduces node localisation complexity when compared to GPS-based approaches whilst provisioning more extensive connection paths. Furthermore, the impact of LoRa spreading factor and kernel function on the performance of the developed models have been studied. Experimental results show that the SVR-enhanced fingerprint yields the most significant improvement in node localisation performance