A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets

The term "outlier" can generally be defined as an observation that is significantly different from the other values in a data set. The outliers may be instances of error or indicate events. The task of outlier detection aims at identifying such outliers in order to improve the analysis of data and further discover interesting and useful knowledge about unusual events within numerous applications domains. In this paper, we report on contemporary unsupervised outlier detection techniques for multiple types of data sets and provide a comprehensive taxonomy framework and two decision trees to select the most suitable technique based on data set. Furthermore, we highlight the advantages, disadvantages and performance issues of each class of outlier detection techniques under this taxonomy framework

Web usage mining for click fraud detection

Estágio realizado na AuditMark e orientado pelo Eng.º Pedro FortunaTese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

Denial of Service in Web-Domains: Building Defenses Against Next-Generation Attack Behavior

The existing state-of-the-art in the field of application layer Distributed Denial of Service (DDoS) protection is generally designed, and thus effective, only for static web domains. To the best of our knowledge, our work is the first that studies the problem of application layer DDoS defense in web domains of dynamic content and organization, and for next-generation bot behaviour. In the first part of this thesis, we focus on the following research tasks: 1) we identify the main weaknesses of the existing application-layer anti-DDoS solutions as proposed in research literature and in the industry, 2) we obtain a comprehensive picture of the current-day as well as the next-generation application-layer attack behaviour and 3) we propose novel techniques, based on a multidisciplinary approach that combines offline machine learning algorithms and statistical analysis, for detection of suspicious web visitors in static web domains. Then, in the second part of the thesis, we propose and evaluate a novel anti-DDoS system that detects a broad range of application-layer DDoS attacks, both in static and dynamic web domains, through the use of advanced techniques of data mining. The key advantage of our system relative to other systems that resort to the use of challenge-response tests (such as CAPTCHAs) in combating malicious bots is that our system minimizes the number of these tests that are presented to valid human visitors while succeeding in preventing most malicious attackers from accessing the web site. The results of the experimental evaluation of the proposed system demonstrate effective detection of current and future variants of application layer DDoS attacks

Multi-Sensor Data Fusion for Travel Time Estimation

The importance of travel time estimation has increased due to the central role it plays in a number of emerging intelligent transport systems and services including Advanced Traveller Information Systems (ATIS), Urban Traffic Control (UTC), Dynamic Route Guidance (DRG), Active Traffic Management (ATM), and network performance monitoring. Along with the emerging of new sensor technologies, the much greater volumes of near real time data provided by these new sensor systems create opportunities for significant improvement in travel time estimation. Data fusion as a recent technique leads to a promising solution to this problem. This thesis presents the development and testing of new methods of multi-sensor data fusion for the accurate, reliable and robust estimation of travel time. This thesis reviews the state-of-art data fusion approaches and its application in transport domain, and discusses both of opportunities and challenging of applying data fusion into travel time estimation in a heterogeneous real time data environment. For a particular England highway scenario where ILDs and ANPR data are largely available, a simple but practical fusion method is proposed to estimate the travel time based on a novel relationship between space-mean-speed and time-mean-speed. In developing a general fusion framework which is able to fuse ILDs, GPS and ANPR data, the Kalman filter is identified as the most appropriate fundamental fusion technique upon which to construct the required framework. This is based both on the ability of the Kalman filter to flexibly accommodate well-established traffic flow models which describe the internal physical relation between the observed variables and objective estimates and on its ability to integrate and propagate in a consistent fashion the uncertainty associated with different data sources. Although the standard linear Kalman filter has been used for multi-sensor travel time estimation in the previous research, the novelty of this research is to develop a nonlinear Kalman filter (EKF and UKF) fusion framework which improves the estimation performance over those methods based on the linear Kalman filter. This proposed framework is validated by both of simulation and real-world scenarios, and is demonstrated the effectiveness of estimating travel time by fusing multi-sensor sources

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

Network anomalies detection via event analysis and correlation by a smart system

The multidisciplinary of contemporary societies compel us to look at Information Technology (IT) systems as one of the most significant grants that we can remember. However, its increase implies a mandatory security force for users, a force in the form of effective and robust tools to combat cybercrime to which users, individual or collective, are ex-posed almost daily. Monitoring and detection of this kind of problem must be ensured in real-time, allowing companies to intervene fruitfully, quickly and in unison. The proposed framework is based on an organic symbiosis between credible, affordable, and effective open-source tools for data analysis, relying on Security Information and Event Management (SIEM), Big Data and Machine Learning (ML) techniques commonly applied for the development of real-time monitoring systems. Dissecting this framework, it is composed of a system based on SIEM methodology that provides monitoring of data in real-time and simultaneously saves the information, to assist forensic investigation teams. Secondly, the application of the Big Data concept is effective in manipulating and organising the flow of data. Lastly, the use of ML techniques that help create mechanisms to detect possible attacks or anomalies on the network. This framework is intended to provide a real-time analysis application in the institution ISCTE – Instituto Universitário de Lisboa (Iscte), offering a more complete, efficient, and secure monitoring of the data from the different devices comprising the network.A multidisciplinaridade das sociedades contemporâneas obriga-nos a perspetivar os sistemas informáticos como uma das maiores dádivas de que há memória. Todavia o seu incremento implica uma mandatária força de segurança para utilizadores, força essa em forma de ferramentas eficazes e robustas no combate ao cibercrime a que os utilizadores, individuais ou coletivos, são sujeitos quase diariamente. A monitorização e deteção deste tipo de problemas tem de ser assegurada em tempo real, permitindo assim, às empresas intervenções frutuosas, rápidas e em uníssono. A framework proposta é alicerçada numa simbiose orgânica entre ferramentas open source credíveis, acessíveis pecuniariamente e eficazes na monitorização de dados, recorrendo a um sistema baseado em técnicas de Security Information and Event Management (SIEM), Big Data e Machine Learning (ML) comumente aplicadas para a criação de sistemas de monitorização em tempo real. Dissecando esta framework, é composta pela metodologia SIEM que possibilita a monitorização de dados em tempo real e em simultâneo guardar a informação, com o objetivo de auxiliar as equipas de investigação forense. Em segundo lugar, a aplicação do conceito Big Data eficaz na manipulação e organização do fluxo dos dados. Por último, o uso de técnicas de ML que ajudam a criação de mecanismos de deteção de possíveis ataques ou anomalias na rede. Esta framework tem como objetivo uma aplicação de análise em tempo real na instituição ISCTE – Instituto Universitário de Lisboa (Iscte), apresentando uma monitorização mais completa, eficiente e segura dos dados dos diversos dispositivos presentes na mesma

Bayesian Models Applied to Cyber Security Anomaly Detection Problems

Cyber security is an important concern for all individuals, organisations and governments globally. Cyber attacks have become more sophisticated, frequent and dangerous than ever, and traditional anomaly detection methods have been proved to be less effective when dealing with these new classes of cyber threats. In order to address this, both classical and Bayesian models offer a valid and innovative alternative to the traditional signature-based methods, motivating the increasing interest in statistical research that it has been observed in recent years. In this review, we provide a description of some typical cyber security challenges, typical types of data and statistical methods, paying special attention to Bayesian approaches for these problems