In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

Review: Intrusion detection system: A comprehensive review

Multivariate data / Mathematical and statistical background / Exploring multivariate data graphically / Exploratory factor analysis / Correspondence analysis / Multidimensional scaling / Cluster analysis / Latent class analysis / Discriminant function analysis / Multivariate dependencies / Classical multivariate inference / Longitudinal data / Problems of measurement error / Confirmatory factor analysis and structural equation models.

Applied Multivariate Data Analysis

Cooperative intelligent transportation systems (cITS) are a promising technology to enhance driving safety and efficiency. Vehicles communicate wirelessly with other vehicles and infrastructure, thereby creating a highly dynamic and heterogeneously managed ad-hoc network. It is these network properties that make it a challenging task to protect integrity of the data and guarantee its correctness. A major component is the problem that traditional security mechanisms like public key infrastructure (PKI)-based asymmetric cryptography only exclude outsider attackers that do not possess key material. However, because attackers can be insiders within the network (i.e., possess valid key material), this approach cannot detect all possible attacks. In this survey, we present misbehavior detection mechanisms that can detect such insider attacks based on attacker behavior and information analysis. In contrast to well-known intrusion detection for classical IT systems, these misbehavior detection mechanisms analyze information semantics to detect attacks, which aligns better with highly application-tailored communication protocols foreseen for cITS. In our survey, we provide an extensive introduction to the cITS ecosystem and discuss shortcomings of PKI-based security. We derive and discuss a classification for misbehavior detection mechanisms, provide an in-depth overview of seminal papers on the topic, and highlight open issues and possible future research trends.

Survey on Misbehavior Detection in Cooperative Intelligent Transportation Systems

Wireless Sensor Networks (WSN) have become increasingly one of the hottest research areas in computer science due to their wide range of applications including critical military and civilian applications. Such applications have created various security threats, especially in unattended environments. To ensure the security and dependability of WSN services, an Intrusion Detection System (IDS) should be in place. This IDS has to be compatible with the characteristics of WSNs and capable of detecting the largest possible number of security threats. In this paper a specialized dataset for WSN is developed to help better detect and classify four types of Denial of Service (DoS) attacks: Blackhole, Grayhole, Flooding, and Scheduling attacks. This paper considers the use of LEACH protocol which is one of the most popular hierarchical routing protocols in WSNs. A scheme has been defined to collect data from Network Simulator 2 (NS-2) and then processed to produce 23 features. The collected dataset is called WSN-DS. Artificial Neural Network (ANN) has been trained on the dataset to detect and classify different DoS attacks. The results show that WSN-DS improved the ability of IDS to achieve higher classification accuracy rate. WEKA toolbox was used with holdout and 10-Fold Cross Validation methods. The best results were achieved with 10-Fold Cross Validation with one hidden layer. The classification accuracies of attacks were 92.8%, 99.4%, 92.2%, 75.6%, and 99.8% for Blackhole, Flooding, Scheduling, and Grayhole attacks, in addition to the normal case (without attacks), respectively.

/pdf/wsn-ds-a-dataset-for-intrusion-detection-systems-in-wireless-1tculttf4v.pdf

WSN-DS: A Dataset for Intrusion Detection Systems in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are important and necessary platforms for the future as the concept “Internet of Things” has emerged lately. They are used for monitoring, tracking, or controlling of many applications in industry, health care, habitat, and military. However, the quality of data collected by sensor nodes is affected by anomalies that occur due to various reasons, such as node failures, reading errors, unusual events, and malicious attacks. Therefore, anomaly detection is a necessary process to ensure the quality of sensor data before it is utilized for making decisions. In this review, we present the challenges of anomaly detection in WSNs and state the requirements to design efficient and effective anomaly detection models. We then review the latest advancements of data anomaly detection research in WSNs and classify current detection approaches in five main classes based on the detection methods used to design these approaches. Varieties of the state-of-the-art models for each class are covered and their limitations are highlighted to provide ideas for potential future works. Furthermore, the reviewed approaches are compared and evaluated based on how well they meet the stated requirements. Finally, the general limitations of current approaches are mentioned and further research opportunities are suggested and discussed.

https://www.mdpi.com/1424-8220/13/8/10087/pdf

Advancements of Data Anomaly Detection Research in Wireless Sensor Networks: A Survey and Open Issues

Vehicular ad hoc network (VANET) is the key enabler for future intelligent transportation systems' applications Due to its high mobility, VANETs rely on the availability of accurate and reliable mobility information of the vehicles However, misbehavior in mobility can lead to catastrophic results in both safety and traffic efficiency Several drawbacks of existing misbehavior detection models designed for VANETs which impacted the performance of the applications and the security solutions altogether Machine learning has not been studied extensively in misbehavior detection in VANET In this paper, an effective misbehavior detection model based on machine learning techniques is proposed The proposed model consists of four main phases: data acquisition, data sharing, analysis and decision making New features are derived which represent the misbehavior, environment and communication status in order to effectively detect the misbehavior data By using Artificial Neural Network (ANN) techniques namely the feed forward and the back propagation algorithms, an effective misbehavior classifier is trained based on historical data that contains both attacker and normal traffic data A real-world traffic dataset namely NGSIM is used to construct and evaluate the proposed model Results show significant improvement in the effectiveness of the proposed model in comparison with the existing baseline model

An effective misbehavior detection model using artificial neural network for vehicular ad hoc network applications

Harmony search algorithm (HSA) is one of the relatively new metaheuristic algorithms that classified under population-based search algorithms. Based on literature, hybridizing local-based searching algorithms with population-based algorithms can improve the performance of hybridized algorithms. This research is an extension to our previous work that focus on solving Nurse Rostering Problems (NRP) using hybrid metaheuristic algorithms. One of the improved version of HSA is enhanced harmony search algorithm (EHSA) where it overcomes some of the weaknesses of basic HSA. Slow convergence is noticed in EHSA which encourage us to hybridize it with other metaheuristic algorithms to improve its performance. In this research, EHSA is hybridized with great deluge algorithm (GD) and called Deluged harmony search algorithm (DHSA). DHSA then compared to CHSA (the hybridization of EHSA with Hill climbing (HC)) which developed earlier. To strike the balance between exploration and exploitation, the exploration stage run using EHSA and the exploitation stage used GD. DHSA is tested to solve a real world NRP problem at National University Malaysia Medical Center (UKMMC). The results show that, DHSA performed much better than CHSA in all instances in terms of solution quality with slightly higher execution time.

/pdf/deluge-harmony-search-algorithm-for-nurse-rostering-problems-4pfuxwwrvw.pdf

Deluge Harmony Search Algorithm For Nurse Rostering Problems

Wireless Sensor Networks (WSNs) are currently used in many application areas including military applications, health related applications, control and tracking applications and environment and habitat monitoring applications. The harsh and unattended deployment of these networks along with their resource restrictions makes their security issue very important. Prevention-based security approaches like cryptography, authentication and key management have been used to protect WSNs from different kinds of attacks but these approaches are not enough to protect the network from insider attacks that may extract sensitive information even in the presence of the prevention-based solution. Detection-based approaches are then proposed to protect WSNs from insider attacks and act as a second line defense after the failure of the prevention-based approaches. Many intrusion detection schemes have been introduced for WSN in the literature. In this article, we present a survey of intrusion detection schemes in WSNs. First, we present the similar works and show their differences from this work. After that, we outline the fundamentals of intrusion detection in WSNs, describing the types of attacks and state the motivation for intrusion detection in WSNs. Then, we demonstrate the challenges of developing an ideal intrusion detection scheme for WSNs followed by the main requirements of a good candidate intrusion detection scheme. The state-of-the-art intrusion detection schemes are then presented based on the techniques used in each scheme and categorizing them into four main categories: rule-based, data mining and computational intelligence based, game theoretical based and statistical based. The analysis of each scheme in these categories is presented showing their advantages and drawbacks. By the end of each category, we state the general advantages and shortcomings of each category. The survey ends by recommending some important research opportunities in this field for future research.

/pdf/a-survey-of-intrusion-detection-schemes-in-wireless-sensor-sy4q1cyrsk.pdf

A Survey of Intrusion Detection Schemes in Wireless Sensor Networks

Wireless sensor networks (WSNs) are increasingly used as platforms for collecting data from unattended environments and monitoring important events in phenomena. However, sensor data is affected by anomalies that occur due to various reasons, such as, node software or hardware failures, reading errors, unusual events, and malicious attacks. Therefore, effective, efficient, and real time detection of anomalous measurement is required to guarantee the quality of data collected by these networks. In this paper, two efficient and effective anomaly detection models PCCAD and APCCAD are proposed for static and dynamic environments, respectively. Both models utilize the One-Class Principal Component Classifier (OCPCC) to measure the dissimilarity between sensor measurements in the feature space. The proposed APCCAD model incorporates an incremental learning method that is able to track the dynamic normal changes of data streams in the monitored environment. The efficiency and effectiveness of the proposed models are demonstrated using real life datasets collected by real sensor network projects. Experimental results show that the proposed models have advantages over existing models in terms of efficient utilization of sensor limited resources. The results further reveal that the proposed models achieve better detection effectiveness in terms of high detection accuracy with low false alarms especially for dynamic environmental data streams compared to some existing models.

Murad A. Rassam

Papers

Advancements of Data Anomaly Detection Research in Wireless Sensor Networks: A Survey and Open Issues

An effective misbehavior detection model using artificial neural network for vehicular ad hoc network applications

Deluge Harmony Search Algorithm For Nurse Rostering Problems

A Survey of Intrusion Detection Schemes in Wireless Sensor Networks

Adaptive and online data anomaly detection for wireless sensor systems